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Introduction -The occurrence of tables a. Picture of a table: +Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. +1. Introduction +The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.
+1
-- b. Red-annotation of bounding boxes, +- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer - c. Structure predicted by TableFormer:
-Figure 1: +Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.
+01 2 11 2 11 2 11 2 1 +34 3567 +8 29101112 +13141516 +17181920
-Recently, -The first problem is called table-location and has been -considered as a solved problem, given enough ground-truth -The second problem is called table-structure decompo- -In this paper, we want to address these weaknesses and -To meet the design criteria listed above, we developed a -- • -- • -- • -- • -The paper is structured as follows. -its results & performance in Sec. 5. As a conclusion, we de- +Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. +The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be +considered as a solved problem, given enough ground-truth data to train on. +The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. +In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image. +To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows: +- · We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. +- · Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works. +- · We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity. +- · An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility. +The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe +its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. 2. Previous work and State of the Art -Identifying the structure of a table has been an outstand- -Before the rising -Image-to-Text networks +Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc. +Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification. +Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder → text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder → dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the tag-decoder which is constrained to the table-tags. -In -Graph -Hybrid Deep Learning-Rule-Based approach +In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper. +Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]. +Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. 3. Datasets -We rely on large-scale datasets such as PubTabNet [37], +We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-
-Figure 2: +Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets
balance in the previous datasets. -The PubTabNet dataset contains 509k tables delivered as -Due to the heterogeneity across the dataset formats, it -amount of such tables, and kept only those ones ranging -The availability of the bounding boxes for all table cells -As it is illustrated in Fig. 2, the table distributions from -Motivated by those observations we aimed at generating -In this regard, we have prepared four synthetic datasets, +The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits. +Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small +amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns). +The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. +As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. +Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. +In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third - + +TagsBboxSizeFormat +PubTabNet33509kPNG +FinTabNet33112kPDF +TableBank37145kJPEG +Combined-Tabnet(*)33400kPNG +Combined(**)33500kPNG +SynthTabNet33600kPNG
TableTable 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.
-Table -one adopts a colorful appearance with high contrast and the +Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. +one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples. Tab. 1 summarizes the various attributes of the datasets. 4. The TableFormer model -Given the image of a table, TableFormer is able to pre- +Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required. 4.1. Model architecture. -We now describe in detail the proposed method, which -CNN Backbone Network. +We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. +CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-
-Figure 3: +Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.
-Figure 4: +Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.
-forming classification, and adding an adaptive pooling layer -Structure Decoder. -The -Cell BBox Decoder. -The encoding generated by the -tention encoding is then multiplied to the encoded image to -The output -Loss Functions. -The loss used to train the TableFormer can be defined as -where +forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder . +Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images. +The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score. +Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query. +The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at- +tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence. +The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer. +Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. +The loss used to train the TableFormer can be defined as following: +where λ ∈ [0, 1], and λ iou , λ l 1 ∈ R are hyper-parameters. 5. Experimental Results 5.1. Implementation Details -TableFormer uses ResNet-18 as the -Although input constraints are used also by other methods, runtime performance and lower memory footprint of Table- -The Transformer Encoder consists of two 'Transformer -For training, TableFormer is trained with 3 Adam opti- -TableFormer is implemented with PyTorch and Torchvi- +TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: +Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions. +The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5. +For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. +TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. 5.2. Generalization -TableFormer is evaluated on three major publicly avail- -We also share our baseline results on the challenging +TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively. +We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized. 5.3. Datasets and Metrics -The Tree-Edit-Distance-Based Similarity (TEDS) met- -where +The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as: +where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T . 5.4. Quantitative Analysis -Structure. +Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. +ModelDatasetSimpleTEDS ComplexAll +EDDPTN91.188.789.9 +GTEPTN--93.01 +TableFormerPTN98.595.096.75 +EDDFTN88.492.0890.6 +GTEFTN--87.14 +GTE (FT)FTN--91.02 +TableFormerFTN97.596.096.8 +EDDTB86.0-86.0 +TableFormerTB89.6-89.6 +TableFormerSTN96.995.796.7
-Table 2: +Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). FT: Model was trained on PubTabNet then finetuned. -Cell Detection. -our +Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. - + +ModelDatasetmAPmAP (PP) +EDD+BBoxPubTabNet79.282.7 +TableFormerPubTabNet82.186.8 +TableFormerSynthTabNet87.7-
Table 3:Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.
-Table 3: -Cell Content. +Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. +Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. - + +ModelSimpleTEDS ComplexAll +Tabula78.057.867.9 +Traprange60.849.955.4 +Camelot80.066.073.0 +Acrobat Pro68.961.865.3 +EDD91.285.488.3 +TableFormer95.490.193.6
Table 4:Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.
-Table 4: +Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. - a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells Japanese language (previously unseen by TableFormer): Example table from FinTabNet: @@ -130,117 +164,134 @@
+b. Structure predicted by TableFormer, with superimposed matched PDF cell text:
-b. Structure predicted by TableFormer, with superimposed matched PDF cell text: -
- - + +論文ファイル論文ファイル参考文献参考文献 +出典ファイル数 英語日本語英語日本語 +Association for Computational Linguistics(ACL2003)656501500 +Computational Linguistics(COLING2002)14014001500 +電気情報通信学会 2003 年総合大会1508142223147 +情報処理学会第 65 回全国大会 (2003)1771176150236 +第 17 回人工知能学会全国大会 (2003)2085203152244 +自然言語処理研究会第 146 〜 155 回98296150232 +WWW から収集した論文107733414796 +計9452946511122955
Text is aligned to match original for ease of viewing
Text is aligned to match original for ease of viewing -
- -Figure 5: -
+ + +Shares (in millions)Shares (in millions)Weighted Average Grant Date Fair ValueWeighted Average Grant Date Fair Value +RSUsPSUsRSUsPSUs +Nonvested on January 11.10.390.10 $$ 91.19 +Granted0.50.1117.44122.41 +Vested(0.5)(0.1)87.0881.14 +Canceled or forfeited(0.1)-102.0192.18 +Nonvested on December 311.00.3104.85 $$ 104.51 +
-Figure 6: +Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.
-5.5. Qualitative Analysis -We showcase
+Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.
+
+ +
+5.5. Qualitative Analysis 6. Future Work & Conclusion -In this paper, we presented TableFormer an end-to-end +We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type. +In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets. 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Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. Winter Conference for Applications in Computer Vision (WACV) , 2021. 2, 3 +- [37] Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: Data, model, +- and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7 +- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1 +TableFormer: Table Structure Understanding with Transformers Supplementary Material 1. Details on the datasets -ances in regard to their size, 1.1. Data preparation -As a first step of our data preparation process, we have -We have developed a technique that tries -Figure 7 illustrates the distribution of the tables across +As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length. +We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. +Figure 7 illustrates the distribution of the tables across different dimensions per dataset. 1.2. Synthetic datasets -Aiming to train and evaluate our models in a broader -2. -The process of generating a synthetic dataset can be de- -- 1. -- 2. -- 3. Generate content: Based on the dataset -- 4. -- 5. -Although TableFormer can predict the table structure and +Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). +The process of generating a synthetic dataset can be decomposed into the following steps: +- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). +- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. +- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. +- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table. +- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process. +2. Prediction post-processing for PDF documents +Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:
-Figure 7: +Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.
-- • -- • -However, it is possible to mitigate those limitations by -Here is a step-by-step description of the prediction post- -- 1. Get the minimal grid dimensions - number of rows and -- 2. -- 3. -- 3.a. -- 4. -where -- 5. -dian cell size for all table cells. -- 6. -- 7. -- 8. -- 9. -9a. -- 9b. -- 9c. -- 9d. Intersect the orphan's bounding box with the column -- 9e. If the table cell under the identified row and column +- · TableFormer output does not include the table cell content. +- · There are occasional inaccuracies in the predictions of the bounding boxes. +dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. +However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes. +Here is a step-by-step description of the prediction postprocessing: +- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure. +- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches. +- 3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones. +- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column. +- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: +where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point. +- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me- +- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes. +- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells. +- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score. +- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan. +9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row). +- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row. +- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column). +- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column. +- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or- phan cell. -9f. -Aditional images with examples of TableFormer predic- +9f. Otherwise create a new structural cell and match it wit the orphan cell. +Aditional images with examples of TableFormer predictions and post-processing can be found below.
@@ -253,12 +304,12 @@
-Figure 8: +Figure 8: Example of a table with multi-line header. - +
Figure 9:Figure 9: Example of a table with big empty distance between cells.
-Figure 9: +Figure 9: Example of a table with big empty distance between cells.
@@ -273,9 +324,9 @@ - +
Figure 10:Figure 10: Example of a complex table with empty cells.
-Figure 10: +Figure 10: Example of a complex table with empty cells.
@@ -294,7 +345,7 @@
-Figure 11: +Figure 11: Simple table with different style and empty cells.
@@ -307,7 +358,7 @@
-Figure 12: +Figure 12: Simple table predictions and post processing.
@@ -319,13 +370,13 @@
-Figure 13: +Figure 13: Table predictions example on colorful table.
- +
Figure 14:Figure 14: Example with multi-line text.
-Figure 14: +Figure 14: Example with multi-line text.
@@ -337,7 +388,6 @@
-Figure 15:
@@ -355,13 +405,10 @@
- -
- - +
Figure 16:Figure 15: Example with triangular table.
-Figure 16: +Figure 15: Example with triangular table.
@@ -377,8 +424,12 @@
+ + +
+Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.
-Figure 17: +Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.
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Datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [62.067, 126.75400000000002, 286.365, 135.30600000000004], "page": 3, "span": [0, 55], "__ref_s3_data": null}], "text": "We rely on large-scale datasets such as PubTabNet [37],", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [308.862, 465.706, 437.27, 474.258], "page": 3, "span": [0, 33], "__ref_s3_data": null}], "text": "balance in the previous datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 451.648, 545.115, 460.2], "page": 3, "span": [0, 55], "__ref_s3_data": null}], "text": "The PubTabNet dataset contains 509k tables delivered as", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 150.66499999999996, 545.115, 159.21699999999998], "page": 3, "span": [0, 55], "__ref_s3_data": null}], "text": "Due to the heterogeneity across the dataset formats, it", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 707.971, 286.365, 716.523], "page": 4, "span": [0, 55], "__ref_s3_data": null}], "text": "amount of such tables, and kept only those ones ranging", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [62.067, 682.219, 286.365, 690.771], "page": 4, "span": [0, 58], "__ref_s3_data": null}], "text": "The availability of the bounding boxes for all table cells", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [62.067, 465.184, 286.365, 473.736], "page": 4, "span": [0, 60], "__ref_s3_data": null}], "text": "As it is illustrated in Fig. 2, the table distributions from", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [62.067, 343.79, 286.365, 352.342], "page": 4, "span": [0, 54], "__ref_s3_data": null}], "text": "Motivated by those observations we aimed at generating", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [62.067, 150.66499999999996, 286.365, 159.21699999999998], "page": 4, "span": [0, 57], "__ref_s3_data": null}], "text": "In this regard, we have prepared four synthetic datasets,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [308.862, 615.518, 330.75, 624.07], "page": 4, "span": [0, 5], "__ref_s3_data": null}], "text": "Table", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [308.862, 533.559, 545.115, 542.111], "page": 4, "span": [0, 59], "__ref_s3_data": null}], "text": "one adopts a colorful appearance with high contrast and the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 485.407, 542.744, 493.959], "page": 4, "span": [0, 57], "__ref_s3_data": null}], "text": "Tab. 1 summarizes the various attributes of the datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 460.068, 444.936, 470.816], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4. The TableFormer model", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [320.817, 441.24, 545.115, 449.792], "page": 4, "span": [0, 55], "__ref_s3_data": null}], "text": "Given the image of a table, TableFormer is able to pre-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 324.454, 420.161, 334.306], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4.1. Model architecture.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [320.817, 306.414, 545.115, 314.966], "page": 4, "span": [0, 52], "__ref_s3_data": null}], "text": "We now describe in detail the proposed method, which", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 114.78300000000002, 431.91, 123.73900000000003], "page": 4, "span": [0, 21], "__ref_s3_data": null}], "text": "CNN Backbone Network.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [308.862, 533.645, 545.115, 542.197], "page": 5, "span": [0, 60], "__ref_s3_data": null}], "text": "forming classification, and adding an adaptive pooling layer", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 485.704, 400.866, 494.66], "page": 5, "span": [0, 18], "__ref_s3_data": null}], "text": "Structure Decoder.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 366.065, 336.309, 374.617], "page": 5, "span": [0, 3], "__ref_s3_data": null}], "text": "The", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 234.43899999999996, 404.762, 243.39499999999998], "page": 5, "span": [0, 18], "__ref_s3_data": null}], "text": "Cell BBox Decoder.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 126.75400000000002, 444.343, 135.30600000000004], "page": 5, "span": [0, 29], "__ref_s3_data": null}], "text": "The encoding generated by the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 707.971, 286.365, 716.523], "page": 6, "span": [0, 59], "__ref_s3_data": null}], "text": "tention encoding is then multiplied to the encoded image to", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [62.067, 623.555, 108.154, 632.107], "page": 6, "span": [0, 10], "__ref_s3_data": null}], "text": "The output", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [62.067, 539.122, 129.215, 548.078], "page": 6, "span": [0, 15], "__ref_s3_data": null}], "text": "Loss Functions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [62.067, 335.169, 286.365, 343.721], "page": 6, "span": [0, 56], "__ref_s3_data": null}], "text": "The loss used to train the TableFormer can be defined as", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [125.715, 289.903, 128.688, 298.609], "page": 6, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112, 251.87, 74.451, 260.422], "page": 6, "span": [0, 5], "__ref_s3_data": null}], "text": "where", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 225.33500000000004, 171.983, 236.08299999999997], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "5. 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Implementation Details", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [62.067, 187.44499999999994, 202.978, 195.99700000000007], "page": 6, "span": [0, 33], "__ref_s3_data": null}], "text": "TableFormer uses ResNet-18 as the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [91.661, 128.63400000000001, 186.017, 137.18600000000004], "page": 6, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112, 90.88900000000001, 286.365, 99.44100000000003], "page": 6, "span": [0, 115], "__ref_s3_data": null}, {"bbox": [50.112, 90.88900000000001, 286.365, 99.44100000000003], "page": 6, "span": [0, 115], "__ref_s3_data": null}], "text": "Although input constraints are used also by other methods, runtime performance and lower memory footprint of Table-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 666.95, 545.115, 675.502], "page": 6, "span": [0, 52], "__ref_s3_data": null}], "text": "The Transformer Encoder consists of two 'Transformer", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 446.602, 545.115, 455.154], "page": 6, "span": [0, 54], "__ref_s3_data": null}], "text": "For training, TableFormer is trained with 3 Adam opti-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 345.805, 545.115, 354.357], "page": 6, "span": [0, 52], "__ref_s3_data": null}], "text": "TableFormer is implemented with PyTorch and Torchvi-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 202.59400000000005, 397.443, 212.44600000000003], "page": 6, "span": [0, 19], "__ref_s3_data": null}], "text": "5.2. Generalization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [320.817, 179.73000000000002, 545.115, 188.28200000000004], "page": 6, "span": [0, 55], "__ref_s3_data": null}], "text": "TableFormer is evaluated on three major publicly avail-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 102.84400000000005, 545.115, 111.39599999999996], "page": 6, "span": [0, 53], "__ref_s3_data": null}], "text": "We also share our baseline results on the challenging", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 707.747, 167.898, 717.599], "page": 7, "span": [0, 25], "__ref_s3_data": null}], "text": "5.3. Datasets and Metrics", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [62.067, 689.829, 286.365, 698.381], "page": 7, "span": [0, 51], "__ref_s3_data": null}], "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [86.219, 626.126, 118.878, 634.832], "page": 7, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [62.067, 602.017, 86.406, 610.569], "page": 7, "span": [0, 5], "__ref_s3_data": null}], "text": "where", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 557.329, 170.452, 567.181], "page": 7, "span": [0, 26], "__ref_s3_data": null}], "text": "5.4. 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Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.812, 689.929, 284.346, 697.411], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Japanese language (previously unseen by TableFormer):", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [304.831, 689.929, 431.091, 697.411], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Example table from FinTabNet:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"prov": [{"bbox": [53.812, 575.977, 385.934, 583.459], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/6"}, {"name": "Table", "type": "table", "$ref": "#/tables/7"}, {"prov": [{"bbox": [380.427, 493.464, 549.422, 499.449], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Text is aligned to match original for ease of viewing", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [50.112, 290.753, 163.756, 300.605], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "5.5. 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"fc40b9fb3698f24af7beda03b7afac10c3fcc6c73e83b4c6159785ea2991e2c4", "model": "default", "page": 15}, {"hash": "96080fce6eb8572fe319782f353a67661947f48e67607b1ffd8c01d617d075a7", "model": "default", "page": 16}]}, "main-text": [{"prov": [{"bbox": [96.301, 672.069, 498.927, 684.966], "page": 1, "span": [0, 61], "__ref_s3_data": null}], "text": "TableFormer: Table Structure Understanding with Transformers.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [142.477, 620.783, 452.75, 644.992], "page": 1, "span": [0, 73], "__ref_s3_data": null}], "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [208.123, 607.73, 378.733, 615.438], "page": 1, "span": [0, 35], "__ref_s3_data": null}], "text": "{ ahn,nli,mly,taa @zurich.ibm.com }", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [145.995, 565.769, 190.48, 576.517], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "Abstract", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [315.567, 565.338, 408.441, 573.651], "page": 1, "span": [0, 22], "__ref_s3_data": null}], "text": "a. Picture of a table:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 279.00299999999993, 286.365, 550.605], "page": 1, "span": [0, 1320], "__ref_s3_data": null}], "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 241.30899999999997, 126.948, 252.05700000000002], "page": 1, "span": [0, 15], "__ref_s3_data": null}], "text": "1. Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 78.93399999999997, 286.365, 230.94799999999998], "page": 1, "span": [0, 712], "__ref_s3_data": null}], "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [315.567, 458.85, 486.402, 477.963], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [315.567, 363.162, 491.191, 371.475], "page": 1, "span": [0, 38], "__ref_s3_data": null}], "text": "- c. Structure predicted by TableFormer:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [308.862, 127.03899999999999, 545.115, 207.322], "page": 1, "span": [0, 363], "__ref_s3_data": null}], "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 78.93399999999997, 545.115, 123.351], "page": 1, "span": [0, 229], "__ref_s3_data": null}], "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 696.016, 286.365, 716.523], "page": 2, "span": [0, 75], "__ref_s3_data": null}], "text": "considered as a solved problem, given enough ground-truth data to train on.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 564.056, 286.365, 692.16], "page": 2, "span": [0, 626], "__ref_s3_data": null}], "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 420.141, 286.365, 560.2], "page": 2, "span": [0, 643], "__ref_s3_data": null}], "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 359.913, 286.367, 416.285], "page": 2, "span": [0, 238], "__ref_s3_data": null}], "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [61.569, 302.763, 286.365, 347.568], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "- \u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569, 245.15999999999997, 286.365, 289.966], "page": 2, "span": [0, 181], "__ref_s3_data": null}], "text": "- \u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569, 199.51300000000003, 286.365, 232.36300000000006], "page": 2, "span": [0, 106], "__ref_s3_data": null}], "text": "- \u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569, 153.865, 286.365, 186.32799999999997], "page": 2, "span": [0, 131], "__ref_s3_data": null}], "text": "- \u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112, 96.71600000000001, 286.365, 141.13300000000004], "page": 2, "span": [0, 231], "__ref_s3_data": null}], "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [60.971, 79.34699999999998, 183.731, 87.70399999999995], "page": 2, "span": [0, 36], "__ref_s3_data": null}], "text": "1 https://github.com/IBM/SynthTabNet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [308.862, 684.061, 545.115, 716.523], "page": 2, "span": [0, 166], "__ref_s3_data": null}], "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 659.52, 498.28, 670.268], "page": 2, "span": [0, 37], "__ref_s3_data": null}], "text": "2. Previous work and State of the Art", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.862, 461.631, 545.115, 649.51], "page": 2, "span": [0, 901], "__ref_s3_data": null}], "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 342.013, 545.115, 458.162], "page": 2, "span": [0, 552], "__ref_s3_data": null}], "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 78.93399999999997, 545.117, 338.932], "page": 2, "span": [0, 1262], "__ref_s3_data": null}], "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder \u2192 text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder \u2192 dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 707.971, 250.151, 716.523], "page": 3, "span": [0, 51], "__ref_s3_data": null}], "text": "tag-decoder which is constrained to the table-tags.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 516.632, 286.365, 704.512], "page": 3, "span": [0, 864], "__ref_s3_data": null}], "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 301.383, 286.365, 513.561], "page": 3, "span": [0, 1007], "__ref_s3_data": null}], "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 169.81899999999996, 286.366, 298.311], "page": 3, "span": [0, 618], "__ref_s3_data": null}], "text": "Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 145.30700000000002, 105.225, 156.05500000000006], "page": 3, "span": [0, 11], "__ref_s3_data": null}], "text": "3. Datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 78.93399999999997, 286.365, 135.30600000000004], "page": 3, "span": [0, 281], "__ref_s3_data": null}], "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [308.862, 465.706, 437.27, 474.258], "page": 3, "span": [0, 33], "__ref_s3_data": null}], "text": "balance in the previous datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 164.72399999999993, 545.115, 460.2], "page": 3, "span": [0, 1400], "__ref_s3_data": null}], "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 78.93399999999997, 545.115, 159.21699999999998], "page": 3, "span": [0, 406], "__ref_s3_data": null}], "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 696.016, 286.365, 716.523], "page": 4, "span": [0, 93], "__ref_s3_data": null}], "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 478.981, 286.365, 690.771], "page": 4, "span": [0, 983], "__ref_s3_data": null}], "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 357.587, 286.365, 473.736], "page": 4, "span": [0, 571], "__ref_s3_data": null}], "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 164.462, 286.367, 352.342], "page": 4, "span": [0, 941], "__ref_s3_data": null}], "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 78.93399999999997, 286.365, 159.21699999999998], "page": 4, "span": [0, 405], "__ref_s3_data": null}], "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [308.862, 567.697, 545.115, 624.249], "page": 4, "span": [0, 267], "__ref_s3_data": null}], "text": "Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [308.862, 497.694, 545.115, 542.111], "page": 4, "span": [0, 210], "__ref_s3_data": null}], "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.817, 485.407, 542.744, 493.959], "page": 4, "span": [0, 57], "__ref_s3_data": null}], "text": "Tab. 1 summarizes the various attributes of the datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 460.068, 444.936, 470.816], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4. The TableFormer model", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.862, 345.599, 545.115, 449.792], "page": 4, "span": [0, 504], "__ref_s3_data": null}], "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 324.454, 420.161, 334.306], "page": 4, "span": [0, 24], "__ref_s3_data": null}], "text": "4.1. Model architecture.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.862, 127.08600000000001, 545.116, 314.966], "page": 4, "span": [0, 907], "__ref_s3_data": null}], "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 78.93399999999997, 545.115, 123.73900000000003], "page": 4, "span": [0, 223], "__ref_s3_data": null}], "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [308.862, 497.779, 545.115, 542.197], "page": 5, "span": [0, 227], "__ref_s3_data": null}], "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 378.124, 545.115, 494.66], "page": 5, "span": [0, 563], "__ref_s3_data": null}], "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 246.51300000000003, 545.115, 374.617], "page": 5, "span": [0, 592], "__ref_s3_data": null}], "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 138.813, 545.115, 243.39499999999998], "page": 5, "span": [0, 483], "__ref_s3_data": null}], "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 78.93399999999997, 545.115, 135.485], "page": 5, "span": [0, 286], "__ref_s3_data": null}], "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 636.24, 286.365, 716.523], "page": 6, "span": [0, 380], "__ref_s3_data": null}], "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 551.823, 286.365, 632.107], "page": 6, "span": [0, 371], "__ref_s3_data": null}], "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 347.855, 286.366, 548.078], "page": 6, "span": [0, 965], "__ref_s3_data": null}], "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 323.214, 286.365, 343.721], "page": 6, "span": [0, 67], "__ref_s3_data": null}], "text": "The loss used to train the TableFormer can be defined as following:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [124.33, 274.03600000000006, 286.362, 298.609], "page": 6, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112, 251.111, 281.597, 260.74], "page": 6, "span": [0, 61], "__ref_s3_data": null}], "text": "where \u03bb \u2208 [0, 1], and \u03bb iou , \u03bb l 1 \u2208 R are hyper-parameters.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 225.33500000000004, 171.983, 236.08299999999997], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "5. Experimental Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 205.88400000000001, 179.175, 215.736], "page": 6, "span": [0, 27], "__ref_s3_data": null}], "text": "5.1. Implementation Details", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 151.57899999999995, 286.365, 196.17599999999993], "page": 6, "span": [0, 207], "__ref_s3_data": null}], "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [91.661, 113.69000000000005, 286.362, 137.50400000000002], "page": 6, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112, 78.93399999999997, 286.365, 99.44100000000003], "page": 6, "span": [0, 274], "__ref_s3_data": null}, {"bbox": [50.112, 78.93399999999997, 286.365, 99.44100000000003], "page": 6, "span": [0, 274], "__ref_s3_data": null}], "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 463.712, 545.115, 675.502], "page": 6, "span": [0, 1024], "__ref_s3_data": null}], "text": "The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 362.916, 545.115, 455.154], "page": 6, "span": [0, 419], "__ref_s3_data": null}], "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 238.20900000000006, 545.115, 354.357], "page": 6, "span": [0, 528], "__ref_s3_data": null}], "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 202.59400000000005, 397.443, 212.44600000000003], "page": 6, "span": [0, 19], "__ref_s3_data": null}], "text": "5.2. Generalization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.862, 119.95399999999995, 545.115, 188.28200000000004], "page": 6, "span": [0, 299], "__ref_s3_data": null}], "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 78.93299999999999, 545.115, 111.39599999999996], "page": 6, "span": [0, 155], "__ref_s3_data": null}], "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 707.747, 167.898, 717.599], "page": 7, "span": [0, 25], "__ref_s3_data": null}], "text": "5.3. Datasets and Metrics", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 653.963, 286.365, 698.381], "page": 7, "span": [0, 192], "__ref_s3_data": null}], "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [86.219, 618.368, 286.362, 641.572], "page": 7, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112, 578.107, 286.363, 610.887], "page": 7, "span": [0, 154], "__ref_s3_data": null}], "text": "where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 557.329, 170.452, 567.181], "page": 7, "span": [0, 26], "__ref_s3_data": null}], "text": "5.4. Quantitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 395.948, 286.365, 548.35], "page": 7, "span": [0, 723], "__ref_s3_data": null}], "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [50.112, 178.79099999999994, 286.365, 199.298], "page": 7, "span": [0, 101], "__ref_s3_data": null}], "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 166.836, 261.787, 175.38800000000003], "page": 7, "span": [0, 50], "__ref_s3_data": null}], "text": "FT: Model was trained on PubTabNet then finetuned.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 78.93399999999997, 286.366, 147.64999999999998], "page": 7, "span": [0, 1084], "__ref_s3_data": null}, {"bbox": [50.112, 78.93399999999997, 286.366, 147.64999999999998], "page": 7, "span": [0, 1084], "__ref_s3_data": null}], "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [308.862, 454.775, 545.115, 475.282], "page": 7, "span": [0, 94], "__ref_s3_data": null}], "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [308.862, 271.918, 545.116, 424.32], "page": 7, "span": [0, 715], "__ref_s3_data": null}], "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/5"}, {"prov": [{"bbox": [308.862, 102.40800000000002, 545.115, 134.87], "page": 7, "span": [0, 148], "__ref_s3_data": null}], "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [53.286, 705.523, 499.556, 713.005], "page": 8, "span": [0, 108], "__ref_s3_data": null}], "text": "- a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.812, 689.929, 284.346, 697.411], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Japanese language (previously unseen by TableFormer):", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [304.831, 689.929, 431.091, 697.411], "page": 8, "span": [0, 29], "__ref_s3_data": null}], "text": "Example table from FinTabNet:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/6"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"name": "Table", "type": "table", "$ref": "#/tables/6"}, {"prov": [{"bbox": [380.427, 493.464, 549.422, 499.449], "page": 8, "span": [0, 53], "__ref_s3_data": null}], "text": "Text is aligned to match original for ease of viewing", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/7"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [50.112, 290.753, 163.756, 300.605], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "5.5. Qualitative Analysis", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.862, 290.543, 460.848, 301.291], "page": 8, "span": [0, 27], "__ref_s3_data": null}], "text": "6. Future Work & Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 78.93399999999997, 286.365, 254.85799999999995], "page": 8, "span": [0, 866], "__ref_s3_data": null}], "text": "We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 138.77999999999997, 545.115, 278.83799999999997], "page": 8, "span": [0, 640], "__ref_s3_data": null}], "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 109.15300000000002, 364.406, 119.90099999999995], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [313.345, 79.13999999999999, 545.113, 97.79600000000005], "page": 8, "span": [0, 121], "__ref_s3_data": null}], "text": "- [1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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Springer International Publishing. 2, 3, 7", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 630.469, 545.113, 671.043], "page": 10, "span": [0, 221], "__ref_s3_data": null}], "text": "- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [132.842, 656.516, 465.376, 681.425], "page": 11, "span": [0, 83], "__ref_s3_data": null}], "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 620.091, 175.964, 630.839], "page": 11, "span": [0, 26], "__ref_s3_data": null}], "text": "1. Details on the datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 601.169, 150.364, 611.021], "page": 11, "span": [0, 21], "__ref_s3_data": null}], "text": "1.1. Data preparation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 403.931, 286.365, 591.811], "page": 11, "span": [0, 931], "__ref_s3_data": null}], "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 164.62599999999998, 286.365, 400.326], "page": 11, "span": [0, 1149], "__ref_s3_data": null}], "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 140.51300000000003, 286.365, 161.02099999999996], "page": 11, "span": [0, 92], "__ref_s3_data": null}], "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 119.75800000000004, 153.608, 129.61], "page": 11, "span": [0, 23], "__ref_s3_data": null}], "text": "1.2. Synthetic datasets", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [50.112, 77.93799999999999, 286.365, 110.39999999999998], "page": 11, "span": [0, 389], "__ref_s3_data": null}, {"bbox": [50.112, 77.93799999999999, 286.365, 110.39999999999998], "page": 11, "span": [0, 389], "__ref_s3_data": null}], "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 559.989, 545.115, 580.496], "page": 11, "span": [0, 89], "__ref_s3_data": null}], "text": "The process of generating a synthetic dataset can be decomposed into the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 475.543, 545.115, 555.826], "page": 11, "span": [0, 373], "__ref_s3_data": null}], "text": "- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 343.277, 545.115, 471.381], "page": 11, "span": [0, 573], "__ref_s3_data": null}], "text": "- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 294.697, 545.115, 339.294], "page": 11, "span": [0, 195], "__ref_s3_data": null}], "text": "- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 246.11699999999996, 545.115, 290.535], "page": 11, "span": [0, 218], "__ref_s3_data": null}], "text": "- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 185.582, 545.115, 241.95500000000004], "page": 11, "span": [0, 238], "__ref_s3_data": null}], "text": "- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 145.01300000000003, 545.109, 169.70900000000006], "page": 11, "span": [0, 47], "__ref_s3_data": null}], "text": "2. Prediction post-processing for PDF documents", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [308.862, 77.93700000000001, 545.115, 134.30999999999995], "page": 11, "span": [0, 247], "__ref_s3_data": null}], "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [61.569, 560.293, 286.365, 580.8], "page": 12, "span": [0, 61], "__ref_s3_data": null}], "text": "- \u00b7 TableFormer output does not include the table cell content.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [61.569, 527.153, 286.365, 547.66], "page": 12, "span": [0, 77], "__ref_s3_data": null}], "text": "- \u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 536.382, 545.115, 580.8], "page": 12, "span": [0, 183], "__ref_s3_data": null}], "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 396.379, 286.365, 512.528], "page": 12, "span": [0, 545], "__ref_s3_data": null}], "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 372.154, 286.365, 392.662], "page": 12, "span": [0, 68], "__ref_s3_data": null}], "text": "Here is a step-by-step description of the prediction postprocessing:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 335.974, 286.365, 368.436], "page": 12, "span": [0, 173], "__ref_s3_data": null}], "text": "- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112, 287.839, 286.365, 332.256], "page": 12, "span": [0, 187], "__ref_s3_data": null}], "text": "- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112, 263.61300000000006, 286.365, 284.121], "page": 12, "span": [0, 97], "__ref_s3_data": null}], "text": "- 3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112, 227.433, 286.365, 259.89599999999996], "page": 12, "span": [0, 131], "__ref_s3_data": null}], "text": "- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112, 191.25299999999993, 286.365, 223.71500000000003], "page": 12, "span": [0, 169], "__ref_s3_data": null}], "text": "- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [110.705, 137.00199999999995, 286.362, 167.89599999999996], "page": 12, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "equation", "payload": null, "name": "Formula", "font": null}, {"prov": [{"bbox": [50.112, 103.15899999999999, 286.362, 123.98400000000004], "page": 12, "span": [0, 99], "__ref_s3_data": null}], "text": "where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 78.93399999999997, 286.365, 99.44100000000003], "page": 12, "span": [0, 110], "__ref_s3_data": null}], "text": "- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 512.122, 545.115, 532.629], "page": 12, "span": [0, 91], "__ref_s3_data": null}], "text": "- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 404.175, 545.115, 508.368], "page": 12, "span": [0, 471], "__ref_s3_data": null}], "text": "- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 332.094, 545.115, 400.421], "page": 12, "span": [0, 311], "__ref_s3_data": null}], "text": "- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 224.14699999999993, 545.115, 328.34], "page": 12, "span": [0, 503], "__ref_s3_data": null}], "text": "- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 187.93100000000004, 545.117, 220.39300000000003], "page": 12, "span": [0, 113], "__ref_s3_data": null}], "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [308.862, 163.66999999999996, 545.115, 184.178], "page": 12, "span": [0, 101], "__ref_s3_data": null}], "text": "- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 127.45500000000004, 545.115, 159.91700000000003], "page": 12, "span": [0, 117], "__ref_s3_data": null}], "text": "- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 103.19399999999996, 545.115, 123.70100000000002], "page": 12, "span": [0, 107], "__ref_s3_data": null}], "text": "- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [308.862, 78.93399999999997, 545.115, 99.44100000000003], "page": 12, "span": [0, 118], "__ref_s3_data": null}], "text": "- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [50.112, 707.971, 88.847, 716.523], "page": 13, "span": [0, 10], "__ref_s3_data": null}], "text": "phan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 684.061, 286.365, 704.568], "page": 13, "span": [0, 76], "__ref_s3_data": null}], "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [50.112, 660.294, 286.365, 680.837], "page": 13, "span": [0, 97], "__ref_s3_data": null}], "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/8"}, {"name": "Table", "type": "table", "$ref": "#/tables/9"}, {"name": "Table", "type": "table", "$ref": "#/tables/10"}, {"name": "Table", "type": "table", "$ref": "#/tables/11"}, {"prov": [{"bbox": [63.341, 281.123, 273.133, 289.675], "page": 13, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 8: Example of a table with multi-line header.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/12"}, {"prov": [{"bbox": [308.862, 464.626, 545.115, 485.133], "page": 13, "span": [0, 67], "__ref_s3_data": null}], "text": "Figure 9: Example of a table with big empty distance between cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/13"}, {"name": "Table", "type": "table", "$ref": "#/tables/14"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"name": "Table", "type": "table", "$ref": "#/tables/15"}, {"name": "Table", "type": "table", "$ref": "#/tables/16"}, {"prov": [{"bbox": [312.343, 102.68600000000004, 541.632, 111.23800000000006], "page": 13, "span": [0, 55], "__ref_s3_data": null}], "text": "Figure 10: Example of a complex table with empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/18"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"name": "Table", "type": "table", "$ref": "#/tables/19"}, {"name": "Table", "type": "table", "$ref": "#/tables/20"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"prov": [{"bbox": [50.112, 414.454, 286.365, 434.961], "page": 14, "span": [0, 61], "__ref_s3_data": null}], "text": "Figure 11: Simple table with different style and empty cells.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/22"}, {"name": "Table", "type": "table", "$ref": "#/tables/23"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"prov": [{"bbox": [54.619, 111.36099999999999, 281.856, 119.91300000000001], "page": 14, "span": [0, 56], "__ref_s3_data": null}], "text": "Figure 12: Simple table predictions and post processing.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/24"}, {"name": "Table", "type": "table", "$ref": "#/tables/25"}, {"name": "Table", "type": "table", "$ref": "#/tables/26"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"name": "Table", "type": "table", "$ref": "#/tables/27"}, {"prov": [{"bbox": [344.985, 99.63300000000004, 508.989, 108.18499999999995], "page": 14, "span": [0, 40], "__ref_s3_data": null}], "text": "Figure 14: Example with multi-line text.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/28"}, {"name": "Table", "type": "table", "$ref": "#/tables/29"}, {"name": "Table", "type": "table", "$ref": "#/tables/30"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/17"}, {"name": "Table", "type": "table", "$ref": "#/tables/31"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/18"}, {"name": "Table", "type": "table", "$ref": "#/tables/32"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/19"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/20"}, {"name": "Table", "type": "table", "$ref": "#/tables/33"}, {"prov": [{"bbox": [84.233, 138.82799999999997, 252.242, 147.38], "page": 15, "span": [0, 41], "__ref_s3_data": null}], "text": "Figure 15: Example with triangular table.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/34"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/21"}, {"name": "Table", "type": "table", "$ref": "#/tables/35"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/22"}, {"name": "Table", "type": "table", "$ref": "#/tables/36"}, {"name": "Table", "type": "table", "$ref": "#/tables/37"}, {"prov": [{"bbox": [308.862, 118.28899999999999, 545.115, 138.79600000000005], "page": 15, "span": [0, 106], "__ref_s3_data": null}], "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/23"}], "figures": [{"prov": [{"bbox": [315.65362548828125, 489.1985778808594, 537.1475219726562, 563.276611328125], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [314.78173828125, 381.9505615234375, 539.1802978515625, 453.9347229003906], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.7172546386719, 295.9709777832031, 536.835693359375, 358.176513671875], "page": 1, "span": [0, 220], "__ref_s3_data": null}], "text": "Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [312.10369873046875, 541.3901519775391, 550.38916015625, 713.5591354370117], "page": 3, "span": [0, 104], "__ref_s3_data": null}], "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [74.30525970458984, 608.2984924316406, 519.9801025390625, 714.0887985229492], "page": 5, "span": [0, 212], "__ref_s3_data": null}], "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.03328323364258, 284.3311462402344, 285.3731689453125, 534.3346557617188], "page": 5, "span": [0, 745], "__ref_s3_data": null}], "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [49.97503662109375, 604.4210662841797, 301.6335754394531, 688.2873382568359], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [305.5836486816406, 611.3732452392578, 554.8258666992188, 693.3458404541016], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [51.736167907714844, 348.3419189453125, 211.83778381347656, 411.51934814453125], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [216.76925659179688, 348.65301513671875, 375.7829284667969, 411.5093688964844], "page": 8, "span": [0, 397], "__ref_s3_data": null}], "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [383.1364440917969, 349.2250671386719, 542.1132202148438, 410.7686767578125], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.54227066040039, 644.4090881347656, 544.938232421875, 717.2514572143555], "page": 12, "span": [0, 245], "__ref_s3_data": null}], "text": "Figure 7: Distribution of the tables across different dimensions per dataset. 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"col": 5, "col-header": false, "col-span": [5, 6], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [169.615, 285.628, 171.938, 290.274], "spans": [[9, 0]], "text": "\u8a08", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": true, "row-span": [9, 10]}, {"bbox": [183.105, 285.522, 190.857, 289.809], "spans": [[9, 1]], "text": "945", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [207.698, 285.522, 215.45, 289.809], "spans": [[9, 2]], "text": "294", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [232.292, 285.522, 240.043, 289.809], "spans": [[9, 3]], "text": "651", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [255.765, 285.522, 265.752, 289.809], "spans": [[9, 4]], "text": "1122", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [281.477, 285.522, 289.229, 289.809], "spans": [[9, 5]], "text": "955", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 9, "row-header": false, "row-span": [9, 10]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [304.9219970703125, 504.09930419921875, 550.2321166992188, 573.4851379394531], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 5, "#-rows": 7, "data": [[{"bbox": null, "spans": [[0, 0]], "text": "", "type": "body"}, {"bbox": [392.097, 221.639, 438.015, 226.62099999999998], "spans": [[0, 1], [0, 2]], "text": "Shares (in millions)", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [392.097, 221.639, 438.015, 226.62099999999998], "spans": [[0, 1], [0, 2]], "text": "Shares (in millions)", "type": "col_header", "col": 2, "col-header": true, "col-span": [1, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [459.049, 221.68899999999996, 542.0, 232.88099999999997], "spans": [[0, 3], [0, 4]], "text": "Weighted Average Grant Date Fair Value", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 5], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [459.049, 221.68899999999996, 542.0, 232.88099999999997], "spans": [[0, 3], [0, 4]], "text": "Weighted Average Grant Date Fair Value", "type": "col_header", "col": 4, "col-header": true, "col-span": [3, 5], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": null, "spans": [[1, 0]], "text": "", "type": "body"}, {"bbox": [393.244, 236.812, 407.346, 241.79399999999998], "spans": [[1, 1]], "text": "RSUs", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [427.183, 236.812, 440.988, 241.79399999999998], "spans": [[1, 2]], "text": "PSUs", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [468.383, 236.812, 482.485, 241.79399999999998], "spans": [[1, 3]], "text": "RSUs", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [516.926, 236.812, 530.73, 241.79399999999998], "spans": [[1, 4]], "text": "PSUs", "type": "col_header", "col": 4, "col-header": true, "col-span": [4, 5], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [306.115, 244.67600000000004, 364.656, 249.65700000000004], "spans": [[2, 0]], "text": "Nonvested on January 1", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": true, "row-span": [2, 3]}, {"bbox": [396.247, 244.97799999999995, 403.755, 249.96000000000004], "spans": [[2, 1]], "text": "1.1", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [429.818, 244.97799999999995, 437.327, 249.96000000000004], "spans": [[2, 2]], "text": "0.3", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [465.529, 244.97799999999995, 483.55, 249.96000000000004], "spans": [[2, 3]], "text": "90.10 $", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [513.448, 244.97799999999995, 531.47, 249.96000000000004], "spans": [[2, 4]], "text": "$ 91.19", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [306.115, 253.74900000000002, 325.627, 258.731], "spans": [[3, 0]], "text": "Granted", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": true, "row-span": [3, 4]}, {"bbox": [396.247, 253.74900000000002, 403.755, 258.731], "spans": [[3, 1]], "text": "0.5", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [429.818, 253.74900000000002, 437.327, 258.731], "spans": [[3, 2]], "text": "0.1", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [466.436, 253.74900000000002, 482.548, 258.731], "spans": [[3, 3]], "text": "117.44", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [514.291, 253.74900000000002, 530.81, 258.731], "spans": [[3, 4]], "text": "122.41", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [306.115, 261.61300000000006, 322.629, 266.595], "spans": [[4, 0]], "text": "Vested", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}, {"bbox": [394.432, 261.61300000000006, 405.536, 266.595], "spans": [[4, 1]], "text": "(0.5)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [427.702, 261.61300000000006, 438.806, 266.595], "spans": [[4, 2]], "text": "(0.1)", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [468.555, 261.61300000000006, 482.07, 266.595], "spans": [[4, 3]], "text": "87.08", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [516.019, 261.61300000000006, 529.534, 266.595], "spans": [[4, 4]], "text": "81.14", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [306.115, 269.706, 356.248, 274.688], "spans": [[5, 0]], "text": "Canceled or forfeited", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [394.432, 270.384, 405.536, 275.366], "spans": [[5, 1]], "text": "(0.1)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [431.028, 270.384, 436.428, 275.366], "spans": [[5, 2]], "text": "-", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [465.831, 270.384, 482.35, 275.366], "spans": [[5, 3]], "text": "102.01", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [516.019, 270.384, 529.534, 275.366], "spans": [[5, 4]], "text": "92.18", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [306.115, 278.55100000000004, 373.358, 283.532], "spans": [[6, 0]], "text": "Nonvested on December 31", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}, {"bbox": [396.247, 278.55100000000004, 403.755, 283.532], "spans": [[6, 1]], "text": "1.0", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [429.516, 278.55100000000004, 437.025, 283.532], "spans": [[6, 2]], "text": "0.3", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [463.714, 278.55100000000004, 484.74, 283.532], "spans": [[6, 3]], "text": "104.85 $", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [512.995, 278.55100000000004, 534.02, 283.532], "spans": [[6, 4]], "text": "$ 104.51", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [84.0283203125, 577.6066589355469, 239.1690673828125, 635.6664581298828], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [82.92001342773438, 500.716064453125, 239.1903533935547, 558.2236785888672], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [83.94786071777344, 424.0904235839844, 239.17135620117188, 482.9522705078125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [83.31756591796875, 304.7430114746094, 248.873046875, 395.9864501953125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [310.3294372558594, 655.8524780273438, 555.8338623046875, 690.8223266601562], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 9: Example of a table with big empty distance between cells.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.9566345214844, 607.2774658203125, 555.7466430664062, 637.3855133056641], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.9635314941406, 558.4485473632812, 555.7054443359375, 596.2945861816406], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [309.79150390625, 499.60601806640625, 425.9603271484375, 538.0946350097656], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [335.2694091796875, 354.97760009765625, 490.081787109375, 403.53253173828125], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 10: Example of a complex table with empty cells.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [334.9334716796875, 289.2789001464844, 490.0914306640625, 338.0523681640625], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [335.2545471191406, 224.31207275390625, 490.22369384765625, 272.92431640625], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [333.9573669433594, 126.5096435546875, 518.4768676757812, 198.8865966796875], "page": 13, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.72642135620117, 447.7554931640625, 283.114013671875, 518.3907165527344], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.434879302978516, 300.17974853515625, 310.7267150878906, 338.51251220703125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [50.86823654174805, 249.55401611328125, 310.6080017089844, 287.90374755859375], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [51.27280807495117, 200.086669921875, 311.0897216796875, 238.271484375], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [318.9809265136719, 577.3739471435547, 534.6229248046875, 630.7653961181641], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.0057678222656, 512.1423034667969, 534.408935546875, 565.8936614990234], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [328.1381530761719, 433.7275695800781, 523.8916015625, 503.3182067871094], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.4707946777344, 314.05645751953125, 518.5693359375, 361.09698486328125], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "Figure 14: Example with multi-line text.", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [319.982666015625, 256.30419921875, 519.0963745117188, 302.7562561035156], "page": 14, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": 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792.0, "page": 11, "width": 612.0}, {"height": 792.0, "page": 12, "width": 612.0}, {"height": 792.0, "page": 13, "width": 612.0}, {"height": 792.0, "page": 14, "width": 612.0}, {"height": 792.0, "page": 15, "width": 612.0}, {"height": 792.0, "page": 16, "width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/2203.01017v2.md b/tests/data/groundtruth/docling_v1/2203.01017v2.md index 52e22d4e..2cad1001 100644 --- a/tests/data/groundtruth/docling_v1/2203.01017v2.md +++ b/tests/data/groundtruth/docling_v1/2203.01017v2.md @@ -1,178 +1,220 @@ ## TableFormer: Table Structure Understanding with Transformers. -## Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar +## Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research -{ +{ ahn,nli,mly,taa @zurich.ibm.com } ## Abstract -Tables organize valuable content in a concise and com- +## a. Picture of a table: + +Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. ## 1. Introduction -The occurrence of tables - -## a. Picture of a table: +The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues. -- b. Red-annotation of bounding boxes, +- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer - c. Structure predicted by TableFormer: -Figure 1: +Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. -Recently, +| 0 | 1 2 1 | 1 2 1 | 1 2 1 | 1 2 1 | +|-----|---------|---------|---------|---------| +| 3 | 4 3 | 5 | 6 | 7 | +| 8 2 | 9 | 10 | 11 | 12 | +| 13 | | 14 | 15 | 16 | +| 17 | 18 | | 19 | 20 | -The first problem is called table-location and has been +Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. -considered as a solved problem, given enough ground-truth +The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be -The second problem is called table-structure decompo- +considered as a solved problem, given enough ground-truth data to train on. -In this paper, we want to address these weaknesses and +The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. -To meet the design criteria listed above, we developed a +In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image. -- • +To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows: -The paper is structured as follows. +- · We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. -its results & performance in Sec. 5. As a conclusion, we de- +- · Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works. + +- · We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity. + +- · An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility. + +The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe + +its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. ## 2. Previous work and State of the Art -Identifying the structure of a table has been an outstand- +Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc. -Before the rising +Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification. -Image-to-Text networks +Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder → text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder → dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the tag-decoder which is constrained to the table-tags. -In +In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper. -Graph +Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]. -Hybrid Deep Learning-Rule-Based approach +Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. ## 3. Datasets -We rely on large-scale datasets such as PubTabNet [37], +We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im- -Figure 2: +Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets balance in the previous datasets. -The PubTabNet dataset contains 509k tables delivered as +The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits. -Due to the heterogeneity across the dataset formats, it +Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small -amount of such tables, and kept only those ones ranging +amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns). -The availability of the bounding boxes for all table cells +The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. -As it is illustrated in Fig. 2, the table distributions from +As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. -Motivated by those observations we aimed at generating +Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. -In this regard, we have prepared four synthetic datasets, +In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third -Table +Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. +| | Tags | Bbox | Size | Format | +|--------------------|--------|--------|--------|----------| +| PubTabNet | 3 | 3 | 509k | PNG | +| FinTabNet | 3 | 3 | 112k | PDF | +| TableBank | 3 | 7 | 145k | JPEG | +| Combined-Tabnet(*) | 3 | 3 | 400k | PNG | +| Combined(**) | 3 | 3 | 500k | PNG | +| SynthTabNet | 3 | 3 | 600k | PNG | - -one adopts a colorful appearance with high contrast and the +one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples. Tab. 1 summarizes the various attributes of the datasets. ## 4. The TableFormer model -Given the image of a table, TableFormer is able to pre- +Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required. ## 4.1. Model architecture. -We now describe in detail the proposed method, which +We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. -CNN Backbone Network. +CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per- -Figure 3: +Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure. -Figure 4: +Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes. -forming classification, and adding an adaptive pooling layer +forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder . -Structure Decoder. +Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images. -The +The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score. -Cell BBox Decoder. +Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query. -The encoding generated by the +The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at- -tention encoding is then multiplied to the encoded image to +tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence. -The output +The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer. -Loss Functions. +Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. -The loss used to train the TableFormer can be defined as +The loss used to train the TableFormer can be defined as following: -where +where λ ∈ [0, 1], and λ iou , λ l 1 ∈ R are hyper-parameters. ## 5. Experimental Results ## 5.1. Implementation Details -TableFormer uses ResNet-18 as the +TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: -Although input constraints are used also by other methods, runtime performance and lower memory footprint of Table- +Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions. -The Transformer Encoder consists of two 'Transformer +The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5. -For training, TableFormer is trained with 3 Adam opti- +For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. -TableFormer is implemented with PyTorch and Torchvi- +TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. ## 5.2. Generalization -TableFormer is evaluated on three major publicly avail- +TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively. -We also share our baseline results on the challenging +We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized. ## 5.3. Datasets and Metrics -The Tree-Edit-Distance-Based Similarity (TEDS) met- +The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as: -where +where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T . ## 5.4. Quantitative Analysis -Structure. +Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. -Table 2: +| Model | Dataset | Simple | TEDS Complex | All | +|-------------|-----------|----------|----------------|-------| +| EDD | PTN | 91.1 | 88.7 | 89.9 | +| GTE | PTN | - | - | 93.01 | +| TableFormer | PTN | 98.5 | 95.0 | 96.75 | +| EDD | FTN | 88.4 | 92.08 | 90.6 | +| GTE | FTN | - | - | 87.14 | +| GTE (FT) | FTN | - | - | 91.02 | +| TableFormer | FTN | 97.5 | 96.0 | 96.8 | +| EDD | TB | 86.0 | - | 86 | +| TableFormer | TB | 89.6 | - | 89.6 | +| TableFormer | STN | 96.9 | 95.7 | 96.7 | + +Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). FT: Model was trained on PubTabNet then finetuned. -Cell Detection. +Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. -our +Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. -Table 3: +| Model | Dataset | mAP | mAP (PP) | +|-------------|-------------|-------|------------| +| EDD+BBox | PubTabNet | 79.2 | 82.7 | +| TableFormer | PubTabNet | 82.1 | 86.8 | +| TableFormer | SynthTabNet | 87.7 | - | +Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. +Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. -Cell Content. - -Table 4: - - +| Model | Simple | TEDS Complex | All | +|-------------|----------|----------------|-------| +| Tabula | 78 | 57.8 | 67.9 | +| Traprange | 60.8 | 49.9 | 55.4 | +| Camelot | 80 | 66 | 73 | +| Acrobat Pro | 68.9 | 61.8 | 65.3 | +| EDD | 91.2 | 85.4 | 88.3 | +| TableFormer | 95.4 | 90.1 | 93.6 | - a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells @@ -182,204 +224,224 @@ Table 4: - - b. Structure predicted by TableFormer, with superimposed matched PDF cell text: + Text is aligned to match original for ease of viewing -Figure 5: +| | | 論文ファイル | 論文ファイル | 参考文献 | 参考文献 | +|----------------------------------------------------|-----------------|----------------|----------------|------------|------------| +| 出典 | ファイル数 英語 | | 日本語 | 英語 | 日本語 | +| Association for Computational Linguistics(ACL2003) | 65 | 65 | 0 | 150 | 0 | +| Computational Linguistics(COLING2002) | 140 | 140 | 0 | 150 | 0 | +| 電気情報通信学会 2003 年総合大会 | 150 | 8 | 142 | 223 | 147 | +| 情報処理学会第 65 回全国大会 (2003) | 177 | 1 | 176 | 150 | 236 | +| 第 17 回人工知能学会全国大会 (2003) | 208 | 5 | 203 | 152 | 244 | +| 自然言語処理研究会第 146 〜 155 回 | 98 | 2 | 96 | 150 | 232 | +| WWW から収集した論文 | 107 | 73 | 34 | 147 | 96 | +| 計 | 945 | 294 | 651 | 1122 | 955 | + +| | Shares (in millions) | Shares (in millions) | Weighted Average Grant Date Fair Value | Weighted Average Grant Date Fair Value | +|--------------------------|------------------------|------------------------|------------------------------------------|------------------------------------------| +| | RSUs | PSUs | RSUs | PSUs | +| Nonvested on January 1 | 1.1 | 0.3 | 90.10 $ | $ 91.19 | +| Granted | 0.5 | 0.1 | 117.44 | 122.41 | +| Vested | (0.5) | (0.1) | 87.08 | 81.14 | +| Canceled or forfeited | (0.1) | - | 102.01 | 92.18 | +| Nonvested on December 31 | 1.0 | 0.3 | 104.85 $ | $ 104.51 | + +Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table. + + +Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset. -Figure 6: ## 5.5. Qualitative Analysis -We showcase - - - ## 6. Future Work & Conclusion -In this paper, we presented TableFormer an end-to-end +We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type. + +In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1 -## TableFormer: Table Structure Understanding with Transformers +## TableFormer: Table Structure Understanding with Transformers Supplementary Material ## 1. Details on the datasets -ances in regard to their size, - ## 1.1. Data preparation -As a first step of our data preparation process, we have +As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length. -We have developed a technique that tries +We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. -Figure 7 illustrates the distribution of the tables across +Figure 7 illustrates the distribution of the tables across different dimensions per dataset. ## 1.2. Synthetic datasets -Aiming to train and evaluate our models in a broader +Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). -## 2. +The process of generating a synthetic dataset can be decomposed into the following steps: -The process of generating a synthetic dataset can be de- +- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). -- 1. +- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. -- 2. +- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. -- 3. Generate content: Based on the dataset +- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table. -- 4. +- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process. -- 5. +## 2. Prediction post-processing for PDF documents -Although TableFormer can predict the table structure and +Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons: -Figure 7: +Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity. -- • +- · TableFormer output does not include the table cell content. -However, it is possible to mitigate those limitations by +- · There are occasional inaccuracies in the predictions of the bounding boxes. -Here is a step-by-step description of the prediction post- +dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. -- 1. Get the minimal grid dimensions - number of rows and +However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes. -- 2. +Here is a step-by-step description of the prediction postprocessing: -- 3. +- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure. -- 3.a. +- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches. -- 4. +- 3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones. -where +- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column. -- 5. +- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: -dian cell size for all table cells. +where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point. -- 6. +- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me- -- 7. +- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes. -- 8. +- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells. -- 9. +- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score. -9a. +- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan. -- 9b. +9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row). -- 9c. +- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row. -- 9d. Intersect the orphan's bounding box with the column +- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column). -- 9e. If the table cell under the identified row and column +- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column. + +- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or- phan cell. -9f. +9f. Otherwise create a new structural cell and match it wit the orphan cell. -Aditional images with examples of TableFormer predic- +Aditional images with examples of TableFormer predictions and post-processing can be found below. -Figure 8: +Figure 8: Example of a table with multi-line header. -Figure 9: +Figure 9: Example of a table with big empty distance between cells. -Figure 10: +Figure 10: Example of a complex table with empty cells. @@ -387,20 +449,19 @@ Figure 10: -Figure 11: +Figure 11: Simple table with different style and empty cells. -Figure 12: +Figure 12: Simple table predictions and post processing. -Figure 13: +Figure 13: Table predictions example on colorful table. -Figure 14: +Figure 14: Example with multi-line text. -Figure 15: @@ -409,7 +470,7 @@ Figure 15: -Figure 16: +Figure 15: Example with triangular table. @@ -417,5 +478,7 @@ Figure 16: -Figure 17: +Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact. + +Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json b/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json index b5d96fca..c74d2e11 100644 --- a/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json +++ b/tests/data/groundtruth/docling_v1/2203.01017v2.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 96.301, "r_y0": 119.93100000000004, "r_x1": 498.927, "r_y1": 119.93100000000004, "r_x2": 498.927, "r_y2": 107.03399999999999, "r_x3": 96.301, "r_y3": 107.03399999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers.", "orig": "TableFormer: Table Structure Understanding with Transformers.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.477, "r_y0": 157.26999999999998, "r_x1": 452.75, "r_y1": 157.26999999999998, "r_x2": 452.75, "r_y2": 147.00800000000004, "r_x3": 142.477, "r_y3": 147.00800000000004, "coord_origin": "TOPLEFT"}, "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "orig": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.918, "r_y0": 171.21699999999998, "r_x1": 332.306, "r_y1": 171.21699999999998, "r_x2": 332.306, "r_y2": 160.95500000000004, "r_x3": 262.918, "r_y3": 160.95500000000004, "coord_origin": "TOPLEFT"}, "text": "IBM Research", "orig": "IBM Research", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.123, "r_y0": 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"g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 297.804, "r_x1": 286.365, "r_y1": 297.804, "r_x2": 286.365, "r_y2": 289.216, "r_x3": 50.112, "r_y3": 289.216, "coord_origin": "TOPLEFT"}, "text": "nately, tables come in a large variety of shapes and sizes.", "orig": "nately, tables come in a large variety of shapes and sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 309.759, "r_x1": 103.601, "r_y1": 309.759, "r_x2": 103.601, "r_y2": 301.171, "r_x3": 50.112, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "Furthermore,", "orig": "Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.393, "r_y0": 309.759, "r_x1": 124.692, "r_y1": 309.759, "r_x2": 124.692, "r_y2": 301.171, "r_x3": 108.393, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "they", "orig": "they", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 129.026, "r_y0": 309.759, "r_x1": 286.365, "r_y1": 309.759, "r_x2": 286.365, "r_y2": 301.171, "r_x3": 129.026, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "can have complex column/row-header", "orig": "can have complex column/row-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 321.714, "r_x1": 286.365, "r_y1": 321.714, "r_x2": 286.365, "r_y2": 313.126, "r_x3": 50.112, "r_y3": 313.126, "coord_origin": "TOPLEFT"}, "text": "configurations, multiline rows, different variety of separa-", "orig": "configurations, multiline rows, different variety of separa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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"orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.947, "r_y0": 345.624, "r_x1": 286.365, "r_y1": 345.624, "r_x2": 286.365, "r_y2": 337.036, "r_x3": 181.947, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "from an image is a non-", "orig": "from an image is a non-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 357.58, "r_x1": 95.791, "r_y1": 357.58, "r_x2": 95.791, "r_y2": 348.992, "r_x3": 50.112, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "trivial task.", "orig": "trivial task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, "r_x1": 132.443, "r_y1": 369.535, "r_x2": 132.443, "r_y2": 360.947, "r_x3": 50.112, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "identification model.", "orig": "identification model.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.777, "r_y0": 369.535, "r_x1": 286.365, "r_y1": 369.535, "r_x2": 286.365, "r_y2": 360.947, "r_x3": 136.777, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "The latter improves the latest end-to-", "orig": "The latter improves the latest end-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 381.49, "r_x1": 170.4, "r_y1": 381.49, "r_x2": 170.4, "r_y2": 372.902, "r_x3": 50.112, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "end deep learning model (i.e.", "orig": "end deep learning model (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 417.355, "r_x1": 286.365, "r_y1": 417.355, "r_x2": 286.365, "r_y2": 408.767, "r_x3": 50.112, "r_y3": 408.767, "coord_origin": "TOPLEFT"}, "text": "we can obtain the content of the table-cells from program-", "orig": "we can obtain the content of the table-cells from program-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 429.311, "r_x1": 286.365, "r_y1": 429.311, "r_x2": 286.365, "r_y2": 420.723, "r_x3": 50.112, "r_y3": 420.723, "coord_origin": "TOPLEFT"}, "text": "matic PDF's directly from the PDF source and avoid the", "orig": "matic PDF's directly from the PDF source and avoid the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 441.266, "r_x1": 207.232, "r_y1": 441.266, "r_x2": 207.232, "r_y2": 432.678, "r_x3": 50.112, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. Red-annotation of bounding boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.463, "r_y0": 333.15, "r_x1": 472.474, "r_y1": 333.15, "r_x2": 472.474, "r_y2": 324.837, "r_x3": 326.463, "r_y3": 324.837, "coord_origin": "TOPLEFT"}, "text": "Blue-predictions by TableFormer", "orig": "Blue-predictions by TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 428.838, "r_x1": 491.191, "r_y1": 428.838, "r_x2": 491.191, "r_y2": 420.525, "r_x3": 315.567, "r_y3": 420.525, "coord_origin": "TOPLEFT"}, "text": "c. Structure predicted by TableFormer:", "orig": "c. 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Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 62.067, "t": 561.052, "r": 166.266, "b": 569.604, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The occurrence of tables"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 584.678, "r": 358.077, "b": 593.23, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 593.23, "r_x1": 358.077, "r_y1": 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Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.172, "r_x1": 286.365, "r_y1": 288.172, "r_x2": 286.365, "r_y2": 279.62, "r_x3": 50.112, "r_y3": 279.62, "coord_origin": "TOPLEFT"}, "text": "we can obtain the structure of any table, irregardless of the", "orig": "we can obtain the structure of any table, irregardless of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.128, "r_x1": 88.568, "r_y1": 300.128, "r_x2": 88.568, "r_y2": 291.576, "r_x3": 50.112, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "language.", "orig": "language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.502, "r_y0": 300.128, "r_x1": 286.365, "r_y1": 300.128, "r_x2": 286.365, "r_y2": 291.576, "r_x3": 95.502, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "Second, we want our algorithm to leverage as", "orig": "Second, we want our algorithm to leverage as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.083, "r_x1": 286.365, "r_y1": 312.083, "r_x2": 286.365, "r_y2": 303.531, "r_x3": 50.112, "r_y3": 303.531, "coord_origin": "TOPLEFT"}, "text": "much data as possible from the original PDF document. For", "orig": "much data as possible from the original PDF document. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.038, "r_x1": 286.365, "r_y1": 324.038, "r_x2": 286.365, "r_y2": 315.486, "r_x3": 50.112, "r_y3": 315.486, "coord_origin": "TOPLEFT"}, "text": "programmatic PDF documents, the text-cells can often be", "orig": "programmatic PDF documents, the text-cells can often be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.993, "r_x1": 286.365, "r_y1": 335.993, "r_x2": 286.365, "r_y2": 327.441, "r_x3": 50.112, "r_y3": 327.441, "coord_origin": "TOPLEFT"}, "text": "extracted much faster and with higher accuracy compared", "orig": "extracted much faster and with higher accuracy compared", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.948, "r_x1": 286.365, "r_y1": 347.948, "r_x2": 286.365, "r_y2": 339.396, "r_x3": 50.112, "r_y3": 339.396, "coord_origin": "TOPLEFT"}, "text": "to OCR methods. Last but not least, we want to have a di-", "orig": "to OCR methods. Last but not least, we want to have a di-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.904, "r_x1": 286.365, "r_y1": 359.904, "r_x2": 286.365, "r_y2": 351.352, "r_x3": 50.112, "r_y3": 351.352, "coord_origin": "TOPLEFT"}, "text": "rect link between the table-cell and its bounding box in the", "orig": "rect link between the table-cell and its bounding box in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.859, "r_x1": 76.951, "r_y1": 371.859, "r_x2": 76.951, "r_y2": 363.307, "r_x3": 50.112, "r_y3": 363.307, "coord_origin": "TOPLEFT"}, "text": "image.", "orig": "image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 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"r_y2": 399.625, "r_x3": 249.718, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "In partic-", "orig": "In partic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.132, "r_x1": 67.696, "r_y1": 420.132, "r_x2": 67.696, "r_y2": 411.58, "r_x3": 50.112, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "ular,", "orig": "ular,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.322, "r_y0": 420.132, "r_x1": 286.365, "r_y1": 420.132, "r_x2": 286.365, "r_y2": 411.58, "r_x3": 71.322, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "our contributions in this work can be summarised as", "orig": "our contributions in this work can be summarised as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.087, "r_x1": 82.52, "r_y1": 432.087, "r_x2": 82.52, "r_y2": 423.535, "r_x3": 50.112, "r_y3": 423.535, "coord_origin": "TOPLEFT"}, "text": "follows:", "orig": "follows:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 453.372, "r_x1": 65.056, "r_y1": 453.372, "r_x2": 65.056, "r_y2": 444.82, "r_x3": 61.569, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 453.372, "r_x1": 117.101, "r_y1": 453.372, "r_x2": 117.101, "r_y2": 444.82, "r_x3": 70.037, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "We propose", "orig": "We propose", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": 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structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.845, "r_y0": 342.39, "r_x1": 436.802, "r_y1": 342.39, "r_x2": 436.802, "r_y2": 333.838, "r_x3": 395.845, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "popularity", "orig": "popularity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.145, "r_y0": 342.39, "r_x1": 449.444, "r_y1": 342.39, "r_x2": 449.444, "r_y2": 333.838, "r_x3": 441.145, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.798, "r_y0": 342.39, "r_x1": 545.115, "r_y1": 342.39, "r_x2": 545.115, "r_y2": 333.838, "r_x3": 453.798, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "deep neural networks,", "orig": "deep neural networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 354.345, "r_x1": 545.115, "r_y1": 354.345, "r_x2": 545.115, "r_y2": 345.793, "r_x3": 308.862, "r_y3": 345.793, "coord_origin": "TOPLEFT"}, "text": "the community relied heavily on heuristic and/or statistical", "orig": "the community relied heavily on heuristic and/or statistical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 366.3, "r_x1": 545.115, "r_y1": 366.3, "r_x2": 545.115, "r_y2": 357.748, "r_x3": 308.862, "r_y3": 357.748, "coord_origin": "TOPLEFT"}, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "orig": "methods to do table structure identification [3, 7, 11, 5, 13,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 378.256, "r_x1": 324.633, "r_y1": 378.256, "r_x2": 324.633, "r_y2": 369.704, "r_x3": 308.862, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "28].", "orig": "28].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.634, "r_y0": 378.256, "r_x1": 545.115, "r_y1": 378.256, "r_x2": 545.115, "r_y2": 369.704, "r_x3": 329.634, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "Although such methods work well on constrained ta-", "orig": "Although such methods work well on constrained ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 390.211, "r_x1": 545.115, "r_y1": 390.211, "r_x2": 545.115, "r_y2": 381.659, "r_x3": 308.862, "r_y3": 381.659, "coord_origin": "TOPLEFT"}, "text": "bles [12], a more data-driven approach can be applied due", "orig": "bles [12], a more data-driven approach can be applied due", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 402.166, "r_x1": 545.115, "r_y1": 402.166, "r_x2": 545.115, "r_y2": 393.614, "r_x3": 308.862, "r_y3": 393.614, "coord_origin": "TOPLEFT"}, "text": "to the advent of convolutional neural networks (CNNs) and", "orig": "to the advent of convolutional neural networks (CNNs) and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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ar-", "orig": "edge, there are currently two different types of network ar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 438.032, "r_x1": 545.115, "r_y1": 438.032, "r_x2": 545.115, "r_y2": 429.48, "r_x3": 308.862, "r_y3": 429.48, "coord_origin": "TOPLEFT"}, "text": "chitecture that are being pursued for state-of-the-art table-", "orig": "chitecture that are being pursued for state-of-the-art table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 449.987, "r_x1": 401.285, "r_y1": 449.987, "r_x2": 401.285, "r_y2": 441.435, "r_x3": 308.862, "r_y3": 441.435, "coord_origin": "TOPLEFT"}, "text": "structure identification.", "orig": "structure identification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, 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"r_x2": 459.218, "r_y2": 489.321, "r_x3": 308.862, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "tags [37, 17] or LaTeX symbols[10].", "orig": "tags [37, 17] or LaTeX symbols[10].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.115, "r_y0": 497.873, "r_x1": 545.115, "r_y1": 497.873, "r_x2": 545.115, "r_y2": 489.321, "r_x3": 465.115, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "The choice of sym-", "orig": "The choice of sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.828, "r_x1": 545.115, "r_y1": 509.828, "r_x2": 545.115, "r_y2": 501.276, "r_x3": 308.862, "r_y3": 501.276, "coord_origin": "TOPLEFT"}, "text": "bols is ultimately not very important, since one can be trans-", "orig": "bols is ultimately not very important, since one can be trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.784, "r_x1": 396.692, "r_y1": 521.784, "r_x2": 396.692, "r_y2": 513.232, "r_x3": 308.862, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "formed into the other.", "orig": "formed into the other.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.265, "r_y0": 521.784, "r_x1": 545.115, "r_y1": 521.784, "r_x2": 545.115, "r_y2": 513.232, "r_x3": 401.265, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "There are however subtle variations", "orig": "There are however subtle variations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.739, "r_x1": 433.484, "r_y1": 533.739, "r_x2": 433.484, "r_y2": 525.187, "r_x3": 308.862, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "in the Image-to-Text networks.", "orig": "in the Image-to-Text networks.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 438.187, "r_y0": 533.739, "r_x1": 545.115, "r_y1": 533.739, "r_x2": 545.115, "r_y2": 525.187, "r_x3": 438.187, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "The easiest network archi-", "orig": "The easiest network archi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.694, "r_x1": 420.941, "r_y1": 545.694, "r_x2": 420.941, "r_y2": 537.142, "r_x3": 308.862, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "tectures are 'image-encoder", "orig": "tectures are 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.595, "r_y0": 545.388, "r_x1": 433.558, "r_y1": 545.388, "r_x2": 433.558, "r_y2": 536.8240000000001, "r_x3": 423.595, "r_y3": 536.8240000000001, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.212, "r_y0": 545.694, "r_x1": 545.113, "r_y1": 545.694, "r_x2": 545.113, "r_y2": 537.142, "r_x3": 436.212, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "text-decoder' (IETD), sim-", "orig": "text-decoder' (IETD), sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.649, "r_x1": 545.115, "r_y1": 557.649, "r_x2": 545.115, "r_y2": 549.097, "r_x3": 308.862, "r_y3": 549.097, "coord_origin": "TOPLEFT"}, "text": "ilar to network architectures that try to provide captions to", "orig": "ilar to network architectures that try to provide captions to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.604, "r_x1": 545.115, "r_y1": 569.604, "r_x2": 545.115, "r_y2": 561.052, "r_x3": 308.862, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "images [32]. In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.045, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 364.045, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "to generate the textual content of each table", "orig": "to generate the textual content of each table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 325.739, "r_y1": 701.111, "r_x2": 325.739, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.746, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 331.746, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "The network architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "considered as a solved problem, given enough ground-truth", "orig": "considered as a solved problem, given enough ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 99.84000000000003, "r": 286.365, "b": 108.39200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 142.49, "r": 545.115, "b": 151.04200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 231.79999999999995, "r": 286.365, "b": 240.35199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 333.838, "r": 391.492, "b": 342.39, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 375.715, "r": 286.365, "b": 384.267, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569, "t": 444.82, "r": 65.056, "b": 453.372, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 453.372, "r_x1": 65.056, "r_y1": 453.372, "r_x2": 65.056, "r_y2": 444.82, "r_x3": 61.569, "r_y3": 444.82, 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As a conclusion, we de-"}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 99.84000000000003, "r": 286.365, "b": 108.39200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The second problem is called table-structure decompo-"}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 142.49, "r": 545.115, "b": 151.04200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Identifying the structure of a table has been an outstand-"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 231.79999999999995, "r": 286.365, "b": 240.35199999999998, "coord_origin": 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333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Before the rising"}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 375.715, "r": 286.365, "b": 384.267, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To meet the design criteria listed above, we developed a"}, 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As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we de-"}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 99.84000000000003, "r": 286.365, "b": 108.39200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The second problem is called table-structure decompo-"}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 142.49, "r": 545.115, "b": 151.04200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Identifying the structure of a table has been an outstand-"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 231.79999999999995, "r": 286.365, "b": 240.35199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we want to address these weaknesses and"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 333.838, "r": 391.492, "b": 342.39, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Before the rising"}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 375.715, "r": 286.365, "b": 384.267, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To meet the design criteria listed above, we developed a"}, 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table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.90599999999995, "r_x1": 286.365, "r_y1": 131.90599999999995, "r_x2": 286.365, "r_y2": 123.35400000000004, "r_x3": 50.112, "r_y3": 123.35400000000004, "coord_origin": "TOPLEFT"}, "text": "In the case of IETD, this OCR engine is implicit in the de-", "orig": "In the case of IETD, this OCR engine is implicit in the de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.861, "r_x1": 286.365, "r_y1": 143.861, "r_x2": 286.365, "r_y2": 135.30899999999997, "r_x3": 50.112, "r_y3": 135.30899999999997, "coord_origin": "TOPLEFT"}, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.849, "r_y0": 311.29, "r_x1": 286.365, "r_y1": 311.29, "r_x2": 286.365, "r_y2": 302.738, "r_x3": 138.849, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "Note that one table cell can consti-", "orig": "Note that one table cell can consti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.245, "r_x1": 286.365, "r_y1": 323.245, "r_x2": 286.365, "r_y2": 314.693, "r_x3": 50.112, "r_y3": 314.693, "coord_origin": "TOPLEFT"}, "text": "tute out of multiple text-cells. To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.11, "r_x1": 286.365, "r_y1": 359.11, "r_x2": 286.365, "r_y2": 350.558, "r_x3": 50.112, "r_y3": 350.558, "coord_origin": "TOPLEFT"}, "text": "node is then associated with en embedding vector coming", "orig": "node is then associated with en embedding vector coming", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.066, "r_x1": 286.365, "r_y1": 371.066, "r_x2": 286.365, "r_y2": 362.514, "r_x3": 50.112, "r_y3": 362.514, "coord_origin": "TOPLEFT"}, "text": "from the encoded image, its coordinates and the encoded", "orig": "from the encoded image, its coordinates and the encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.021, "r_x1": 67.397, "r_y1": 383.021, "r_x2": 67.397, "r_y2": 374.469, "r_x3": 50.112, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "text.", "orig": "text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 72.976, "r_y0": 383.021, "r_x1": 286.365, "r_y1": 383.021, "r_x2": 286.365, "r_y2": 374.469, "r_x3": 72.976, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "Furthermore, nodes that represent adjacent text-cells", "orig": "Furthermore, nodes that represent adjacent text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 394.976, "r_x1": 92.602, "r_y1": 394.976, "r_x2": 92.602, "r_y2": 386.424, "r_x3": 50.112, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "are linked.", "orig": "are linked.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.305, "r_y0": 394.976, "r_x1": 286.365, "r_y1": 394.976, "r_x2": 286.365, "r_y2": 386.424, "r_x3": 97.305, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "Graph Convolutional Networks (GCN's) based", "orig": "Graph Convolutional Networks (GCN's) based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 406.931, "r_x1": 286.365, "r_y1": 406.931, "r_x2": 286.365, "r_y2": 398.379, "r_x3": 50.112, "r_y3": 398.379, "coord_origin": "TOPLEFT"}, "text": "methods take the image as an input, but also the position of", "orig": "methods take the image as an input, but also the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 418.886, "r_x1": 286.365, "r_y1": 418.886, "r_x2": 286.365, "r_y2": 410.334, "r_x3": 50.112, "r_y3": 410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based 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"TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", 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"rule-sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.975, "r_y0": 574.36, "r_x1": 256.726, "r_y1": 574.36, "r_x2": 256.726, "r_y2": 565.808, "r_x3": 248.975, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.459, "r_y0": 574.36, "r_x1": 286.365, "r_y1": 574.36, "r_x2": 286.365, "r_y2": 565.808, "r_x3": 261.459, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "obtain", "orig": "obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.315, "r_x1": 122.64, "r_y1": 586.315, "r_x2": 122.64, "r_y2": 577.763, "r_x3": 50.112, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "its table-structure.", "orig": "its table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.482, "r_y0": 586.315, "r_x1": 286.365, "r_y1": 586.315, "r_x2": 286.365, "r_y2": 577.763, "r_x3": 127.482, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "Currently, this approach achieves state-", "orig": "Currently, this approach achieves state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 598.27, "r_x1": 87.731, "r_y1": 598.27, "r_x2": 87.731, "r_y2": 589.718, "r_x3": 50.112, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "of-the-art", "orig": "of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.074, "r_y0": 598.27, "r_x1": 120.577, "r_y1": 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"r_y1": 598.27, "r_x2": 165.987, "r_y2": 589.718, "r_x3": 153.255, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "not", "orig": "not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.331, "r_y0": 598.27, "r_x1": 286.365, "r_y1": 598.27, "r_x2": 286.365, "r_y2": 589.718, "r_x3": 170.331, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "an end-to-end deep-learning", "orig": "an end-to-end deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 610.225, "r_x1": 82.49, "r_y1": 610.225, "r_x2": 82.49, "r_y2": 601.673, "r_x3": 50.112, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.173, "r_y0": 610.225, "r_x1": 286.365, "r_y1": 610.225, "r_x2": 286.365, "r_y2": 601.673, "r_x3": 87.173, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "As such, new rules need to be written if different", "orig": "As such, new rules need to be written if different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 622.181, "r_x1": 175.989, "r_y1": 622.181, "r_x2": 175.989, "r_y2": 613.629, "r_x3": 50.112, "r_y3": 613.629, "coord_origin": "TOPLEFT"}, "text": "types of tables are encountered.", "orig": "types of tables are encountered.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 646.693, "r_x1": 105.225, "r_y1": 646.693, "r_x2": 105.225, "r_y2": 635.9449999999999, "r_x3": 50.112, "r_y3": 635.9449999999999, "coord_origin": "TOPLEFT"}, "text": "3. Datasets", "orig": "3. Datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 62.067, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "We rely on large-scale datasets such as PubTabNet [37],", "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "orig": "FinTabNet [36], and TableBank [17] datasets to train and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, 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"orig": "pearance styles and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.688, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 173.688, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "We also introduce our own", "orig": "We also introduce our own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "synthetically generated SynthTabNet dataset to fix an im-", "orig": "synthetically generated SynthTabNet dataset to fix an im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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datasets", "orig": "dimensions in PubTabNet + FinTabNet datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 326.294, "r_x1": 437.27, "r_y1": 326.294, "r_x2": 437.27, "r_y2": 317.742, "r_x3": 308.862, "r_y3": 317.742, "coord_origin": "TOPLEFT"}, "text": "balance in the previous datasets.", "orig": "balance in the previous datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 340.352, "r_x1": 545.115, "r_y1": 340.352, "r_x2": 545.115, "r_y2": 331.8, "r_x3": 320.817, "r_y3": 331.8, "coord_origin": "TOPLEFT"}, "text": "The PubTabNet dataset contains 509k tables delivered as", "orig": "The PubTabNet dataset contains 509k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, 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The annotations consist of the table", "orig": "annotated PNG images. The annotations consist of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 364.263, "r_x1": 545.115, "r_y1": 364.263, "r_x2": 545.115, "r_y2": 355.711, "r_x3": 308.862, "r_y3": 355.711, "coord_origin": "TOPLEFT"}, "text": "structure represented in HTML format, the tokenized text", "orig": "structure represented in HTML format, the tokenized text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.218, "r_x1": 545.115, "r_y1": 376.218, "r_x2": 545.115, "r_y2": 367.666, "r_x3": 308.862, "r_y3": 367.666, "coord_origin": "TOPLEFT"}, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "orig": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.173, "r_x1": 545.115, "r_y1": 388.173, "r_x2": 545.115, "r_y2": 379.621, "r_x3": 308.862, "r_y3": 379.621, "coord_origin": "TOPLEFT"}, "text": "pearance style of PubTabNet. Depending on its complexity,", "orig": "pearance style of PubTabNet. Depending on its complexity,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.128, "r_x1": 545.115, "r_y1": 400.128, "r_x2": 545.115, "r_y2": 391.576, "r_x3": 308.862, "r_y3": 391.576, "coord_origin": "TOPLEFT"}, "text": "a table is characterized as 'simple' when it does not contain", "orig": "a table is characterized as 'simple' when it does not contain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.083, "r_x1": 545.115, "r_y1": 412.083, "r_x2": 545.115, "r_y2": 403.531, "r_x3": 308.862, "r_y3": 403.531, "coord_origin": "TOPLEFT"}, "text": "row spans or column spans, otherwise it is 'complex'. The", "orig": "row spans or column spans, otherwise it is 'complex'. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.038, "r_x1": 545.115, "r_y1": 424.038, "r_x2": 545.115, "r_y2": 415.486, "r_x3": 308.862, "r_y3": 415.486, "coord_origin": "TOPLEFT"}, "text": "dataset is divided into Train and Val splits (roughly 98% and", "orig": "dataset is divided into Train and Val splits (roughly 98% and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.994, "r_x1": 545.115, "r_y1": 435.994, "r_x2": 545.115, "r_y2": 427.442, "r_x3": 308.862, "r_y3": 427.442, "coord_origin": "TOPLEFT"}, "text": "2%). The Train split consists of 54% simple and 46% com-", "orig": "2%). The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized 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"from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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Datasets", "orig": "3. 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Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 656.694, "r": 286.365, "b": 665.246, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 62.067, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "We rely on large-scale datasets such as PubTabNet [37],", "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37],"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "amount of such tables, and kept only those ones ranging", "orig": "amount of such tables, and kept only those ones ranging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 212.283, "r_y1": 95.98400000000004, "r_x2": 212.283, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "between 1*1 and 20*10 (rows/columns).", "orig": "between 1*1 and 20*10 (rows/columns).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 109.78099999999995, "r_x1": 286.365, "r_y1": 109.78099999999995, "r_x2": 286.365, "r_y2": 101.22900000000004, "r_x3": 62.067, "r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 121.73599999999999, "r_x1": 176.458, "r_y1": 121.73599999999999, "r_x2": 176.458, "r_y2": 113.18399999999997, "r_x3": 50.112, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "is essential to train our models.", "orig": "is essential to train our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.971, "r_y0": 121.73599999999999, "r_x1": 286.365, "r_y1": 121.73599999999999, "r_x2": 286.365, "r_y2": 113.18399999999997, "r_x3": 180.971, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "In order to distinguish be-", "orig": "In order to distinguish be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 133.69100000000003, "r_x1": 286.365, "r_y1": 133.69100000000003, "r_x2": 286.365, "r_y2": 125.13900000000001, "r_x3": 50.112, "r_y3": 125.13900000000001, "coord_origin": "TOPLEFT"}, "text": "tween empty and non-empty bounding boxes, we have in-", "orig": "tween empty and non-empty bounding boxes, we have in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 145.64700000000005, "r_x1": 286.365, "r_y1": 145.64700000000005, "r_x2": 286.365, "r_y2": 137.09500000000003, "r_x3": 50.112, "r_y3": 137.09500000000003, "coord_origin": "TOPLEFT"}, "text": "troduced a binary class in the annotation. Unfortunately, the", "orig": "troduced a binary class in the annotation. Unfortunately, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 157.60199999999998, "r_x1": 286.365, "r_y1": 157.60199999999998, "r_x2": 286.365, "r_y2": 149.04999999999995, "r_x3": 50.112, "r_y3": 149.04999999999995, "coord_origin": "TOPLEFT"}, "text": "original datasets either omit the bounding boxes for whole", "orig": "original datasets either omit the bounding boxes for whole", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 169.55700000000002, "r_x1": 94.127, "r_y1": 169.55700000000002, "r_x2": 94.127, "r_y2": 161.005, "r_x3": 50.112, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "tables (e.g.", "orig": "tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.959, "r_y0": 169.55700000000002, "r_x1": 286.365, "r_y1": 169.55700000000002, "r_x2": 286.365, "r_y2": 161.005, "r_x3": 98.959, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "TableBank) or they narrow their scope only to", "orig": "TableBank) or they narrow their scope only to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 181.51199999999994, "r_x1": 116.951, "r_y1": 181.51199999999994, "r_x2": 116.951, "r_y2": 172.96000000000004, "r_x3": 50.112, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "non-empty cells.", "orig": "non-empty cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 121.345, "r_y0": 181.51199999999994, "r_x1": 286.365, "r_y1": 181.51199999999994, "r_x2": 286.365, "r_y2": 172.96000000000004, "r_x3": 121.345, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Therefore, it was imperative to introduce", "orig": "Therefore, it was imperative to introduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 193.46699999999998, "r_x1": 286.365, "r_y1": 193.46699999999998, "r_x2": 286.365, "r_y2": 184.91499999999996, "r_x3": 50.112, "r_y3": 184.91499999999996, "coord_origin": "TOPLEFT"}, "text": "a data pre-processing procedure that generates the missing", "orig": "a data pre-processing procedure that generates the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 205.423, "r_x1": 286.365, "r_y1": 205.423, "r_x2": 286.365, "r_y2": 196.87099999999998, "r_x3": 50.112, "r_y3": 196.87099999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes out of the annotation information. This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, 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generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. The corpora to gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 50.112, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "ate the table text consists of the most frequent terms appear-", "orig": "ate the table text consists of the most frequent terms appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "ing in PubTabNet and FinTabNet together with randomly", "orig": "ing in PubTabNet and FinTabNet together with randomly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 109.519, "r_y1": 689.156, "r_x2": 109.519, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "generated text.", "orig": "generated text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.337, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 115.337, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "The first two synthetic datasets have been", "orig": "The first two synthetic datasets have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "fine-tuned to mimic the appearance of the original datasets", "orig": "fine-tuned to mimic the appearance of the original datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "but encompass more complicated table structures. The third", "orig": "but encompass more complicated table structures. 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545.115, "r_y2": 249.889, "r_x3": 308.862, "r_y3": 249.889, "coord_origin": "TOPLEFT"}, "text": "one adopts a colorful appearance with high contrast and the", "orig": "one adopts a colorful appearance with high contrast and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.39599999999996, "r_x1": 545.115, "r_y1": 270.39599999999996, "r_x2": 545.115, "r_y2": 261.84400000000005, "r_x3": 308.862, "r_y3": 261.84400000000005, "coord_origin": "TOPLEFT"}, "text": "last one contains tables with sparse content. Lastly, we have", "orig": "last one contains tables with sparse content. Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 319.153, "r_y0": 497.541, "r_x1": 509.021, "r_y1": 497.541, "r_x2": 509.021, "r_y2": 488.989, "r_x3": 319.153, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "composed of three main components, see Fig.", "orig": "composed of three main components, see Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 515.586, "r_y0": 497.541, "r_x1": 523.058, "r_y1": 497.541, "r_x2": 523.058, "r_y2": 488.989, "r_x3": 515.586, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 497.541, "r_x1": 545.115, "r_y1": 497.541, "r_x2": 545.115, "r_y2": 488.989, "r_x3": 529.623, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.452, "r_x1": 319.93, "r_y1": 521.452, "r_x2": 319.93, "r_y2": 512.9, "r_x3": 308.862, "r_y3": 512.9, "coord_origin": "TOPLEFT"}, "text": "tor", "orig": "tor", "text_direction": 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As for spanning cells, such as row", "orig": "the Cell BBox Decoder. As for spanning cells, such as row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.183, "r_x1": 483.118, "r_y1": 593.183, "r_x2": 483.118, "r_y2": 584.631, "r_x3": 308.862, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "or column span, the tag is broken down to '", "orig": "or column span, the tag is broken down to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.119, "r_y0": 593.019, "r_x1": 490.868, "r_y1": 593.019, "r_x2": 490.868, "r_y2": 584.313, "r_x3": 483.119, "r_y3": 584.313, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.868, "r_y0": 593.183, "r_x1": 545.114, "r_y1": 593.183, "r_x2": 545.114, "r_y2": 584.631, "r_x3": 490.868, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "', 'rowspan='", "orig": "', 'rowspan='", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.138, "r_x1": 545.115, "r_y1": 605.138, "r_x2": 545.115, "r_y2": 596.586, "r_x3": 308.862, "r_y3": 596.586, "coord_origin": "TOPLEFT"}, "text": "or 'colspan=', with the number of spanning cells (attribute),", "orig": "or 'colspan=', with the number of spanning cells (attribute),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.093, "r_x1": 329.644, "r_y1": 617.093, "r_x2": 329.644, "r_y2": 608.5409999999999, "r_x3": 308.862, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "and '", "orig": "and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.646, "r_y0": 616.929, "r_x1": 337.395, "r_y1": 616.929, "r_x2": 337.395, "r_y2": 608.223, "r_x3": 329.646, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.394, "r_y0": 617.093, "r_x1": 343.202, "r_y1": 617.093, "r_x2": 343.202, "r_y2": 608.5409999999999, "r_x3": 337.394, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "'.", "orig": "'.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.064, "r_y0": 617.093, "r_x1": 468.591, "r_y1": 617.093, "r_x2": 468.591, "r_y2": 608.5409999999999, "r_x3": 348.064, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "The hidden state attached to '", "orig": "The hidden state attached to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 468.595, "r_y0": 616.929, "r_x1": 476.344, "r_y1": 616.929, "r_x2": 476.344, "r_y2": 608.223, "r_x3": 468.595, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.344, "r_y0": 617.093, "r_x1": 545.116, "r_y1": 617.093, "r_x2": 545.116, "r_y2": 608.5409999999999, "r_x3": 476.344, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "' is passed to the", "orig": "' is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.048, "r_x1": 545.115, "r_y1": 629.048, "r_x2": 545.115, "r_y2": 620.496, "r_x3": 308.862, "r_y3": 620.496, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "orig": "Cell BBox Decoder. A shared feed forward network (FFN)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.0029999999999, "r_x1": 533.2, "r_y1": 641.0029999999999, "r_x2": 533.2, "r_y2": 632.451, "r_x3": 308.862, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "receives the hidden states from the Structure Decoder,", "orig": "receives the hidden states from the Structure Decoder,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.364, "r_y0": 641.0029999999999, "r_x1": 545.115, "r_y1": 641.0029999999999, "r_x2": 545.115, "r_y2": 632.451, "r_x3": 537.364, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.9590000000001, "r_x1": 545.115, "r_y1": 652.9590000000001, "r_x2": 545.115, "r_y2": 644.407, "r_x3": 308.862, "r_y3": 644.407, "coord_origin": "TOPLEFT"}, "text": "provide the final detection predictions of the bounding box", "orig": "provide the final detection predictions of the bounding box", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.914, "r_x1": 449.424, "r_y1": 664.914, "r_x2": 449.424, "r_y2": 656.362, "r_x3": 308.862, "r_y3": 656.362, "coord_origin": "TOPLEFT"}, "text": "coordinates and their classification.", "orig": "coordinates and their classification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.217, "r_x1": 431.91, "r_y1": 677.217, "r_x2": 431.91, "r_y2": 668.261, "r_x3": 320.817, "r_y3": 668.261, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network.", "orig": "CNN Backbone Network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.499, "r_y0": 677.201, "r_x1": 545.113, "r_y1": 677.201, "r_x2": 545.113, "r_y2": 668.649, "r_x3": 439.499, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "A ResNet-18 CNN is the", "orig": "A ResNet-18 CNN is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "backbone that receives the table image and encodes it as a", "orig": "backbone that receives the table image and encodes it as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "vector of predefined length. The network has been modified", "orig": "vector of predefined length. The network has been modified", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "by removing the linear and pooling layer, as we are not per-", "orig": "by removing the linear and pooling layer, as we are not per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, 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"r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.92999999999995, "r": 330.75, "b": 176.48199999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9667726755142212, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 176.48199999999997, "r_x1": 330.75, "r_y1": 176.48199999999997, "r_x2": 330.75, "r_y2": 167.92999999999995, "r_x3": 308.862, "r_y3": 167.92999999999995, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.889, "r": 545.115, "b": 258.44100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.978954017162323, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.44100000000003, "r_x1": 545.115, "r_y1": 258.44100000000003, "r_x2": 545.115, "r_y2": 249.889, "r_x3": 308.862, "r_y3": 249.889, "coord_origin": "TOPLEFT"}, "text": "one adopts a colorful appearance with high contrast and the", "orig": "one adopts a colorful appearance with high contrast and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 320.817, "t": 298.041, "r": 542.744, "b": 306.593, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 318.264, "r": 286.365, "b": 326.816, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": -1, "rgba": 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The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 342.208, "r": 545.115, "b": 350.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 439.658, "r": 286.365, "b": 448.21, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 477.034, "r": 545.115, "b": 485.586, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 62.067, "t": 632.783, "r": 286.365, "b": 641.335, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 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various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 318.264, "r": 286.365, "b": 326.816, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As it is illustrated in Fig. 2, the table 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The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 342.208, "r": 545.115, "b": 350.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Given the image of a table, TableFormer is able to pre-"}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 439.658, "r": 286.365, "b": 448.21, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Motivated by those observations we aimed at generating"}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 477.034, "r": 545.115, "b": 485.586, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We now describe in detail the proposed method, which"}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 62.067, "t": 632.783, "r": 286.365, "b": 641.335, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": -1, "rgba": {"r": 0, 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summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 318.264, "r": 286.365, "b": 326.816, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from"}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. 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As our model utilizes a transformer architecture, the", "orig": "tions). 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Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": 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The FFN consists", "orig": "network (FFN). 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As for the", "orig": "structure tokens. As for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 161.318, "r_y0": 288.585, "r_x1": 238.797, "r_y1": 288.585, "r_x2": 238.797, "r_y2": 279.99699999999996, "r_x3": 161.318, "r_y3": 279.99699999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.521, "r_y0": 288.728, "r_x1": 286.363, "r_y1": 288.728, "r_x2": 286.363, "r_y2": 280.176, "r_x3": 241.521, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "it is trained", "orig": "it is trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.683, "r_x1": 211.377, "r_y1": 300.683, "r_x2": 211.377, "r_y2": 292.131, 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In", "orig": ") to be scale invariant as explained in [25]. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.549, "r_x1": 286.365, "r_y1": 336.549, "r_x2": 286.365, "r_y2": 327.997, "r_x3": 50.112, "r_y3": 327.997, "coord_origin": "TOPLEFT"}, "text": "comparison to DETR, we do not use the Hungarian algo-", "orig": "comparison to DETR, we do not use the Hungarian algo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.504, "r_x1": 286.365, "r_y1": 348.504, "r_x2": 286.365, "r_y2": 339.952, "r_x3": 50.112, "r_y3": 339.952, "coord_origin": "TOPLEFT"}, "text": "rithm [15] to match the predicted bounding boxes with the", "orig": "rithm [15] to match the predicted bounding boxes with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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Additionally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 640.421, "r_x1": 207.033, "r_y1": 640.421, "r_x2": 207.033, "r_y2": 631.869, "r_x3": 50.112, "r_y3": 631.869, "coord_origin": "TOPLEFT"}, "text": "enforce the following input constraints:", "orig": "enforce the following input constraints:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.661, "r_y0": 663.366, "r_x1": 186.017, "r_y1": 663.366, "r_x2": 186.017, "r_y2": 654.814, "r_x3": 91.661, "r_y3": 654.814, "coord_origin": "TOPLEFT"}, "text": "Image width and height", "orig": "Image width and height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.507, "r_y0": 663.06, "r_x1": 196.256, "r_y1": 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0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "such as EDD, ours are less restrictive due to the improved", "orig": "such as EDD, ours are less restrictive due to the improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, 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As for the", "orig": "forward network of 1024, and 4 attention heads. 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545.115, "r_y2": 236.04899999999998, "r_x3": 308.862, "r_y3": 236.04899999999998, "coord_origin": "TOPLEFT"}, "text": "ages, which contain mostly lines and text, unlike the more", "orig": "ages, which contain mostly lines and text, unlike the more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.557, "r_x1": 496.169, "r_y1": 256.557, "r_x2": 496.169, "r_y2": 248.005, "r_x3": 308.862, "r_y3": 248.005, "coord_origin": "TOPLEFT"}, "text": "elaborate content present in other scopes (e.g.", "orig": "elaborate content present in other scopes (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 501.897, "r_y0": 256.557, "r_x1": 545.115, "r_y1": 256.557, "r_x2": 545.115, "r_y2": 248.005, "r_x3": 501.897, "r_y3": 248.005, "coord_origin": "TOPLEFT"}, "text": "the COCO", "orig": "the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.51199999999994, "r_x1": 342.336, "r_y1": 268.51199999999994, "r_x2": 342.336, "r_y2": 259.96000000000004, "r_x3": 308.862, "r_y3": 259.96000000000004, "coord_origin": "TOPLEFT"}, "text": "dataset).", "orig": "dataset).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.952, "r_y0": 268.51199999999994, "r_x1": 545.115, "r_y1": 268.51199999999994, "r_x2": 545.115, "r_y2": 259.96000000000004, "r_x3": 348.952, "r_y3": 259.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Moreover, we have added ResNet blocks to the", "orig": "Moreover, we have added ResNet blocks to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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545.115, "r_y2": 295.825, "r_x3": 308.862, "r_y3": 295.825, "coord_origin": "TOPLEFT"}, "text": "learned weights which would damage the other prediction", "orig": "learned weights which would damage the other prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.332, "r_x1": 545.115, "r_y1": 316.332, "r_x2": 545.115, "r_y2": 307.78, "r_x3": 308.862, "r_y3": 307.78, "coord_origin": "TOPLEFT"}, "text": "task (structure vs bounding boxes), but learn task specific", "orig": "task (structure vs bounding boxes), but learn task specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.288, "r_x1": 532.483, "r_y1": 328.288, "r_x2": 532.483, "r_y2": 319.736, "r_x3": 308.862, "r_y3": 319.736, "coord_origin": "TOPLEFT"}, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "orig": "weights instead. Lastly our dropout layers are set to 0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 357.353, "r_x1": 403.736, "r_y1": 357.353, "r_x2": 403.736, "r_y2": 348.801, "r_x3": 308.862, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": "mizers, each one for the", "orig": "mizers, each one for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.076, "r_y0": 357.21, "r_x1": 545.112, "r_y1": 357.21, "r_x2": 545.112, "r_y2": 348.622, "r_x3": 406.076, "r_y3": 348.622, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network Structure", "orig": "CNN Backbone Network Structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 503.539, "r_y0": 357.353, "r_x1": 506.03, "r_y1": 357.353, "r_x2": 506.03, "r_y2": 348.801, "r_x3": 503.539, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 369.165, "r_x1": 343.163, "r_y1": 369.165, "r_x2": 343.163, "r_y2": 360.577, "r_x3": 308.862, "r_y3": 360.577, "coord_origin": "TOPLEFT"}, "text": "Decoder", "orig": "Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 343.163, "r_y0": 369.308, "r_x1": 362.202, "r_y1": 369.308, "r_x2": 362.202, "r_y2": 360.756, "r_x3": 343.163, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.286, "r_y0": 369.165, "r_x1": 440.938, "r_y1": 369.165, "r_x2": 440.938, "r_y2": 360.577, "r_x3": 364.286, "r_y3": 360.577, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.939, "r_y0": 369.308, "r_x1": 443.43, "r_y1": 369.308, "r_x2": 443.43, "r_y2": 360.756, "r_x3": 440.939, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.379, "r_y0": 369.308, "r_x1": 545.108, "r_y1": 369.308, "r_x2": 545.108, "r_y2": 360.756, "r_x3": 446.379, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": "Taking the PubTabNet as", "orig": "Taking the PubTabNet as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 381.263, "r_x1": 545.115, "r_y1": 381.263, "r_x2": 545.115, "r_y2": 372.711, "r_x3": 308.862, "r_y3": 372.711, "coord_origin": "TOPLEFT"}, "text": "an example for our parameter set up, the initializing learn-", "orig": "an example for our parameter set up, the initializing learn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 393.219, "r_x1": 545.115, "r_y1": 393.219, "r_x2": 545.115, "r_y2": 384.667, "r_x3": 308.862, "r_y3": 384.667, "coord_origin": "TOPLEFT"}, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "orig": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 405.01, "r_x1": 314.673, "r_y1": 405.01, "r_x2": 314.673, "r_y2": 396.304, "r_x3": 308.862, "r_y3": 396.304, "coord_origin": "TOPLEFT"}, "text": "\u03bb", "orig": "\u03bb", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.658, "r_y0": 405.174, "r_x1": 329.726, "r_y1": 405.174, "r_x2": 329.726, "r_y2": 396.622, "r_x3": 318.658, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.711, "r_y0": 405.174, "r_x1": 341.462, "r_y1": 405.174, "r_x2": 341.462, "r_y2": 396.622, "r_x3": 333.711, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.447, "r_y0": 405.174, "r_x1": 360.391, "r_y1": 405.174, "r_x2": 360.391, "r_y2": 396.622, "r_x3": 345.447, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "0.5.", "orig": "0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.963, "r_y0": 405.174, "r_x1": 415.166, "r_y1": 405.174, "r_x2": 415.166, "r_y2": 396.622, "r_x3": 367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 429.084, "r_x1": 360.966, "r_y1": 429.084, "r_x2": 360.966, "r_y2": 420.532, "r_x3": 308.862, "r_y3": 420.532, "coord_origin": "TOPLEFT"}, "text": "convergence.", "orig": "convergence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, "r_y3": 461.553, "coord_origin": "TOPLEFT"}, "text": "undergoes a single forward pass through the", "orig": "undergoes a single forward pass through the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.078, "r_y0": 469.962, "r_x1": 545.111, "r_y1": 469.962, "r_x2": 545.111, "r_y2": 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This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 624.225, "r_x1": 545.115, "r_y1": 624.225, "r_x2": 545.115, "r_y2": 615.673, "r_x3": 308.862, "r_y3": 615.673, "coord_origin": "TOPLEFT"}, "text": "able datasets of different nature to prove the generalization", "orig": "able datasets of different nature to prove the generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 636.1800000000001, "r_x1": 545.115, "r_y1": 636.1800000000001, "r_x2": 545.115, "r_y2": 627.6279999999999, "r_x3": 308.862, "r_y3": 627.6279999999999, "coord_origin": "TOPLEFT"}, "text": "and effectiveness of our model. The datasets used for eval-", "orig": "and effectiveness of our model. The datasets used for eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 648.135, "r_x1": 545.115, "r_y1": 648.135, "r_x2": 545.115, "r_y2": 639.583, "r_x3": 308.862, "r_y3": 639.583, "coord_origin": "TOPLEFT"}, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "orig": "uation are the PubTabNet, FinTabNet and TableBank which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 660.091, "r_x1": 545.115, "r_y1": 660.091, "r_x2": 545.115, "r_y2": 651.539, "r_x3": 308.862, "r_y3": 651.539, "coord_origin": "TOPLEFT"}, "text": "stem from the scientific, financial and general domains re-", "orig": "stem from the scientific, financial and general domains re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.057, "r_y0": 701.111, "r_x1": 396.214, "r_y1": 701.111, "r_x2": 396.214, "r_y2": 692.559, "r_x3": 366.057, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.406, "r_y0": 701.111, "r_x1": 453.449, "r_y1": 701.111, "r_x2": 453.449, "r_y2": 692.559, "r_x3": 406.406, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Throughout", "orig": "Throughout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.311, "r_y0": 701.111, "r_x1": 471.591, "r_y1": 701.111, "r_x2": 471.591, "r_y2": 692.559, 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495.94, "r_y2": 704.515, "r_x3": 308.862, "r_y3": 704.515, "coord_origin": "TOPLEFT"}, "text": "same parameters stated in Sec. 5.1 are utilized.", "orig": "same parameters stated in Sec. 5.1 are utilized.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "runtime performance and lower memory footprint of Table-", "orig": "runtime performance and lower memory footprint of Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 116.49800000000005, "r": 545.115, "b": 125.04999999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9880930781364441, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 125.04999999999995, "r_x1": 545.115, "r_y1": 125.04999999999995, "r_x2": 545.115, "r_y2": 116.49800000000005, "r_x3": 320.817, "r_y3": 116.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The Transformer Encoder consists of two 'Transformer", "orig": "The Transformer Encoder consists of two 'Transformer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 62.067, "t": 159.89300000000003, "r": 108.154, "b": 168.44500000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 168.44500000000005, "r_x1": 108.154, "r_y1": 168.44500000000005, "r_x2": 108.154, "r_y2": 159.89300000000003, "r_x3": 62.067, "r_y3": 159.89300000000003, "coord_origin": "TOPLEFT"}, "text": "The output", "orig": "The output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 243.92200000000003, "r": 129.215, "b": 252.87800000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 320.817, "t": 336.846, "r": 545.115, "b": 345.398, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 437.643, "r": 545.115, "b": 446.195, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with 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Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 596.0029999999999, "r": 202.978, "b": 604.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 604.5550000000001, "r_x1": 202.978, "r_y1": 604.5550000000001, "r_x2": 202.978, "r_y2": 596.0029999999999, "r_x3": 62.067, "r_y3": 596.0029999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer uses ResNet-18 as the", "orig": "TableFormer uses ResNet-18 as the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 603.718, "r": 545.115, "b": 612.27, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 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"orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 84.029, 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Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 596.0029999999999, "r": 202.978, "b": 604.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 604.5550000000001, "r_x1": 202.978, "r_y1": 604.5550000000001, "r_x2": 202.978, "r_y2": 596.0029999999999, "r_x3": 62.067, "r_y3": 596.0029999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer uses ResNet-18 as the", "orig": "TableFormer uses ResNet-18 as the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer uses ResNet-18 as the"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 603.718, "r": 545.115, "b": 612.27, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": -1, "rgba": {"r": 0, "g": 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286.365, "b": 701.111, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although input constraints are used also by other methods,"}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, 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encoded image to"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "runtime performance and lower memory footprint of Table-", "orig": "runtime performance and lower memory footprint of Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "runtime performance and lower memory footprint of Table-"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 116.49800000000005, "r": 545.115, "b": 125.04999999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9880930781364441, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 125.04999999999995, "r_x1": 545.115, "r_y1": 125.04999999999995, "r_x2": 545.115, "r_y2": 116.49800000000005, "r_x3": 320.817, "r_y3": 116.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The Transformer Encoder consists of two 'Transformer", "orig": "The Transformer Encoder consists of two 'Transformer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The Transformer Encoder consists of two 'Transformer"}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 62.067, "t": 159.89300000000003, "r": 108.154, "b": 168.44500000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 168.44500000000005, "r_x1": 108.154, "r_y1": 168.44500000000005, "r_x2": 108.154, "r_y2": 159.89300000000003, "r_x3": 62.067, "r_y3": 159.89300000000003, "coord_origin": "TOPLEFT"}, "text": "The output", "orig": "The output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The output"}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 243.92200000000003, "r": 129.215, "b": 252.87800000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Loss Functions."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.817, "t": 336.846, "r": 545.115, "b": 345.398, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam opti-"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 437.643, "r": 545.115, "b": 446.195, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvi-"}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 62.067, "t": 448.279, "r": 286.365, "b": 456.831, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 456.831, "r_x1": 286.365, "r_y1": 456.831, "r_x2": 286.365, "r_y2": 448.279, "r_x3": 62.067, "r_y3": 448.279, "coord_origin": "TOPLEFT"}, "text": "The loss used to train the TableFormer can be defined as", "orig": "The loss used to train the TableFormer can be defined as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The loss used to train the TableFormer can be defined as"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 125.715, "t": 493.391, "r": 128.688, "b": 502.097, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.715, "r_y0": 502.097, "r_x1": 128.688, "r_y1": 502.097, "r_x2": 128.688, "r_y2": 493.391, "r_x3": 125.715, "r_y3": 493.391, "coord_origin": "TOPLEFT"}, "text": "l", "orig": "l", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "l"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 531.578, "r": 74.451, "b": 540.13, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 540.13, "r_x1": 74.451, "r_y1": 540.13, "r_x2": 74.451, "r_y2": 531.578, "r_x3": 50.112, "r_y3": 531.578, "coord_origin": "TOPLEFT"}, "text": "where", "orig": "where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "where"}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 555.917, "r": 171.983, "b": 566.665, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 566.665, "r_x1": 171.983, "r_y1": 566.665, "r_x2": 171.983, "r_y2": 555.917, "r_x3": 50.112, "r_y3": 555.917, "coord_origin": "TOPLEFT"}, "text": "5. Experimental Results", "orig": "5. Experimental Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 576.264, "r": 179.175, "b": 586.116, "coord_origin": "TOPLEFT"}, "confidence": 0.9538503289222717, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.116, "r_x1": 179.175, "r_y1": 586.116, "r_x2": 179.175, "r_y2": 576.264, "r_x3": 50.112, "r_y3": 576.264, "coord_origin": "TOPLEFT"}, "text": "5.1. Implementation Details", "orig": "5.1. Implementation Details", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.862, "t": 579.554, "r": 397.443, "b": 589.406, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 596.0029999999999, "r": 202.978, "b": 604.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 604.5550000000001, "r_x1": 202.978, "r_y1": 604.5550000000001, "r_x2": 202.978, "r_y2": 596.0029999999999, "r_x3": 62.067, "r_y3": 596.0029999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer uses ResNet-18 as the", "orig": "TableFormer uses ResNet-18 as the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer uses ResNet-18 as the"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 603.718, "r": 545.115, "b": 612.27, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is evaluated on three major publicly avail-"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661, "t": 654.814, "r": 186.017, "b": 663.366, "coord_origin": "TOPLEFT"}, "confidence": 0.844183623790741, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.661, "r_y0": 663.366, "r_x1": 186.017, "r_y1": 663.366, "r_x2": 186.017, "r_y2": 654.814, "r_x3": 91.661, "r_y3": 654.814, "coord_origin": "TOPLEFT"}, "text": "Image width and height", "orig": "Image width and height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Image width and height"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 320.817, "t": 680.604, "r": 545.115, "b": 689.156, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 320.817, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "We also share our baseline results on the challenging", "orig": "We also share our baseline results on the challenging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We also share our baseline results on the challenging"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 701.111, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although input constraints are used also by other methods,"}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.25300000000004, "r_x1": 167.898, "r_y1": 84.25300000000004, "r_x2": 167.898, "r_y2": 74.40099999999995, "r_x3": 50.112, "r_y3": 74.40099999999995, "coord_origin": "TOPLEFT"}, "text": "5.3. 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"r_x3": 50.112, "r_y3": 279.903, "coord_origin": "TOPLEFT"}, "text": "All the more, our model outperforms pre-trained methods.", "orig": "All the more, our model outperforms pre-trained methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.411, "r_x1": 286.365, "r_y1": 300.411, "r_x2": 286.365, "r_y2": 291.859, "r_x3": 50.112, "r_y3": 291.859, "coord_origin": "TOPLEFT"}, "text": "During the evaluation we do not apply any table filtering.", "orig": "During the evaluation we do not apply any table filtering.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.366, "r_x1": 286.365, "r_y1": 312.366, "r_x2": 286.365, "r_y2": 303.814, "r_x3": 50.112, "r_y3": 303.814, "coord_origin": "TOPLEFT"}, "text": "We also provide our baseline results on the SynthTabNet", "orig": "We also provide our baseline results on the SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.321, "r_x1": 80.269, "r_y1": 324.321, "r_x2": 80.269, "r_y2": 315.769, "r_x3": 50.112, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 85.579, "r_y0": 324.321, "r_x1": 91.666, "r_y1": 324.321, "r_x2": 91.666, "r_y2": 315.769, "r_x3": 85.579, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "It", "orig": "It", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 94.894, "r_y0": 324.321, "r_x1": 257.812, "r_y1": 324.321, "r_x2": 257.812, "r_y2": 315.769, "r_x3": 94.894, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "has been observed that large tables (e.g.", "orig": "has been observed that large tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.122, "r_y0": 324.321, "r_x1": 286.365, "r_y1": 324.321, "r_x2": 286.365, "r_y2": 315.769, "r_x3": 263.122, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "tables", "orig": "tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.276, "r_x1": 286.365, "r_y1": 336.276, "r_x2": 286.365, "r_y2": 327.724, "r_x3": 50.112, "r_y3": 327.724, "coord_origin": "TOPLEFT"}, "text": "that occupy half of the page or more) yield poor predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.203, "r_y0": 360.186, "r_x1": 134.147, "r_y1": 360.186, "r_x2": 134.147, "r_y2": 351.634, "r_x3": 119.203, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "that", "orig": "that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.952, "r_y0": 360.186, "r_x1": 286.365, "r_y1": 360.186, "r_x2": 286.365, "r_y2": 351.634, "r_x3": 137.952, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "produces downsampled images with", "orig": "produces downsampled images with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.142, "r_x1": 155.357, "r_y1": 372.142, "r_x2": 155.357, "r_y2": 363.59, "r_x3": 50.112, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "indistinguishable features.", "orig": "indistinguishable features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 396.052, "r_x1": 170.012, "r_y1": 396.052, "r_x2": 170.012, "r_y2": 387.5, "r_x3": 50.112, "r_y3": 387.5, "coord_origin": 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We make use of the", "orig": "with post-processing during inference. We make use of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 259.655, "r_y1": 689.156, "r_x2": 259.655, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "grid-like structure of tables to refine the predictions.", "orig": "grid-like structure of tables to refine the predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.65, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 263.65, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "A de-", "orig": "A de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.094, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 228.094, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "3, we evaluate", "orig": "3, we evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 322.142, "r_y1": 84.029, "r_x2": 322.142, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 325.454, "r_y0": 83.88599999999997, "r_x1": 404.567, "r_y1": 83.88599999999997, "r_x2": 404.567, "r_y2": 75.298, "r_x3": 325.454, "r_y3": 75.298, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.104, "r_y0": 84.029, "r_x1": 545.11, "r_y1": 84.029, "r_x2": 545.11, "r_y2": 75.47699999999998, "r_x3": 408.104, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "accuracy for cells with a class la-", "orig": "accuracy for cells with a class la-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "bel of 'content' only using the PASCAL VOC mAP metric", "orig": "bel of 'content' only using the PASCAL VOC mAP metric", 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"r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "are only provided. To compare the performance of our pro-", "orig": "are only provided. To compare the performance of our pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 143.80499999999995, "r_x1": 502.017, "r_y1": 143.80499999999995, "r_x2": 502.017, "r_y2": 135.25300000000004, "r_x3": 308.862, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "posed approach, we've integrated TableFormer's", "orig": "posed approach, we've integrated TableFormer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 504.473, "r_y0": 143.66200000000003, "r_x1": 545.11, "r_y1": 143.66200000000003, "r_x2": 545.11, "r_y2": 135.07399999999996, "r_x3": 504.473, "r_y3": 135.07399999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox", "orig": "Cell BBox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 155.61699999999996, "r_x1": 343.163, "r_y1": 155.61699999999996, "r_x2": 343.163, "r_y2": 147.029, "r_x3": 308.862, "r_y3": 147.029, "coord_origin": "TOPLEFT"}, "text": "Decoder", "orig": "Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.371, "r_y0": 155.76, "r_x1": 437.818, "r_y1": 155.76, "r_x2": 437.818, "r_y2": 147.20799999999997, "r_x3": 346.371, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "into EDD architecture.", "orig": "into EDD architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.371, "r_y0": 155.76, "r_x1": 545.115, "r_y1": 155.76, "r_x2": 545.115, "r_y2": 147.20799999999997, "r_x3": 442.371, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "As mentioned previously,", "orig": "As mentioned previously,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 167.71500000000003, "r_x1": 446.157, "r_y1": 167.71500000000003, "r_x2": 446.157, "r_y2": 159.163, "r_x3": 308.862, "r_y3": 159.163, "coord_origin": "TOPLEFT"}, "text": "the Structure Decoder provides the", "orig": "the Structure Decoder provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.29, "r_y0": 167.572, "r_x1": 525.042, "r_y1": 167.572, "r_x2": 525.042, "r_y2": 158.98400000000004, "r_x3": 448.29, "r_y3": 158.98400000000004, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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Tab.", "orig": "from the PDF cells rather than decoding from images. 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Additionally, we see that TableFormer is", "orig": "demonstrates TableFormer predictions on previously unseen language (Japanese). 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training set contains only English content.", "orig": "text, although the training set contains only English content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "We provide more visualizations including the intermediate", "orig": "We provide more visualizations including the intermediate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 195.217, "r_y1": 689.156, "r_x2": 195.217, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", 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Future Work & Conclusion", "orig": "6. 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"r_y2": 632.713, "r_x3": 308.862, "r_y3": 632.713, "coord_origin": "TOPLEFT"}, "text": "erated dataset that reinforces missing characteristics from", "orig": "erated dataset that reinforces missing characteristics from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.22, "r_x1": 365.858, "r_y1": 653.22, "r_x2": 365.858, "r_y2": 644.668, "r_x3": 308.862, "r_y3": 644.668, "coord_origin": "TOPLEFT"}, "text": "other datasets.", "orig": "other datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 682.847, "r_x1": 364.406, "r_y1": 682.847, "r_x2": 364.406, "r_y2": 672.099, "r_x3": 308.862, "r_y3": 672.099, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 313.345, "r_y0": 701.901, "r_x1": 323.8, "r_y1": 701.901, "r_x2": 323.8, "r_y2": 694.204, "r_x3": 313.345, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.785, "r_y0": 701.901, "r_x1": 545.109, "r_y1": 701.901, "r_x2": 545.109, "r_y2": 694.204, "r_x3": 328.785, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.781, "r_y0": 712.86, "r_x1": 545.113, "r_y1": 712.86, "r_x2": 545.113, "r_y2": 705.163, "r_x3": 328.781, "r_y3": 705.163, "coord_origin": "TOPLEFT"}, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "orig": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 21, "label": "list_item", "bbox": {"l": 53.286, "t": 78.995, "r": 499.556, "b": 86.47699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.541211724281311, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.286, "r_y0": 86.47699999999998, "r_x1": 499.556, "r_y1": 86.47699999999998, "r_x2": 499.556, "r_y2": 78.995, "r_x3": 53.286, "r_y3": 78.995, "coord_origin": "TOPLEFT"}, "text": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "orig": "a. 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Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "orig": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.812, "t": 94.58900000000006, "r": 284.346, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46452972292900085, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 102.07100000000003, "r_x1": 284.346, "r_y1": 102.07100000000003, "r_x2": 284.346, "r_y2": 94.58900000000006, "r_x3": 53.812, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Japanese language (previously unseen by TableFormer):", "orig": "Japanese language (previously unseen by TableFormer):", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.831, "t": 94.58900000000006, "r": 431.091, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4629225730895996, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 304.831, "r_y0": 102.07100000000003, "r_x1": 431.091, "r_y1": 102.07100000000003, "r_x2": 431.091, "r_y2": 94.58900000000006, "r_x3": 304.831, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Example table from FinTabNet:", "orig": "Example table from FinTabNet:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example table from FinTabNet:"}, {"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.812, "t": 208.54100000000005, "r": 385.934, "b": 216.02300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.5986505746841431, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 216.02300000000002, "r_x1": 385.934, "r_y1": 216.02300000000002, "r_x2": 385.934, "r_y2": 208.54100000000005, "r_x3": 53.812, "r_y3": 208.54100000000005, "coord_origin": "TOPLEFT"}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 380.427, "t": 292.551, "r": 549.422, "b": 298.536, "coord_origin": "TOPLEFT"}, "confidence": 0.7499915957450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.427, "r_y0": 298.536, "r_x1": 549.422, "r_y1": 298.536, "r_x2": 549.422, "r_y2": 292.551, "r_x3": 380.427, "r_y3": 292.551, "coord_origin": "TOPLEFT"}, "text": "Text is aligned to match original for ease of viewing", "orig": "Text is aligned to match original for ease of viewing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Text is aligned to match original for ease of viewing"}, {"label": "caption", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 50.112, "t": 321.146, "r": 86.864, "b": 329.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9140303134918213, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 329.698, "r_x1": 86.864, "r_y1": 329.698, "r_x2": 86.864, "r_y2": 321.146, "r_x3": 50.112, "r_y3": 321.146, "coord_origin": "TOPLEFT"}, "text": "Figure 5:", "orig": "Figure 5:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5:"}, {"label": "caption", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 62.595, "t": 458.997, "r": 98.849, "b": 467.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9153602719306946, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.595, "r_y0": 467.549, "r_x1": 98.849, "r_y1": 467.549, "r_x2": 98.849, "r_y2": 458.997, "r_x3": 62.595, "r_y3": 458.997, "coord_origin": "TOPLEFT"}, "text": "Figure 6:", "orig": "Figure 6:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 6:"}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.709, "r": 460.848, "b": 501.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 501.457, "r_x1": 460.848, "r_y1": 501.457, "r_x2": 460.848, "r_y2": 490.709, "r_x3": 308.862, "r_y3": 490.709, "coord_origin": "TOPLEFT"}, "text": "6. Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.395, "r": 163.756, "b": 501.247, "coord_origin": "TOPLEFT"}, "confidence": 0.9561247229576111, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.247, "r_x1": 163.756, "r_y1": 501.247, "r_x2": 163.756, "r_y2": 491.395, "r_x3": 50.112, "r_y3": 491.395, "coord_origin": "TOPLEFT"}, "text": "5.5. Qualitative Analysis", "orig": "5.5. Qualitative Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 513.162, "r": 545.115, "b": 521.7139999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 62.067, "t": 537.142, "r": 118.505, "b": 545.694, "coord_origin": "TOPLEFT"}, "confidence": 0.9852361679077148, 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Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "orig": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.812, "t": 94.58900000000006, "r": 284.346, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46452972292900085, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 102.07100000000003, "r_x1": 284.346, "r_y1": 102.07100000000003, "r_x2": 284.346, "r_y2": 94.58900000000006, "r_x3": 53.812, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Japanese language (previously unseen by TableFormer):", "orig": "Japanese language (previously unseen by TableFormer):", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.831, "t": 94.58900000000006, "r": 431.091, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4629225730895996, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 304.831, "r_y0": 102.07100000000003, "r_x1": 431.091, "r_y1": 102.07100000000003, "r_x2": 431.091, "r_y2": 94.58900000000006, "r_x3": 304.831, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Example table from FinTabNet:", "orig": "Example table from FinTabNet:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example table from FinTabNet:"}, {"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.812, "t": 208.54100000000005, "r": 385.934, "b": 216.02300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.5986505746841431, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 216.02300000000002, "r_x1": 385.934, "r_y1": 216.02300000000002, "r_x2": 385.934, "r_y2": 208.54100000000005, "r_x3": 53.812, "r_y3": 208.54100000000005, "coord_origin": "TOPLEFT"}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 380.427, "t": 292.551, "r": 549.422, "b": 298.536, "coord_origin": "TOPLEFT"}, "confidence": 0.7499915957450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.427, "r_y0": 298.536, "r_x1": 549.422, "r_y1": 298.536, "r_x2": 549.422, "r_y2": 292.551, "r_x3": 380.427, "r_y3": 292.551, "coord_origin": "TOPLEFT"}, "text": "Text is aligned to match original for ease of viewing", "orig": "Text is aligned to match original for ease of viewing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Text is aligned to match original for ease of viewing"}, {"label": "caption", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 50.112, "t": 321.146, "r": 86.864, "b": 329.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9140303134918213, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 329.698, "r_x1": 86.864, "r_y1": 329.698, "r_x2": 86.864, "r_y2": 321.146, "r_x3": 50.112, "r_y3": 321.146, "coord_origin": "TOPLEFT"}, "text": "Figure 5:", "orig": "Figure 5:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5:"}, {"label": "caption", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 62.595, "t": 458.997, "r": 98.849, "b": 467.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9153602719306946, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.595, "r_y0": 467.549, "r_x1": 98.849, "r_y1": 467.549, "r_x2": 98.849, "r_y2": 458.997, "r_x3": 62.595, "r_y3": 458.997, "coord_origin": "TOPLEFT"}, "text": "Figure 6:", "orig": "Figure 6:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 6:"}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.709, "r": 460.848, "b": 501.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 501.457, "r_x1": 460.848, "r_y1": 501.457, "r_x2": 460.848, "r_y2": 490.709, "r_x3": 308.862, "r_y3": 490.709, "coord_origin": "TOPLEFT"}, "text": "6. Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.395, "r": 163.756, "b": 501.247, "coord_origin": "TOPLEFT"}, "confidence": 0.9561247229576111, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.247, "r_x1": 163.756, "r_y1": 501.247, "r_x2": 163.756, "r_y2": 491.395, "r_x3": 50.112, "r_y3": 491.395, "coord_origin": "TOPLEFT"}, "text": "5.5. Qualitative Analysis", "orig": "5.5. Qualitative Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 513.162, "r": 545.115, "b": 521.7139999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 62.067, "t": 537.142, "r": 118.505, "b": 545.694, "coord_origin": "TOPLEFT"}, "confidence": 0.9852361679077148, 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Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.382, "r_y0": 244.726, "r_x1": 492.911, "r_y1": 244.726, "r_x2": 492.911, "r_y2": 236.17399999999998, "r_x3": 335.382, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "Prepare styling and content templates:", "orig": "Prepare styling and content templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.669, "r_y0": 244.726, "r_x1": 545.115, "r_y1": 244.726, "r_x2": 545.115, "r_y2": 236.17399999999998, "r_x3": 498.669, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "The styling", "orig": "The styling", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.457, "r_x1": 425.693, "r_y1": 316.457, "r_x2": 425.693, "r_y2": 307.905, "r_x3": 308.862, "r_y3": 307.905, "coord_origin": "TOPLEFT"}, "text": "PubTabNet, FinTabNet, etc.).", "orig": "PubTabNet, FinTabNet, etc.).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.692, "r_y0": 329.171, "r_x1": 435.377, "r_y1": 329.171, "r_x2": 435.377, "r_y2": 320.619, "r_x3": 332.692, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "Generate table structures:", "orig": "Generate table structures:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.342, "r_y0": 329.171, "r_x1": 545.115, "r_y1": 329.171, "r_x2": 545.115, "r_y2": 320.619, "r_x3": 439.342, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "The structure of each syn-", "orig": "The structure of each syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 341.126, "r_x1": 545.115, "r_y1": 341.126, "r_x2": 545.115, "r_y2": 332.574, "r_x3": 308.862, "r_y3": 332.574, "coord_origin": "TOPLEFT"}, "text": "thetic dataset assumes a horizontal table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, "r_y1": 353.081, "r_x2": 545.115, "r_y2": 344.529, "r_x3": 431.462, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "rows and a table body that", "orig": "rows and a table body that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 365.037, "r_x1": 545.115, "r_y1": 365.037, "r_x2": 545.115, "r_y2": 356.485, "r_x3": 308.862, "r_y3": 356.485, "coord_origin": "TOPLEFT"}, "text": "may contain a combination of row spans and column spans.", "orig": "may contain a combination of row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.992, "r_x1": 545.115, "r_y1": 376.992, "r_x2": 545.115, "r_y2": 368.44, "r_x3": 308.862, "r_y3": 368.44, "coord_origin": "TOPLEFT"}, "text": "However, spans are not allowed to cross the header - body", "orig": "However, spans are not allowed to cross the header - body", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.947, "r_x1": 348.334, "r_y1": 388.947, "r_x2": 348.334, "r_y2": 380.395, "r_x3": 308.862, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "boundary.", "orig": "boundary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 353.504, 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rows and columns, number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.857, "r_x1": 545.115, "r_y1": 412.857, "r_x2": 545.115, "r_y2": 404.305, "r_x3": 308.862, "r_y3": 404.305, "coord_origin": "TOPLEFT"}, "text": "header rows, type of spans (header only spans, row only", "orig": "header rows, type of spans (header only spans, row only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.812, "r_x1": 333.49, "r_y1": 424.812, "r_x2": 333.49, "r_y2": 416.26, "r_x3": 308.862, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "spans,", "orig": "spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.082, "r_y0": 424.812, "r_x1": 418.66, "r_y1": 424.812, "r_x2": 418.66, "r_y2": 416.26, "r_x3": 338.082, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "column only spans,", "orig": "column only spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.253, "r_y0": 424.812, "r_x1": 545.115, "r_y1": 424.812, "r_x2": 545.115, "r_y2": 416.26, "r_x3": 423.253, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "both row and column spans),", "orig": "both row and column spans),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.768, "r_x1": 545.115, "r_y1": 436.768, "r_x2": 545.115, "r_y2": 428.216, "r_x3": 308.862, "r_y3": 428.216, "coord_origin": "TOPLEFT"}, "text": "maximum span size and the ratio of the table area covered", "orig": "maximum span size and the ratio of the table area covered", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.723, "r_x1": 345.943, "r_y1": 448.723, "r_x2": 345.943, "r_y2": 440.171, "r_x3": 308.862, "r_y3": 440.171, "coord_origin": "TOPLEFT"}, "text": "by spans.", "orig": "by spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.073, "r_y0": 461.294, "r_x1": 511.864, "r_y1": 461.294, "r_x2": 511.864, "r_y2": 452.706, "r_x3": 488.073, "r_y3": 452.706, "coord_origin": "TOPLEFT"}, "text": "theme", "orig": "theme", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 511.863, "r_y0": 461.437, "r_x1": 545.108, "r_y1": 461.437, "r_x2": 545.108, "r_y2": 452.885, "r_x3": 511.863, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": ", a set of", "orig": ", a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.392, "r_x1": 545.115, "r_y1": 473.392, "r_x2": 545.115, "r_y2": 464.84, "r_x3": 308.862, "r_y3": 464.84, "coord_origin": "TOPLEFT"}, "text": "suitable content templates is chosen first. Then, this content", "orig": "suitable content templates is chosen first. Then, this content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.348, "r_x1": 545.115, "r_y1": 485.348, "r_x2": 545.115, "r_y2": 476.796, "r_x3": 308.862, "r_y3": 476.796, "coord_origin": "TOPLEFT"}, "text": "can be combined with purely random text to produce the", "orig": "can be combined with purely random text to produce the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.303, "r_x1": 379.148, "r_y1": 497.303, "r_x2": 379.148, "r_y2": 488.751, "r_x3": 308.862, "r_y3": 488.751, "coord_origin": "TOPLEFT"}, "text": "synthetic content.", "orig": "synthetic content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.217, "r_y0": 510.017, "r_x1": 434.082, "r_y1": 510.017, "r_x2": 434.082, "r_y2": 501.465, "r_x3": 334.217, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Apply styling templates:", "orig": "Apply styling templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.063, "r_y0": 510.017, "r_x1": 545.115, "r_y1": 510.017, "r_x2": 545.115, "r_y2": 501.465, "r_x3": 439.063, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Depending on the domain", "orig": "Depending on the domain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.972, "r_x1": 406.884, "r_y1": 521.972, "r_x2": 406.884, "r_y2": 513.4200000000001, "r_x3": 308.862, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "of the synthetic dataset,", "orig": "of the synthetic dataset,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.019, "r_y0": 521.972, "r_x1": 545.115, "r_y1": 521.972, "r_x2": 545.115, "r_y2": 513.4200000000001, "r_x3": 411.019, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "a set of styling templates is first", "orig": "a set of styling templates is first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.928, "r_x1": 384.299, "r_y1": 533.928, "r_x2": 384.299, "r_y2": 525.376, "r_x3": 308.862, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "manually selected.", "orig": "manually selected.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.253, "r_y0": 533.928, "r_x1": 545.115, "r_y1": 533.928, "r_x2": 545.115, "r_y2": 525.376, "r_x3": 391.253, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "Then, a style is randomly selected to", "orig": "Then, a style is randomly selected to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.883, "r_x1": 496.159, "r_y1": 545.883, "r_x2": 496.159, "r_y2": 537.331, "r_x3": 308.862, "r_y3": 537.331, "coord_origin": "TOPLEFT"}, "text": "format the appearance of the synthesized table.", "orig": "format the appearance of the synthesized table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.402, "r_y0": 558.597, "r_x1": 450.371, "r_y1": 558.597, "r_x2": 450.371, "r_y2": 550.045, "r_x3": 335.402, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "Render the complete tables:", "orig": "Render the complete tables:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.139, "r_y0": 558.597, "r_x1": 545.115, "r_y1": 558.597, "r_x2": 545.115, "r_y2": 550.045, "r_x3": 456.139, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "The synthetic table is", "orig": "The synthetic table is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 570.552, "r_x1": 334.326, "r_y1": 570.552, "r_x2": 334.326, "r_y2": 562.0, "r_x3": 308.862, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "finally", "orig": "finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.983, "r_y0": 570.552, "r_x1": 545.115, "r_y1": 570.552, "r_x2": 545.115, "r_y2": 562.0, "r_x3": 337.983, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "rendered by a web browser engine to generate the", "orig": "rendered by a web browser engine to generate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 582.5070000000001, "r_x1": 545.115, "r_y1": 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A batching technique is", "orig": "bounding boxes for each table cell. 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This happens mainly due the fol-", "orig": "original table is required. This happens mainly due the fol-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 714.063, "r_x1": 371.427, "r_y1": 714.063, "r_x2": 371.427, "r_y2": 705.511, "r_x3": 308.862, "r_y3": 705.511, "coord_origin": "TOPLEFT"}, "text": "lowing reasons:", "orig": "lowing reasons:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 121.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 426.929, "b": 171.476, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 62.067, "t": 200.18899999999996, "r": 286.365, "b": 208.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 320.817, "t": 211.50400000000002, "r": 545.115, "b": 220.05600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 320.817, "t": 236.17399999999998, "r": 328.289, "b": 244.726, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 320.817, "t": 320.619, "r": 328.289, "b": 329.171, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 391.674, "r": 236.243, "b": 400.226, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 320.817, "t": 452.885, "r": 485.758, "b": 461.437, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 320.817, "t": 501.465, "r": 328.289, "b": 510.017, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 320.817, "t": 550.045, "r": 328.289, "b": 558.597, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2909999999999, "r": 317.828, "b": 633.039, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 62.067, "t": 630.979, "r": 286.365, "b": 639.531, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 657.69, "r": 545.115, "b": 666.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. 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Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 426.929, "b": 171.476, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ances in regard to their size,"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 62.067, "t": 200.18899999999996, "r": 286.365, "b": 208.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As a first step of our data preparation process, we have"}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 320.817, "t": 211.50400000000002, "r": 545.115, "b": 220.05600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The process of generating a synthetic dataset can be de-"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 320.817, "t": 236.17399999999998, "r": 328.289, "b": 244.726, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 320.817, "t": 320.619, "r": 328.289, "b": 329.171, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 391.674, "r": 236.243, "b": 400.226, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have developed a technique that tries"}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 320.817, "t": 452.885, "r": 485.758, "b": 461.437, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Generate content: Based on the dataset"}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 320.817, "t": 501.465, "r": 328.289, "b": 510.017, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 320.817, "t": 550.045, "r": 328.289, "b": 558.597, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2909999999999, "r": 317.828, "b": 633.039, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 62.067, "t": 630.979, "r": 286.365, "b": 639.531, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 657.69, "r": 545.115, "b": 666.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although TableFormer can predict the table structure and"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 681.6, "r": 286.365, "b": 690.152, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Aiming to train and evaluate our models in a broader"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 121.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 426.929, "b": 171.476, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ances in regard to their size,"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 62.067, "t": 200.18899999999996, "r": 286.365, "b": 208.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As a first step of our data preparation process, we have"}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 320.817, "t": 211.50400000000002, "r": 545.115, "b": 220.05600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The process of generating a synthetic dataset can be de-"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 320.817, "t": 236.17399999999998, "r": 328.289, "b": 244.726, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 320.817, "t": 320.619, "r": 328.289, "b": 329.171, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 391.674, "r": 236.243, "b": 400.226, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have developed a technique that tries"}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 320.817, "t": 452.885, "r": 485.758, "b": 461.437, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Generate content: Based on the dataset"}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 320.817, "t": 501.465, "r": 328.289, "b": 510.017, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 320.817, "t": 550.045, "r": 328.289, "b": 558.597, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2909999999999, "r": 317.828, "b": 633.039, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 62.067, "t": 630.979, "r": 286.365, "b": 639.531, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 657.69, "r": 545.115, "b": 666.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although TableFormer can predict the table structure and"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. 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Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 681.6, "r": 286.365, "b": 690.152, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Aiming to train and evaluate our models in a broader"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": 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"orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 219.75199999999995, "r_x1": 286.365, "r_y1": 219.75199999999995, "r_x2": 286.365, "r_y2": 211.20000000000005, "r_x3": 70.037, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer output does not include the table cell con-", "orig": "TableFormer output does not include the table cell con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 231.707, "r_x1": 87.472, "r_y1": 231.707, "r_x2": 87.472, "r_y2": 223.15499999999997, "r_x3": 70.037, "r_y3": 223.15499999999997, "coord_origin": "TOPLEFT"}, "text": "tent.", "orig": "tent.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 252.89200000000005, "r_x1": 65.056, "r_y1": 252.89200000000005, "r_x2": 65.056, "r_y2": 244.34000000000003, "r_x3": 61.569, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 252.89200000000005, "r_x1": 286.365, "r_y1": 252.89200000000005, "r_x2": 286.365, "r_y2": 244.34000000000003, "r_x3": 70.037, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "There are occasional inaccuracies in the predictions of", "orig": "There are occasional inaccuracies in the predictions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 264.847, "r_x1": 150.415, "r_y1": 264.847, "r_x2": 150.415, "r_y2": 256.29499999999996, "r_x3": 70.037, "r_y3": 256.29499999999996, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes.", "orig": "the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 288.024, "r_x1": 286.365, "r_y1": 288.024, "r_x2": 286.365, "r_y2": 279.472, "r_x3": 62.067, "r_y3": 279.472, "coord_origin": "TOPLEFT"}, "text": "However, it is possible to mitigate those limitations by", "orig": "However, it is possible to mitigate those limitations by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 299.979, "r_x1": 286.365, "r_y1": 299.979, "r_x2": 286.365, "r_y2": 291.427, "r_x3": 50.112, "r_y3": 291.427, "coord_origin": "TOPLEFT"}, "text": "combining the TableFormer predictions with the informa-", "orig": "combining the TableFormer predictions with the informa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.934, "r_x1": 286.365, "r_y1": 311.934, "r_x2": 286.365, "r_y2": 303.382, "r_x3": 50.112, "r_y3": 303.382, "coord_origin": "TOPLEFT"}, "text": "tion already present inside a programmatic PDF document.", "orig": "tion already present inside a programmatic PDF document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.889, "r_x1": 123.387, "r_y1": 323.889, "r_x2": 123.387, "r_y2": 315.337, "r_x3": 50.112, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "More specifically,", "orig": "More specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.348, "r_y0": 323.889, "r_x1": 286.365, "r_y1": 323.889, "r_x2": 286.365, "r_y2": 315.337, "r_x3": 128.348, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "PDF documents can be seen as a se-", "orig": "PDF documents can be seen as a se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.845, "r_x1": 286.365, "r_y1": 335.845, "r_x2": 286.365, "r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 516.431, "r_x1": 69.539, "r_y1": 516.431, "r_x2": 69.539, "r_y2": 507.879, "r_x3": 62.067, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.248, "r_y0": 516.431, "r_x1": 286.365, "r_y1": 516.431, "r_x2": 286.365, "r_y2": 507.879, "r_x3": 75.248, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "Use a carefully selected IOU threshold to designate", "orig": "Use a carefully selected IOU threshold to designate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 528.387, "r_x1": 226.071, "r_y1": 528.387, "r_x2": 226.071, "r_y2": 519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. 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"TOPLEFT"}, "text": "-coordinates and cell sizes.", "orig": "-coordinates and cell sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 292.184, "r_x1": 328.289, "r_y1": 292.184, "r_x2": 328.289, "r_y2": 283.632, "r_x3": 320.817, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "7.", "orig": "7.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.144, "r_y0": 292.184, "r_x1": 545.115, "r_y1": 292.184, "r_x2": 545.115, "r_y2": 283.632, "r_x3": 332.144, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "Generate a new set of pair-wise matches between the", "orig": "Generate a new set of pair-wise matches between the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.139, "r_x1": 476.562, "r_y1": 304.139, "r_x2": 476.562, "r_y2": 295.587, "r_x3": 308.862, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "corrected bounding boxes and PDF cells.", "orig": "corrected bounding boxes and PDF cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 482.082, "r_y0": 304.139, "r_x1": 545.115, "r_y1": 304.139, "r_x2": 545.115, "r_y2": 295.587, "r_x3": 482.082, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "This time use a", "orig": "This time use a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.094, "r_x1": 545.115, "r_y1": 316.094, "r_x2": 545.115, "r_y2": 307.542, "r_x3": 308.862, "r_y3": 307.542, "coord_origin": "TOPLEFT"}, "text": "modified version of the IOU metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", 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"orig": "configurations, multiline rows, different variety of separa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 333.669, "r_x1": 175.123, "r_y1": 333.669, "r_x2": 175.123, "r_y2": 325.081, "r_x3": 50.112, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "tion lines, missing entries, etc.", "orig": "tion lines, missing entries, etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.931, "r_y0": 333.669, "r_x1": 286.365, "r_y1": 333.669, "r_x2": 286.365, "r_y2": 325.081, "r_x3": 180.931, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "As such, the correct iden-", "orig": "As such, the correct iden-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, "r_x1": 132.443, "r_y1": 369.535, "r_x2": 132.443, "r_y2": 360.947, "r_x3": 50.112, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "identification model.", "orig": "identification model.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.777, "r_y0": 369.535, "r_x1": 286.365, "r_y1": 369.535, "r_x2": 286.365, "r_y2": 360.947, "r_x3": 136.777, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "The latter improves the latest end-to-", "orig": "The latter improves the latest end-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 381.49, "r_x1": 170.4, "r_y1": 381.49, "r_x2": 170.4, "r_y2": 372.902, "r_x3": 50.112, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "end deep learning model (i.e.", "orig": "end deep learning model (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 417.355, "r_x1": 286.365, "r_y1": 417.355, "r_x2": 286.365, "r_y2": 408.767, "r_x3": 50.112, "r_y3": 408.767, "coord_origin": "TOPLEFT"}, "text": "we can obtain the content of the table-cells from program-", "orig": "we can obtain the content of the table-cells from program-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 429.311, "r_x1": 286.365, "r_y1": 429.311, "r_x2": 286.365, "r_y2": 420.723, "r_x3": 50.112, "r_y3": 420.723, "coord_origin": "TOPLEFT"}, "text": "matic PDF's directly from the PDF source and avoid the", "orig": "matic PDF's directly from the PDF source and avoid the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 441.266, "r_x1": 207.232, "r_y1": 441.266, "r_x2": 207.232, "r_y2": 432.678, "r_x3": 50.112, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. Red-annotation of bounding boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.463, "r_y0": 333.15, "r_x1": 472.474, "r_y1": 333.15, "r_x2": 472.474, "r_y2": 324.837, "r_x3": 326.463, "r_y3": 324.837, "coord_origin": "TOPLEFT"}, "text": "Blue-predictions by TableFormer", "orig": "Blue-predictions by TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 428.838, "r_x1": 491.191, "r_y1": 428.838, "r_x2": 491.191, "r_y2": 420.525, "r_x3": 315.567, "r_y3": 420.525, "coord_origin": "TOPLEFT"}, "text": "c. Structure predicted by TableFormer:", "orig": "c. 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"r_y3": 289.216, "coord_origin": "TOPLEFT"}, "text": "nately, tables come in a large variety of shapes and sizes.", "orig": "nately, tables come in a large variety of shapes and sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 309.759, "r_x1": 103.601, "r_y1": 309.759, "r_x2": 103.601, "r_y2": 301.171, "r_x3": 50.112, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "Furthermore,", "orig": "Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.393, "r_y0": 309.759, "r_x1": 124.692, "r_y1": 309.759, "r_x2": 124.692, "r_y2": 301.171, "r_x3": 108.393, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "they", "orig": "they", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 129.026, "r_y0": 309.759, "r_x1": 286.365, "r_y1": 309.759, "r_x2": 286.365, "r_y2": 301.171, "r_x3": 129.026, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "can have complex column/row-header", "orig": "can have complex column/row-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 321.714, "r_x1": 286.365, "r_y1": 321.714, "r_x2": 286.365, "r_y2": 313.126, "r_x3": 50.112, "r_y3": 313.126, "coord_origin": "TOPLEFT"}, "text": "configurations, multiline rows, different variety of separa-", "orig": "configurations, multiline rows, different variety of separa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 333.669, "r_x1": 175.123, "r_y1": 333.669, "r_x2": 175.123, "r_y2": 325.081, "r_x3": 50.112, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "tion lines, missing entries, etc.", "orig": "tion lines, missing entries, etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.931, "r_y0": 333.669, "r_x1": 286.365, "r_y1": 333.669, "r_x2": 286.365, "r_y2": 325.081, "r_x3": 180.931, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "As such, the correct iden-", "orig": "As such, the correct iden-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 345.624, "r_x1": 85.539, "r_y1": 345.624, "r_x2": 85.539, "r_y2": 337.036, "r_x3": 50.112, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "tification", "orig": "tification", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 89.942, 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"rect": {"r_x0": 181.947, "r_y0": 345.624, "r_x1": 286.365, "r_y1": 345.624, "r_x2": 286.365, "r_y2": 337.036, "r_x3": 181.947, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "from an image is a non-", "orig": "from an image is a non-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 357.58, "r_x1": 95.791, "r_y1": 357.58, "r_x2": 95.791, "r_y2": 348.992, "r_x3": 50.112, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "trivial task.", "orig": "trivial task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, "r_x1": 132.443, "r_y1": 369.535, "r_x2": 132.443, "r_y2": 360.947, "r_x3": 50.112, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "identification model.", "orig": "identification model.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.777, "r_y0": 369.535, "r_x1": 286.365, "r_y1": 369.535, "r_x2": 286.365, "r_y2": 360.947, "r_x3": 136.777, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "The latter improves the latest end-to-", "orig": "The latter improves the latest end-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 381.49, "r_x1": 170.4, "r_y1": 381.49, "r_x2": 170.4, "r_y2": 372.902, "r_x3": 50.112, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "end deep learning model (i.e.", "orig": "end deep learning model (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 417.355, "r_x1": 286.365, "r_y1": 417.355, "r_x2": 286.365, "r_y2": 408.767, "r_x3": 50.112, "r_y3": 408.767, "coord_origin": "TOPLEFT"}, "text": "we can obtain the content of the table-cells from program-", "orig": "we can obtain the content of the table-cells from program-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 429.311, "r_x1": 286.365, "r_y1": 429.311, "r_x2": 286.365, "r_y2": 420.723, "r_x3": 50.112, "r_y3": 420.723, "coord_origin": "TOPLEFT"}, "text": "matic PDF's directly from the PDF source and avoid the", "orig": "matic PDF's directly from the PDF source and avoid the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 441.266, "r_x1": 207.232, "r_y1": 441.266, "r_x2": 207.232, "r_y2": 432.678, "r_x3": 50.112, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.112, "t": 539.943, "r": 126.948, "b": 550.691, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 561.052, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 315.567, "t": 218.34900000000005, "r": 408.441, "b": 226.66200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.567, "t": 314.037, "r": 486.402, "b": 333.15, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. 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non-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 357.58, "r_x1": 95.791, "r_y1": 357.58, "r_x2": 95.791, "r_y2": 348.992, "r_x3": 50.112, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "trivial task.", "orig": "trivial task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, "r_x1": 132.443, "r_y1": 369.535, "r_x2": 132.443, 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false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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"coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.112, "t": 539.943, "r": 126.948, "b": 550.691, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 561.052, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.567, "t": 218.34900000000005, "r": 408.441, "b": 226.66200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.567, "t": 314.037, "r": 486.402, "b": 333.15, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. Red-annotation of bounding boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.463, "r_y0": 333.15, "r_x1": 472.474, "r_y1": 333.15, "r_x2": 472.474, "r_y2": 324.837, "r_x3": 326.463, "r_y3": 324.837, "coord_origin": "TOPLEFT"}, "text": "Blue-predictions by TableFormer", "orig": "Blue-predictions by TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.567, "t": 420.525, "r": 491.191, "b": 428.838, "coord_origin": "TOPLEFT"}, "confidence": 0.5501358509063721, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 428.838, "r_x1": 491.191, "r_y1": 428.838, "r_x2": 491.191, "r_y2": 420.525, "r_x3": 315.567, "r_y3": 420.525, "coord_origin": "TOPLEFT"}, "text": "c. Structure predicted by TableFormer:", "orig": "c. Structure predicted by TableFormer:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78173828125, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742761611938477, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.148, "r_y0": 351.356, "r_x1": 412.54, "r_y1": 351.356, "r_x2": 412.54, "r_y2": 343.601, "r_x3": 408.148, "r_y3": 343.601, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 356.11, "r_y0": 350.099, "r_x1": 360.503, "r_y1": 350.099, "r_x2": 360.503, "r_y2": 342.345, "r_x3": 356.11, "r_y3": 342.345, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, 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finding the structure of a given table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.961, "r_x1": 375.552, "r_y1": 664.961, "r_x2": 375.552, "r_y2": 656.409, "r_x3": 308.862, "r_y3": 656.409, "coord_origin": "TOPLEFT"}, "text": "in the document.", "orig": "in the document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.649, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 320.817, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "The first problem is called table-location and has been", "orig": "The first problem is called table-location and has been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "orig": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 474.261, "r_y1": 701.111, "r_x2": 474.261, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "of-the-art object-detection networks (e.g.", "orig": "of-the-art object-detection networks (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.342, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 479.342, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "YOLO and later", "orig": "YOLO and later", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 395.796, "r_y1": 713.066, "r_x2": 395.796, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "on Mask-RCNN [9]).", "orig": "on Mask-RCNN [9]).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.046, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 401.046, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "For all practical purposes, it can be", "orig": "For all practical purposes, it can be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). 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image is a non-", "orig": "from an image is a non-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 357.58, "r_x1": 95.791, "r_y1": 357.58, "r_x2": 95.791, "r_y2": 348.992, "r_x3": 50.112, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "trivial task.", "orig": "trivial task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 417.355, "r_x1": 286.365, "r_y1": 417.355, "r_x2": 286.365, "r_y2": 408.767, "r_x3": 50.112, "r_y3": 408.767, "coord_origin": "TOPLEFT"}, "text": "we can obtain the content of the table-cells from program-", "orig": "we can obtain the content of the table-cells from program-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 429.311, "r_x1": 286.365, "r_y1": 429.311, "r_x2": 286.365, "r_y2": 420.723, "r_x3": 50.112, "r_y3": 420.723, "coord_origin": "TOPLEFT"}, "text": "matic PDF's directly from the PDF source and avoid the", "orig": "matic PDF's directly from the PDF source and avoid the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 441.266, "r_x1": 207.232, "r_y1": 441.266, "r_x2": 207.232, "r_y2": 432.678, "r_x3": 50.112, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.112, "t": 539.943, "r": 126.948, "b": 550.691, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 561.052, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.567, "t": 218.34900000000005, "r": 408.441, "b": 226.66200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.567, "t": 314.037, "r": 486.402, "b": 333.15, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. Red-annotation of bounding boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.463, "r_y0": 333.15, "r_x1": 472.474, "r_y1": 333.15, "r_x2": 472.474, "r_y2": 324.837, "r_x3": 326.463, "r_y3": 324.837, "coord_origin": "TOPLEFT"}, "text": "Blue-predictions by TableFormer", "orig": "Blue-predictions by TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.567, "t": 420.525, "r": 491.191, "b": 428.838, "coord_origin": "TOPLEFT"}, "confidence": 0.5501358509063721, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 428.838, "r_x1": 491.191, "r_y1": 428.838, "r_x2": 491.191, "r_y2": 420.525, "r_x3": 315.567, "r_y3": 420.525, "coord_origin": "TOPLEFT"}, "text": "c. Structure predicted by TableFormer:", "orig": "c. Structure predicted by TableFormer:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78173828125, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742761611938477, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.148, "r_y0": 351.356, "r_x1": 412.54, "r_y1": 351.356, "r_x2": 412.54, "r_y2": 343.601, "r_x3": 408.148, "r_y3": 343.601, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 356.11, "r_y0": 350.099, "r_x1": 360.503, "r_y1": 350.099, "r_x2": 360.503, "r_y2": 342.345, "r_x3": 356.11, "r_y3": 342.345, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, 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finding the structure of a given table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.961, "r_x1": 375.552, "r_y1": 664.961, "r_x2": 375.552, "r_y2": 656.409, "r_x3": 308.862, "r_y3": 656.409, "coord_origin": "TOPLEFT"}, "text": "in the document.", "orig": "in the document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.649, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 320.817, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "The first problem is called table-location and has been", "orig": "The first problem is called table-location and has been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "orig": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 474.261, "r_y1": 701.111, "r_x2": 474.261, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "of-the-art object-detection networks (e.g.", "orig": "of-the-art object-detection networks (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.342, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 479.342, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "YOLO and later", "orig": "YOLO and later", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 395.796, "r_y1": 713.066, "r_x2": 395.796, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "on Mask-RCNN [9]).", "orig": "on Mask-RCNN [9]).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.046, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 401.046, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "For all practical purposes, it can be", "orig": "For all practical purposes, it can be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8045893311500549, "cells": [{"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.34, "t": 160.0, "r": 36.34, "b": 560.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8773143887519836, "cells": [{"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 560.0, "r_x1": 36.34, "r_y1": 160.0, "r_x2": 18.34, "r_y2": 160.0, "r_x3": 18.34, "r_y3": 560.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "orig": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "considered as a solved problem, given enough ground-truth", "orig": "considered as a solved problem, given enough ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 112.647, "r_y1": 95.98400000000004, "r_x2": 112.647, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "data to train on.", "orig": "data to train on.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 120.34699999999998, "r_x1": 74.75, "r_y1": 120.34699999999998, "r_x2": 74.75, "r_y2": 111.79499999999996, "r_x3": 50.112, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "sition.", "orig": "sition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.335, "r_y0": 120.34699999999998, "r_x1": 286.365, "r_y1": 120.34699999999998, "r_x2": 286.365, "r_y2": 111.79499999999996, "r_x3": 81.335, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "The latter is a long standing problem in the com-", "orig": "The latter is a long standing problem in the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 132.30200000000002, "r_x1": 235.117, "r_y1": 132.30200000000002, "r_x2": 235.117, "r_y2": 123.75, "r_x3": 50.112, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "munity of document understanding [6, 4, 14].", "orig": "munity of document understanding [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.079, "r_y0": 132.30200000000002, "r_x1": 286.365, "r_y1": 132.30200000000002, "r_x2": 286.365, "r_y2": 123.75, "r_x3": 240.079, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "Contrary to", "orig": "Contrary to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 144.25699999999995, "r_x1": 286.365, "r_y1": 144.25699999999995, "r_x2": 286.365, "r_y2": 135.70500000000004, "r_x3": 50.112, "r_y3": 135.70500000000004, "coord_origin": "TOPLEFT"}, "text": "the table-location problem, there are no commonly used ap-", "orig": "the table-location problem, there are no commonly used ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 156.21299999999997, "r_x1": 286.365, "r_y1": 156.21299999999997, "r_x2": 286.365, "r_y2": 147.66099999999994, "r_x3": 50.112, "r_y3": 147.66099999999994, "coord_origin": "TOPLEFT"}, "text": "proaches that can easily be re-purposed to solve this prob-", "orig": "proaches that can easily be re-purposed to solve this prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 168.168, "r_x1": 286.365, "r_y1": 168.168, "r_x2": 286.365, "r_y2": 159.61599999999999, "r_x3": 50.112, "r_y3": 159.61599999999999, "coord_origin": "TOPLEFT"}, "text": "lem. Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. 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structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. 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Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 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{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.798, "r_y0": 342.39, "r_x1": 545.115, "r_y1": 342.39, "r_x2": 545.115, "r_y2": 333.838, "r_x3": 453.798, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "deep neural networks,", "orig": "deep neural networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 354.345, "r_x1": 545.115, "r_y1": 354.345, "r_x2": 545.115, "r_y2": 345.793, "r_x3": 308.862, "r_y3": 345.793, "coord_origin": "TOPLEFT"}, "text": "the community relied heavily on heuristic and/or statistical", "orig": "the community relied heavily on heuristic and/or statistical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 366.3, "r_x1": 545.115, "r_y1": 366.3, "r_x2": 545.115, "r_y2": 357.748, "r_x3": 308.862, "r_y3": 357.748, "coord_origin": "TOPLEFT"}, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "orig": "methods to do table structure identification [3, 7, 11, 5, 13,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 378.256, "r_x1": 324.633, "r_y1": 378.256, "r_x2": 324.633, "r_y2": 369.704, "r_x3": 308.862, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "28].", "orig": "28].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.634, "r_y0": 378.256, "r_x1": 545.115, "r_y1": 378.256, "r_x2": 545.115, "r_y2": 369.704, "r_x3": 329.634, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "Although such methods work well on constrained ta-", "orig": "Although such methods work well on constrained ta-", "text_direction": 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465.411, "coord_origin": "TOPLEFT"}, "text": "starting", "orig": "starting", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 473.713, "r_y0": 473.963, "r_x1": 545.115, "r_y1": 473.963, "r_x2": 545.115, "r_y2": 465.411, "r_x3": 473.713, "r_y3": 465.411, "coord_origin": "TOPLEFT"}, "text": "from an encoded", "orig": "from an encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.918, "r_x1": 335.701, "r_y1": 485.918, "r_x2": 335.701, "r_y2": 477.366, "r_x3": 308.862, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "image.", "orig": "image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.853, "r_y0": 485.918, "r_x1": 411.009, "r_y1": 485.918, "r_x2": 411.009, "r_y2": 477.366, "r_x3": 345.853, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "Such sequences", "orig": "Such sequences", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.85, "r_y0": 485.918, "r_x1": 424.149, "r_y1": 485.918, "r_x2": 424.149, "r_y2": 477.366, "r_x3": 415.85, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 429.001, "r_y0": 485.918, "r_x1": 454.914, "r_y1": 485.918, "r_x2": 454.914, "r_y2": 477.366, "r_x3": 429.001, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "tokens", "orig": "tokens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 459.756, "r_y0": 485.918, "r_x1": 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497.873, "r_x1": 459.218, "r_y1": 497.873, "r_x2": 459.218, "r_y2": 489.321, "r_x3": 308.862, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "tags [37, 17] or LaTeX symbols[10].", "orig": "tags [37, 17] or LaTeX symbols[10].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.115, "r_y0": 497.873, "r_x1": 545.115, "r_y1": 497.873, "r_x2": 545.115, "r_y2": 489.321, "r_x3": 465.115, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "The choice of sym-", "orig": "The choice of sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.828, "r_x1": 545.115, "r_y1": 509.828, "r_x2": 545.115, "r_y2": 501.276, "r_x3": 308.862, "r_y3": 501.276, "coord_origin": "TOPLEFT"}, "text": "bols is ultimately not very important, since one can be trans-", "orig": "bols is ultimately not very important, since one can be trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.784, "r_x1": 396.692, "r_y1": 521.784, "r_x2": 396.692, "r_y2": 513.232, "r_x3": 308.862, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "formed into the other.", "orig": "formed into the other.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.265, "r_y0": 521.784, "r_x1": 545.115, "r_y1": 521.784, "r_x2": 545.115, "r_y2": 513.232, "r_x3": 401.265, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "There are however subtle variations", "orig": "There are however subtle variations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.739, "r_x1": 433.484, "r_y1": 533.739, "r_x2": 433.484, "r_y2": 525.187, "r_x3": 308.862, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "in the Image-to-Text networks.", "orig": "in the Image-to-Text networks.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 438.187, "r_y0": 533.739, "r_x1": 545.115, "r_y1": 533.739, "r_x2": 545.115, "r_y2": 525.187, "r_x3": 438.187, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "The easiest network archi-", "orig": "The easiest network archi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.694, "r_x1": 420.941, "r_y1": 545.694, "r_x2": 420.941, "r_y2": 537.142, "r_x3": 308.862, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "tectures are 'image-encoder", "orig": "tectures are 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.595, "r_y0": 545.388, "r_x1": 433.558, "r_y1": 545.388, "r_x2": 433.558, "r_y2": 536.8240000000001, "r_x3": 423.595, "r_y3": 536.8240000000001, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.212, "r_y0": 545.694, "r_x1": 545.113, "r_y1": 545.694, "r_x2": 545.113, "r_y2": 537.142, "r_x3": 436.212, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "text-decoder' (IETD), sim-", "orig": "text-decoder' (IETD), sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.649, "r_x1": 545.115, "r_y1": 557.649, "r_x2": 545.115, "r_y2": 549.097, "r_x3": 308.862, "r_y3": 549.097, "coord_origin": "TOPLEFT"}, "text": "ilar to network architectures that try to provide captions to", "orig": "ilar to network architectures that try to provide captions to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.604, "r_x1": 545.115, "r_y1": 569.604, "r_x2": 545.115, "r_y2": 561.052, "r_x3": 308.862, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "images [32]. In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.045, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 364.045, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "to generate the textual content of each table", "orig": "to generate the textual content of each table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 325.739, "r_y1": 701.111, "r_x2": 325.739, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.746, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 331.746, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "The network architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 95.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "considered as a solved problem, given enough ground-truth", "orig": "considered as a solved problem, given enough ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 112.647, "r_y1": 95.98400000000004, "r_x2": 112.647, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "data to train on.", "orig": "data to train on.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.84000000000003, "r": 286.365, "b": 227.94399999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 120.34699999999998, "r_x1": 74.75, "r_y1": 120.34699999999998, "r_x2": 74.75, "r_y2": 111.79499999999996, "r_x3": 50.112, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "sition.", "orig": "sition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.335, "r_y0": 120.34699999999998, "r_x1": 286.365, "r_y1": 120.34699999999998, "r_x2": 286.365, "r_y2": 111.79499999999996, "r_x3": 81.335, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "The latter is a long standing problem in the com-", "orig": "The latter is a long standing problem in the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 132.30200000000002, "r_x1": 235.117, "r_y1": 132.30200000000002, "r_x2": 235.117, "r_y2": 123.75, "r_x3": 50.112, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "munity of document understanding [6, 4, 14].", "orig": "munity of document understanding [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.079, "r_y0": 132.30200000000002, "r_x1": 286.365, "r_y1": 132.30200000000002, "r_x2": 286.365, "r_y2": 123.75, "r_x3": 240.079, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "Contrary to", "orig": "Contrary to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 144.25699999999995, "r_x1": 286.365, "r_y1": 144.25699999999995, "r_x2": 286.365, "r_y2": 135.70500000000004, "r_x3": 50.112, "r_y3": 135.70500000000004, "coord_origin": "TOPLEFT"}, "text": "the table-location problem, there are no commonly used ap-", "orig": "the table-location problem, there are no commonly used ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 156.21299999999997, "r_x1": 286.365, "r_y1": 156.21299999999997, "r_x2": 286.365, "r_y2": 147.66099999999994, "r_x3": 50.112, "r_y3": 147.66099999999994, "coord_origin": "TOPLEFT"}, "text": "proaches that can easily be re-purposed to solve this prob-", "orig": "proaches that can easily be re-purposed to solve this prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 168.168, "r_x1": 286.365, "r_y1": 168.168, "r_x2": 286.365, "r_y2": 159.61599999999999, "r_x3": 50.112, "r_y3": 159.61599999999999, "coord_origin": "TOPLEFT"}, "text": "lem. Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.79999999999995, "r": 286.365, "b": 371.859, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.172, "r_x1": 286.365, "r_y1": 288.172, "r_x2": 286.365, "r_y2": 279.62, "r_x3": 50.112, "r_y3": 279.62, "coord_origin": "TOPLEFT"}, "text": "we can obtain the structure of any table, irregardless of the", "orig": "we can obtain the structure of any table, irregardless of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.128, "r_x1": 88.568, "r_y1": 300.128, "r_x2": 88.568, "r_y2": 291.576, "r_x3": 50.112, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "language.", "orig": "language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.502, "r_y0": 300.128, "r_x1": 286.365, "r_y1": 300.128, "r_x2": 286.365, "r_y2": 291.576, "r_x3": 95.502, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "Second, we want our algorithm to leverage as", "orig": "Second, we want our algorithm to leverage as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.083, "r_x1": 286.365, "r_y1": 312.083, "r_x2": 286.365, "r_y2": 303.531, "r_x3": 50.112, "r_y3": 303.531, "coord_origin": "TOPLEFT"}, "text": "much data as possible from the original PDF document. For", "orig": "much data as possible from the original PDF document. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.038, "r_x1": 286.365, "r_y1": 324.038, "r_x2": 286.365, "r_y2": 315.486, "r_x3": 50.112, "r_y3": 315.486, "coord_origin": "TOPLEFT"}, "text": "programmatic PDF documents, the text-cells can often be", "orig": "programmatic PDF documents, the text-cells can often be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.993, "r_x1": 286.365, "r_y1": 335.993, "r_x2": 286.365, "r_y2": 327.441, "r_x3": 50.112, "r_y3": 327.441, "coord_origin": "TOPLEFT"}, "text": "extracted much faster and with higher accuracy compared", "orig": "extracted much faster and with higher accuracy compared", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.948, "r_x1": 286.365, "r_y1": 347.948, "r_x2": 286.365, "r_y2": 339.396, "r_x3": 50.112, "r_y3": 339.396, "coord_origin": "TOPLEFT"}, "text": "to OCR methods. Last but not least, we want to have a di-", "orig": "to OCR methods. 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"coord_origin": "TOPLEFT"}, "text": "PubTabNet", "orig": "PubTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.277, "r_y0": 614.2239999999999, "r_x1": 286.365, "r_y1": 614.2239999999999, "r_x2": 286.365, "r_y2": 605.672, "r_x3": 267.277, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "[37],", "orig": "[37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 626.179, "r_x1": 135.521, "r_y1": 626.179, "r_x2": 135.521, "r_y2": 617.627, "r_x3": 70.037, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36],", "orig": "FinTabNet [36],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.925, "r_y0": 626.179, "r_x1": 286.365, "r_y1": 626.179, "r_x2": 286.365, "r_y2": 617.627, "r_x3": 139.925, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "and TableBank [17] with generated", "orig": "and TableBank [17] with generated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 638.135, "r_x1": 198.056, "r_y1": 638.135, "r_x2": 198.056, "r_y2": 629.583, "r_x3": 70.037, "r_y3": 629.583, "coord_origin": "TOPLEFT"}, "text": "ground-truth for reproducibility.", "orig": "ground-truth for reproducibility.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 650.867, "r": 286.365, "b": 695.284, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 659.419, "r_x1": 204.452, "r_y1": 659.419, "r_x2": 204.452, "r_y2": 650.867, "r_x3": 62.067, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.833, "r_y0": 671.374, "r_x1": 286.365, "r_y1": 671.374, "r_x2": 286.365, "r_y2": 662.822, "r_x3": 246.833, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "In Sec. 3,", "orig": "In Sec. 3,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 683.329, "r_x1": 227.396, "r_y1": 683.329, "r_x2": 227.396, "r_y2": 674.777, "r_x3": 50.112, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "we describe the datasets on which we train.", "orig": "we describe the datasets on which we train.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 232.467, "r_y0": 683.329, "r_x1": 286.365, "r_y1": 683.329, "r_x2": 286.365, "r_y2": 674.777, "r_x3": 232.467, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "In Sec. 4, we", "orig": "In Sec. 4, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 695.284, "r_x1": 286.365, "r_y1": 695.284, "r_x2": 286.365, "r_y2": 686.732, "r_x3": 50.112, "r_y3": 686.732, "coord_origin": "TOPLEFT"}, "text": "introduce the TableFormer model-architecture and describe", "orig": "introduce the TableFormer model-architecture and describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.971, "t": 704.296, "r": 183.731, "b": 712.653, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 60.971, "r_y0": 709.427, "r_x1": 63.96, "r_y1": 709.427, "r_x2": 63.96, "r_y2": 704.296, "r_x3": 60.971, "r_y3": 704.296, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.458, "r_y0": 712.653, "r_x1": 183.731, "r_y1": 712.653, "r_x2": 183.731, "r_y2": 705.811, "r_x3": 64.458, "r_y3": 705.811, "coord_origin": "TOPLEFT"}, "text": "https://github.com/IBM/SynthTabNet", "orig": "https://github.com/IBM/SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.49, "r": 545.115, "b": 330.369, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", 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492.682, "r_y0": 485.918, "r_x1": 545.115, "r_y1": 485.918, "r_x2": 545.115, "r_y2": 477.366, "r_x3": 492.682, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "HTML table", "orig": "HTML table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.873, "r_x1": 459.218, "r_y1": 497.873, "r_x2": 459.218, "r_y2": 489.321, "r_x3": 308.862, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "tags [37, 17] or LaTeX symbols[10].", "orig": "tags [37, 17] or LaTeX symbols[10].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.115, "r_y0": 497.873, "r_x1": 545.115, "r_y1": 497.873, "r_x2": 545.115, "r_y2": 489.321, "r_x3": 465.115, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "The choice of sym-", "orig": "The choice of sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.828, "r_x1": 545.115, "r_y1": 509.828, "r_x2": 545.115, "r_y2": 501.276, "r_x3": 308.862, "r_y3": 501.276, "coord_origin": "TOPLEFT"}, "text": "bols is ultimately not very important, since one can be trans-", "orig": "bols is ultimately not very important, since one can be trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.784, "r_x1": 396.692, "r_y1": 521.784, "r_x2": 396.692, "r_y2": 513.232, "r_x3": 308.862, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "formed into the other.", "orig": "formed into the other.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.265, "r_y0": 521.784, "r_x1": 545.115, "r_y1": 521.784, "r_x2": 545.115, "r_y2": 513.232, "r_x3": 401.265, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "There are however subtle variations", "orig": "There are however subtle variations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.739, "r_x1": 433.484, "r_y1": 533.739, "r_x2": 433.484, "r_y2": 525.187, "r_x3": 308.862, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "in the Image-to-Text networks.", "orig": "in the Image-to-Text networks.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 438.187, "r_y0": 533.739, "r_x1": 545.115, "r_y1": 533.739, "r_x2": 545.115, "r_y2": 525.187, "r_x3": 438.187, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "The easiest network archi-", "orig": "The easiest network archi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.694, "r_x1": 420.941, "r_y1": 545.694, "r_x2": 420.941, "r_y2": 537.142, "r_x3": 308.862, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "tectures are 'image-encoder", "orig": "tectures are 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.595, "r_y0": 545.388, "r_x1": 433.558, "r_y1": 545.388, "r_x2": 433.558, "r_y2": 536.8240000000001, "r_x3": 423.595, "r_y3": 536.8240000000001, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.212, "r_y0": 545.694, "r_x1": 545.113, "r_y1": 545.694, "r_x2": 545.113, "r_y2": 537.142, "r_x3": 436.212, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "text-decoder' (IETD), sim-", "orig": "text-decoder' (IETD), sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.649, "r_x1": 545.115, "r_y1": 557.649, "r_x2": 545.115, "r_y2": 549.097, "r_x3": 308.862, "r_y3": 549.097, "coord_origin": "TOPLEFT"}, "text": "ilar to network architectures that try to provide captions to", "orig": "ilar to network architectures that try to provide captions to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.604, "r_x1": 545.115, "r_y1": 569.604, "r_x2": 545.115, "r_y2": 561.052, "r_x3": 308.862, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "images [32]. In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, 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architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 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99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 120.34699999999998, "r_x1": 74.75, "r_y1": 120.34699999999998, "r_x2": 74.75, "r_y2": 111.79499999999996, "r_x3": 50.112, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "sition.", "orig": "sition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.335, "r_y0": 120.34699999999998, "r_x1": 286.365, "r_y1": 120.34699999999998, "r_x2": 286.365, "r_y2": 111.79499999999996, "r_x3": 81.335, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "The latter is a long standing problem in the com-", "orig": "The latter is a long 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144.25699999999995, "r_x1": 286.365, "r_y1": 144.25699999999995, "r_x2": 286.365, "r_y2": 135.70500000000004, "r_x3": 50.112, "r_y3": 135.70500000000004, "coord_origin": "TOPLEFT"}, "text": "the table-location problem, there are no commonly used ap-", "orig": "the table-location problem, there are no commonly used ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 156.21299999999997, "r_x1": 286.365, "r_y1": 156.21299999999997, "r_x2": 286.365, "r_y2": 147.66099999999994, "r_x3": 50.112, "r_y3": 147.66099999999994, "coord_origin": "TOPLEFT"}, "text": "proaches that can easily be re-purposed to solve this prob-", "orig": "proaches that can easily be re-purposed to solve this prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 168.168, "r_x1": 286.365, 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Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.79999999999995, "r": 286.365, "b": 371.859, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.172, "r_x1": 286.365, "r_y1": 288.172, "r_x2": 286.365, "r_y2": 279.62, "r_x3": 50.112, "r_y3": 279.62, "coord_origin": "TOPLEFT"}, "text": "we can obtain the structure of any table, irregardless of the", "orig": "we can obtain the structure of any table, irregardless of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.128, "r_x1": 88.568, "r_y1": 300.128, "r_x2": 88.568, "r_y2": 291.576, "r_x3": 50.112, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "language.", "orig": "language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.502, "r_y0": 300.128, "r_x1": 286.365, "r_y1": 300.128, "r_x2": 286.365, "r_y2": 291.576, "r_x3": 95.502, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "Second, we want our algorithm to leverage as", "orig": "Second, we want our algorithm to leverage as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.083, "r_x1": 286.365, "r_y1": 312.083, "r_x2": 286.365, "r_y2": 303.531, "r_x3": 50.112, "r_y3": 303.531, "coord_origin": "TOPLEFT"}, "text": "much data as possible from the original PDF document. For", "orig": "much data as possible from the original PDF document. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.038, "r_x1": 286.365, "r_y1": 324.038, "r_x2": 286.365, "r_y2": 315.486, "r_x3": 50.112, "r_y3": 315.486, "coord_origin": "TOPLEFT"}, "text": "programmatic PDF documents, the text-cells can often be", "orig": "programmatic PDF documents, the text-cells can often be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.993, "r_x1": 286.365, "r_y1": 335.993, "r_x2": 286.365, "r_y2": 327.441, "r_x3": 50.112, "r_y3": 327.441, "coord_origin": "TOPLEFT"}, "text": "extracted much faster and with higher accuracy compared", "orig": "extracted much faster and with higher accuracy compared", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.948, "r_x1": 286.365, "r_y1": 347.948, "r_x2": 286.365, "r_y2": 339.396, "r_x3": 50.112, "r_y3": 339.396, "coord_origin": "TOPLEFT"}, "text": "to OCR methods. Last but not least, we want to have a di-", "orig": "to OCR methods. Last but not least, we want to have a di-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.904, "r_x1": 286.365, "r_y1": 359.904, "r_x2": 286.365, "r_y2": 351.352, "r_x3": 50.112, "r_y3": 351.352, "coord_origin": "TOPLEFT"}, "text": "rect link between the table-cell and its bounding box in the", "orig": "rect link between the table-cell and its bounding box in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.859, "r_x1": 76.951, "r_y1": 371.859, "r_x2": 76.951, "r_y2": 363.307, "r_x3": 50.112, "r_y3": 363.307, "coord_origin": "TOPLEFT"}, "text": "image.", "orig": "image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.715, "r": 286.367, "b": 432.087, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 396.222, "r_x1": 120.986, "r_y1": 396.222, "r_x2": 120.986, "r_y2": 387.67, "r_x3": 50.112, "r_y3": 387.67, "coord_origin": "TOPLEFT"}, "text": "new model called", "orig": "new model called", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.901, "r_y0": 396.238, "r_x1": 179.731, "r_y1": 396.238, "r_x2": 179.731, "r_y2": 387.282, "r_x3": 123.901, "r_y3": 387.282, "coord_origin": "TOPLEFT"}, "text": "TableFormer", "orig": "TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.646, "r_y0": 396.222, "r_x1": 286.367, "r_y1": 396.222, "r_x2": 286.367, "r_y2": 387.67, "r_x3": 182.646, "r_y3": 387.67, "coord_origin": "TOPLEFT"}, "text": "and a synthetically gener-", "orig": "and a synthetically gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 408.177, "r_x1": 181.758, "r_y1": 408.177, "r_x2": 181.758, "r_y2": 399.625, "r_x3": 50.112, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "ated table structure dataset called", "orig": "ated table structure dataset called", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.104, "r_y0": 408.193, "r_x1": 240.203, "r_y1": 408.193, "r_x2": 240.203, "r_y2": 399.237, "r_x3": 184.104, "r_y3": 399.237, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.204, "r_y0": 403.942, "r_x1": 243.691, "r_y1": 403.942, "r_x2": 243.691, "r_y2": 397.956, "r_x3": 240.204, "r_y3": 397.956, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 244.189, "r_y0": 408.177, "r_x1": 246.68, "r_y1": 408.177, "r_x2": 246.68, "r_y2": 399.625, "r_x3": 244.189, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 249.718, "r_y0": 408.177, "r_x1": 286.361, "r_y1": 408.177, "r_x2": 286.361, "r_y2": 399.625, "r_x3": 249.718, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "In partic-", "orig": "In partic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.132, "r_x1": 67.696, "r_y1": 420.132, "r_x2": 67.696, "r_y2": 411.58, "r_x3": 50.112, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "ular,", "orig": "ular,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.322, "r_y0": 420.132, "r_x1": 286.365, "r_y1": 420.132, "r_x2": 286.365, "r_y2": 411.58, "r_x3": 71.322, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "our contributions in this work can be summarised as", "orig": "our contributions in this work can be summarised as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.087, "r_x1": 82.52, "r_y1": 432.087, "r_x2": 82.52, "r_y2": 423.535, "r_x3": 50.112, "r_y3": 423.535, "coord_origin": "TOPLEFT"}, "text": "follows:", "orig": "follows:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569, "t": 444.432, "r": 286.365, "b": 489.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 453.372, "r_x1": 65.056, "r_y1": 453.372, "r_x2": 65.056, "r_y2": 444.82, "r_x3": 61.569, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 453.372, "r_x1": 117.101, "r_y1": 453.372, "r_x2": 117.101, "r_y2": 444.82, "r_x3": 70.037, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "We propose", "orig": "We propose", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, 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"r_y2": 526.3330000000001, "r_x3": 70.037, "r_y3": 526.3330000000001, "coord_origin": "TOPLEFT"}, "text": "while being much more efficient in training and infer-", "orig": "while being much more efficient in training and infer-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 546.84, "r_x1": 161.653, "r_y1": 546.84, "r_x2": 161.653, "r_y2": 538.288, "r_x3": 70.037, "r_y3": 538.288, "coord_origin": "TOPLEFT"}, "text": "ence to existing works.", "orig": "ence to existing works.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 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present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569, "t": 605.672, "r": 286.365, "b": 638.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 614.2239999999999, "r_x1": 65.056, "r_y1": 614.2239999999999, "r_x2": 65.056, "r_y2": 605.672, "r_x3": 61.569, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 614.2239999999999, "r_x1": 132.005, "r_y1": 614.2239999999999, "r_x2": 132.005, "r_y2": 605.672, "r_x3": 70.037, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "An augmented", "orig": "An augmented", 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"r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.617, "r_y0": 614.2239999999999, "r_x1": 261.2, "r_y1": 614.2239999999999, "r_x2": 261.2, "r_y2": 605.672, "r_x3": 216.617, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "PubTabNet", "orig": "PubTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.277, "r_y0": 614.2239999999999, "r_x1": 286.365, "r_y1": 614.2239999999999, "r_x2": 286.365, "r_y2": 605.672, "r_x3": 267.277, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "[37],", "orig": "[37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 626.179, "r_x1": 135.521, "r_y1": 626.179, "r_x2": 135.521, "r_y2": 617.627, "r_x3": 70.037, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36],", "orig": "FinTabNet [36],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.925, "r_y0": 626.179, "r_x1": 286.365, "r_y1": 626.179, "r_x2": 286.365, "r_y2": 617.627, "r_x3": 139.925, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "and TableBank [17] with generated", "orig": "and TableBank [17] with generated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 638.135, "r_x1": 198.056, "r_y1": 638.135, "r_x2": 198.056, "r_y2": 629.583, "r_x3": 70.037, "r_y3": 629.583, "coord_origin": "TOPLEFT"}, "text": "ground-truth for reproducibility.", "orig": "ground-truth for reproducibility.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 650.867, "r": 286.365, "b": 695.284, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 659.419, "r_x1": 204.452, "r_y1": 659.419, "r_x2": 204.452, "r_y2": 650.867, "r_x3": 62.067, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.833, "r_y0": 671.374, "r_x1": 286.365, "r_y1": 671.374, "r_x2": 286.365, "r_y2": 662.822, "r_x3": 246.833, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "In Sec. 3,", "orig": "In Sec. 3,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 683.329, "r_x1": 227.396, "r_y1": 683.329, "r_x2": 227.396, "r_y2": 674.777, "r_x3": 50.112, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "we describe the datasets on which we train.", "orig": "we describe the datasets on which we train.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 232.467, "r_y0": 683.329, "r_x1": 286.365, "r_y1": 683.329, "r_x2": 286.365, "r_y2": 674.777, "r_x3": 232.467, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "In Sec. 4, we", "orig": "In Sec. 4, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 695.284, "r_x1": 286.365, "r_y1": 695.284, "r_x2": 286.365, "r_y2": 686.732, "r_x3": 50.112, "r_y3": 686.732, "coord_origin": "TOPLEFT"}, "text": "introduce the TableFormer model-architecture and describe", "orig": "introduce the TableFormer model-architecture and describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.971, "t": 704.296, "r": 183.731, "b": 712.653, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 60.971, "r_y0": 709.427, "r_x1": 63.96, "r_y1": 709.427, "r_x2": 63.96, "r_y2": 704.296, "r_x3": 60.971, "r_y3": 704.296, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.458, "r_y0": 712.653, "r_x1": 183.731, "r_y1": 712.653, "r_x2": 183.731, "r_y2": 705.811, "r_x3": 64.458, "r_y3": 705.811, "coord_origin": "TOPLEFT"}, "text": "https://github.com/IBM/SynthTabNet", "orig": "https://github.com/IBM/SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.49, "r": 545.115, "b": 330.369, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.838, "r": 545.115, "b": 449.987, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.845, "r_y0": 342.39, "r_x1": 436.802, "r_y1": 342.39, "r_x2": 436.802, "r_y2": 333.838, "r_x3": 395.845, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "popularity", "orig": "popularity", 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"TOPLEFT"}, "text": "the community relied heavily on heuristic and/or statistical", "orig": "the community relied heavily on heuristic and/or statistical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 366.3, "r_x1": 545.115, "r_y1": 366.3, "r_x2": 545.115, "r_y2": 357.748, "r_x3": 308.862, "r_y3": 357.748, "coord_origin": "TOPLEFT"}, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "orig": "methods to do table structure identification [3, 7, 11, 5, 13,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 378.256, "r_x1": 324.633, "r_y1": 378.256, "r_x2": 324.633, "r_y2": 369.704, "r_x3": 308.862, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "28].", "orig": "28].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.634, "r_y0": 378.256, "r_x1": 545.115, "r_y1": 378.256, "r_x2": 545.115, "r_y2": 369.704, "r_x3": 329.634, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "Although such methods work well on constrained ta-", "orig": "Although such methods work well on constrained ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 390.211, "r_x1": 545.115, "r_y1": 390.211, "r_x2": 545.115, "r_y2": 381.659, "r_x3": 308.862, "r_y3": 381.659, "coord_origin": "TOPLEFT"}, "text": "bles [12], a more data-driven approach can be applied due", "orig": "bles [12], a more data-driven approach can be applied due", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 402.166, 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best-of-our knowl-", "orig": "To the best-of-our knowl-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 426.076, "r_x1": 545.115, "r_y1": 426.076, "r_x2": 545.115, "r_y2": 417.524, "r_x3": 308.862, "r_y3": 417.524, "coord_origin": "TOPLEFT"}, "text": "edge, there are currently two different types of network ar-", "orig": "edge, there are currently two different types of network ar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 438.032, "r_x1": 545.115, "r_y1": 438.032, "r_x2": 545.115, "r_y2": 429.48, "r_x3": 308.862, "r_y3": 429.48, "coord_origin": "TOPLEFT"}, "text": "chitecture that are being pursued for state-of-the-art table-", "orig": "chitecture that are being pursued for state-of-the-art table-", "text_direction": "left_to_right", 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Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 453.068, "r": 545.117, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 462.024, "r_x1": 423.262, "r_y1": 462.024, "r_x2": 423.262, "r_y2": 453.068, "r_x3": 320.817, "r_y3": 453.068, "coord_origin": "TOPLEFT"}, "text": "Image-to-Text networks", "orig": "Image-to-Text networks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.267, "r_y0": 462.008, "r_x1": 426.037, "r_y1": 462.008, "r_x2": 426.037, "r_y2": 453.456, "r_x3": 423.267, "r_y3": 453.456, "coord_origin": "TOPLEFT"}, 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255}, "rect": {"r_x0": 492.682, "r_y0": 485.918, "r_x1": 545.115, "r_y1": 485.918, "r_x2": 545.115, "r_y2": 477.366, "r_x3": 492.682, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "HTML table", "orig": "HTML table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.873, "r_x1": 459.218, "r_y1": 497.873, "r_x2": 459.218, "r_y2": 489.321, "r_x3": 308.862, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "tags [37, 17] or LaTeX symbols[10].", "orig": "tags [37, 17] or LaTeX symbols[10].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.115, "r_y0": 497.873, "r_x1": 545.115, "r_y1": 497.873, "r_x2": 545.115, "r_y2": 489.321, "r_x3": 465.115, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "The choice of sym-", "orig": "The choice of sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.828, "r_x1": 545.115, "r_y1": 509.828, "r_x2": 545.115, "r_y2": 501.276, "r_x3": 308.862, "r_y3": 501.276, "coord_origin": "TOPLEFT"}, "text": "bols is ultimately not very important, since one can be trans-", "orig": "bols is ultimately not very important, since one can be trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.784, "r_x1": 396.692, "r_y1": 521.784, "r_x2": 396.692, "r_y2": 513.232, "r_x3": 308.862, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "formed into the other.", "orig": "formed into the other.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.265, "r_y0": 521.784, "r_x1": 545.115, "r_y1": 521.784, "r_x2": 545.115, "r_y2": 513.232, "r_x3": 401.265, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "There are however subtle variations", "orig": "There are however subtle variations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.739, "r_x1": 433.484, "r_y1": 533.739, "r_x2": 433.484, "r_y2": 525.187, "r_x3": 308.862, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "in the Image-to-Text networks.", "orig": "in the Image-to-Text networks.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 438.187, "r_y0": 533.739, "r_x1": 545.115, "r_y1": 533.739, "r_x2": 545.115, "r_y2": 525.187, "r_x3": 438.187, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "The easiest network archi-", "orig": "The easiest network archi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.694, "r_x1": 420.941, "r_y1": 545.694, "r_x2": 420.941, "r_y2": 537.142, "r_x3": 308.862, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "tectures are 'image-encoder", "orig": "tectures are 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.595, "r_y0": 545.388, "r_x1": 433.558, "r_y1": 545.388, "r_x2": 433.558, "r_y2": 536.8240000000001, "r_x3": 423.595, "r_y3": 536.8240000000001, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.212, "r_y0": 545.694, "r_x1": 545.113, "r_y1": 545.694, "r_x2": 545.113, "r_y2": 537.142, "r_x3": 436.212, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "text-decoder' (IETD), sim-", "orig": "text-decoder' (IETD), sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.649, "r_x1": 545.115, "r_y1": 557.649, "r_x2": 545.115, "r_y2": 549.097, "r_x3": 308.862, "r_y3": 549.097, "coord_origin": "TOPLEFT"}, "text": "ilar to network architectures that try to provide captions to", "orig": "ilar to network architectures that try to provide captions to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.604, "r_x1": 545.115, "r_y1": 569.604, "r_x2": 545.115, "r_y2": 561.052, "r_x3": 308.862, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "images [32]. In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.045, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 364.045, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "to generate the textual content of each table", "orig": "to generate the textual content of each table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 325.739, "r_y1": 701.111, "r_x2": 325.739, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.746, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 331.746, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "The network architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder \u2192 text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder \u2192 dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 95.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, 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"text", "bbox": {"l": 50.112, "t": 99.84000000000003, "r": 286.365, "b": 227.94399999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 120.34699999999998, "r_x1": 74.75, "r_y1": 120.34699999999998, "r_x2": 74.75, "r_y2": 111.79499999999996, "r_x3": 50.112, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "sition.", "orig": "sition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.335, "r_y0": 120.34699999999998, "r_x1": 286.365, "r_y1": 120.34699999999998, "r_x2": 286.365, "r_y2": 111.79499999999996, "r_x3": 81.335, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "The latter is a long standing problem in the com-", "orig": "The latter is a long standing problem in the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 132.30200000000002, "r_x1": 235.117, "r_y1": 132.30200000000002, "r_x2": 235.117, "r_y2": 123.75, "r_x3": 50.112, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "munity of document understanding [6, 4, 14].", "orig": "munity of document understanding [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"r_y3": 147.66099999999994, "coord_origin": "TOPLEFT"}, "text": "proaches that can easily be re-purposed to solve this prob-", "orig": "proaches that can easily be re-purposed to solve this prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 168.168, "r_x1": 286.365, "r_y1": 168.168, "r_x2": 286.365, "r_y2": 159.61599999999999, "r_x3": 50.112, "r_y3": 159.61599999999999, "coord_origin": "TOPLEFT"}, "text": "lem. Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.79999999999995, "r": 286.365, "b": 371.859, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.172, "r_x1": 286.365, "r_y1": 288.172, "r_x2": 286.365, "r_y2": 279.62, "r_x3": 50.112, "r_y3": 279.62, "coord_origin": "TOPLEFT"}, "text": "we can obtain the structure of any table, irregardless of the", "orig": "we can obtain the structure of any table, irregardless of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.128, "r_x1": 88.568, "r_y1": 300.128, "r_x2": 88.568, "r_y2": 291.576, "r_x3": 50.112, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "language.", "orig": "language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.502, "r_y0": 300.128, "r_x1": 286.365, "r_y1": 300.128, "r_x2": 286.365, "r_y2": 291.576, "r_x3": 95.502, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "Second, we want our algorithm to leverage as", "orig": "Second, we want our algorithm to leverage as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.083, "r_x1": 286.365, "r_y1": 312.083, "r_x2": 286.365, "r_y2": 303.531, "r_x3": 50.112, "r_y3": 303.531, "coord_origin": "TOPLEFT"}, "text": "much data as possible from the original PDF document. For", "orig": "much data as possible from the original PDF document. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.038, "r_x1": 286.365, "r_y1": 324.038, "r_x2": 286.365, "r_y2": 315.486, "r_x3": 50.112, "r_y3": 315.486, "coord_origin": "TOPLEFT"}, "text": "programmatic PDF documents, the text-cells can often be", "orig": "programmatic PDF documents, the text-cells can often be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.993, "r_x1": 286.365, "r_y1": 335.993, "r_x2": 286.365, "r_y2": 327.441, "r_x3": 50.112, "r_y3": 327.441, "coord_origin": "TOPLEFT"}, "text": "extracted much faster and with higher accuracy compared", "orig": "extracted much faster and with higher accuracy compared", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.948, "r_x1": 286.365, "r_y1": 347.948, "r_x2": 286.365, "r_y2": 339.396, "r_x3": 50.112, "r_y3": 339.396, "coord_origin": "TOPLEFT"}, "text": "to OCR methods. Last but not least, we want to have a di-", "orig": "to OCR methods. Last but not least, we want to have a di-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.904, "r_x1": 286.365, "r_y1": 359.904, "r_x2": 286.365, "r_y2": 351.352, "r_x3": 50.112, "r_y3": 351.352, "coord_origin": "TOPLEFT"}, "text": "rect link between the table-cell and its bounding box in the", "orig": "rect link between the table-cell and its bounding box in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.859, "r_x1": 76.951, "r_y1": 371.859, "r_x2": 76.951, "r_y2": 363.307, "r_x3": 50.112, "r_y3": 363.307, "coord_origin": "TOPLEFT"}, "text": "image.", "orig": "image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.715, "r": 286.367, "b": 432.087, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 396.222, "r_x1": 120.986, "r_y1": 396.222, "r_x2": 120.986, "r_y2": 387.67, "r_x3": 50.112, "r_y3": 387.67, "coord_origin": "TOPLEFT"}, "text": "new model called", "orig": "new model called", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.901, "r_y0": 396.238, "r_x1": 179.731, "r_y1": 396.238, "r_x2": 179.731, "r_y2": 387.282, "r_x3": 123.901, "r_y3": 387.282, "coord_origin": "TOPLEFT"}, "text": "TableFormer", "orig": "TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.646, "r_y0": 396.222, "r_x1": 286.367, "r_y1": 396.222, "r_x2": 286.367, "r_y2": 387.67, "r_x3": 182.646, "r_y3": 387.67, "coord_origin": "TOPLEFT"}, "text": "and a synthetically gener-", "orig": "and a synthetically gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 408.177, "r_x1": 181.758, "r_y1": 408.177, "r_x2": 181.758, "r_y2": 399.625, "r_x3": 50.112, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "ated table structure dataset called", "orig": "ated table structure dataset called", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.104, "r_y0": 408.193, "r_x1": 240.203, "r_y1": 408.193, "r_x2": 240.203, "r_y2": 399.237, "r_x3": 184.104, "r_y3": 399.237, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.204, "r_y0": 403.942, "r_x1": 243.691, "r_y1": 403.942, "r_x2": 243.691, "r_y2": 397.956, "r_x3": 240.204, "r_y3": 397.956, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 244.189, "r_y0": 408.177, "r_x1": 246.68, "r_y1": 408.177, "r_x2": 246.68, "r_y2": 399.625, "r_x3": 244.189, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 249.718, "r_y0": 408.177, "r_x1": 286.361, "r_y1": 408.177, "r_x2": 286.361, "r_y2": 399.625, "r_x3": 249.718, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "In partic-", "orig": "In partic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.132, "r_x1": 67.696, "r_y1": 420.132, "r_x2": 67.696, "r_y2": 411.58, "r_x3": 50.112, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "ular,", "orig": "ular,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.322, "r_y0": 420.132, "r_x1": 286.365, "r_y1": 420.132, "r_x2": 286.365, "r_y2": 411.58, "r_x3": 71.322, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "our contributions in this work can be summarised as", "orig": "our contributions in this work can be summarised as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.087, "r_x1": 82.52, "r_y1": 432.087, "r_x2": 82.52, "r_y2": 423.535, "r_x3": 50.112, "r_y3": 423.535, "coord_origin": "TOPLEFT"}, "text": "follows:", "orig": "follows:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569, "t": 444.432, "r": 286.365, "b": 489.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 453.372, "r_x1": 65.056, "r_y1": 453.372, "r_x2": 65.056, "r_y2": 444.82, "r_x3": 61.569, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 453.372, "r_x1": 117.101, "r_y1": 453.372, "r_x2": 117.101, "r_y2": 444.82, "r_x3": 70.037, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "We propose", "orig": "We propose", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, 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boxes for", "orig": "that predicts tables structure and bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 477.282, "r_x1": 286.365, "r_y1": 477.282, "r_x2": 286.365, "r_y2": 468.73, "r_x3": 70.037, "r_y3": 468.73, "coord_origin": "TOPLEFT"}, "text": "the table content simultaneously in an end-to-end ap-", "orig": "the table content simultaneously in an end-to-end ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 489.237, "r_x1": 99.636, "r_y1": 489.237, "r_x2": 99.636, "r_y2": 480.685, "r_x3": 70.037, "r_y3": 480.685, "coord_origin": "TOPLEFT"}, "text": "proach.", "orig": "proach.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569, "t": 502.034, "r": 286.365, "b": 546.84, "coord_origin": "TOPLEFT"}, "confidence": 0.982271671295166, "cells": [{"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 510.974, "r_x1": 65.056, "r_y1": 510.974, "r_x2": 65.056, "r_y2": 502.422, "r_x3": 61.569, "r_y3": 502.422, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 510.974, "r_x1": 111.88, "r_y1": 510.974, "r_x2": 111.88, "r_y2": 502.422, "r_x3": 70.037, "r_y3": 502.422, "coord_origin": "TOPLEFT"}, "text": "Across all", "orig": "Across all", "text_direction": "left_to_right", 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"orig": "signif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 522.929, "r_x1": 97.155, "r_y1": 522.929, "r_x2": 97.155, "r_y2": 514.377, "r_x3": 70.037, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "icantly", "orig": "icantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.678, "r_y0": 522.929, "r_x1": 150.375, "r_y1": 522.929, "r_x2": 150.375, "r_y2": 514.377, "r_x3": 101.678, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "outperforms", "orig": "outperforms", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.898, "r_y0": 522.929, "r_x1": 186.301, "r_y1": 522.929, "r_x2": 186.301, "r_y2": 514.377, "r_x3": 154.898, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "existing", "orig": "existing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.824, "r_y0": 522.929, "r_x1": 250.021, "r_y1": 522.929, "r_x2": 250.021, "r_y2": 514.377, "r_x3": 190.824, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 254.544, "r_y0": 522.929, "r_x1": 286.365, "r_y1": 522.929, "r_x2": 286.365, "r_y2": 514.377, "r_x3": 254.544, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "metrics,", "orig": "metrics,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 534.885, "r_x1": 286.365, "r_y1": 534.885, "r_x2": 286.365, "r_y2": 526.3330000000001, "r_x3": 70.037, "r_y3": 526.3330000000001, "coord_origin": "TOPLEFT"}, "text": "while being much more efficient in training and infer-", "orig": "while being much more efficient in training and infer-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 546.84, "r_x1": 161.653, "r_y1": 546.84, "r_x2": 161.653, "r_y2": 538.288, "r_x3": 70.037, "r_y3": 538.288, "coord_origin": "TOPLEFT"}, "text": "ence to existing works.", "orig": "ence to existing works.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 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559.637, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.553, "r_y0": 568.577, "r_x1": 186.976, "r_y1": 568.577, "r_x2": 186.976, "r_y2": 560.025, "r_x3": 182.553, "r_y3": 560.025, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.848, "r_y0": 568.577, "r_x1": 242.767, "r_y1": 568.577, "r_x2": 242.767, "r_y2": 560.025, "r_x3": 191.848, "r_y3": 560.025, "coord_origin": "TOPLEFT"}, "text": "synthetically", "orig": "synthetically", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.639, "r_y0": 568.577, "r_x1": 286.363, "r_y1": 568.577, "r_x2": 286.363, "r_y2": 560.025, "r_x3": 247.639, "r_y3": 560.025, "coord_origin": "TOPLEFT"}, "text": "generated", "orig": "generated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 580.532, "r_x1": 286.365, "r_y1": 580.532, "r_x2": 286.365, "r_y2": 571.98, "r_x3": 70.037, "r_y3": 571.98, "coord_origin": "TOPLEFT"}, "text": "dataset, with various appearance styles and complex-", "orig": "dataset, with various appearance styles and complex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 592.487, "r_x1": 82.401, "r_y1": 592.487, "r_x2": 82.401, "r_y2": 583.935, "r_x3": 70.037, "r_y3": 583.935, "coord_origin": "TOPLEFT"}, "text": "ity.", "orig": "ity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569, "t": 605.672, "r": 286.365, "b": 638.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 614.2239999999999, "r_x1": 65.056, "r_y1": 614.2239999999999, "r_x2": 65.056, "r_y2": 605.672, "r_x3": 61.569, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 614.2239999999999, "r_x1": 132.005, "r_y1": 614.2239999999999, "r_x2": 132.005, "r_y2": 605.672, "r_x3": 70.037, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "An augmented", "orig": "An augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.082, "r_y0": 614.2239999999999, "r_x1": 165.748, "r_y1": 614.2239999999999, "r_x2": 165.748, "r_y2": 605.672, "r_x3": 138.082, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "dataset", "orig": "dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.815, "r_y0": 614.2239999999999, "r_x1": 194.5, "r_y1": 614.2239999999999, "r_x2": 194.5, "r_y2": 605.672, "r_x3": 171.815, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "based", "orig": "based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.577, "r_y0": 614.2239999999999, "r_x1": 210.54, "r_y1": 614.2239999999999, "r_x2": 210.54, "r_y2": 605.672, "r_x3": 200.577, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.617, "r_y0": 614.2239999999999, "r_x1": 261.2, "r_y1": 614.2239999999999, "r_x2": 261.2, "r_y2": 605.672, "r_x3": 216.617, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "PubTabNet", "orig": "PubTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.277, "r_y0": 614.2239999999999, "r_x1": 286.365, "r_y1": 614.2239999999999, "r_x2": 286.365, "r_y2": 605.672, "r_x3": 267.277, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "[37],", "orig": "[37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 626.179, "r_x1": 135.521, "r_y1": 626.179, "r_x2": 135.521, "r_y2": 617.627, "r_x3": 70.037, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36],", "orig": "FinTabNet [36],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.925, "r_y0": 626.179, "r_x1": 286.365, "r_y1": 626.179, "r_x2": 286.365, "r_y2": 617.627, "r_x3": 139.925, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "and TableBank [17] with generated", "orig": "and TableBank [17] with generated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 638.135, "r_x1": 198.056, "r_y1": 638.135, "r_x2": 198.056, "r_y2": 629.583, "r_x3": 70.037, "r_y3": 629.583, "coord_origin": "TOPLEFT"}, "text": "ground-truth for reproducibility.", "orig": "ground-truth for reproducibility.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 650.867, "r": 286.365, "b": 695.284, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 659.419, "r_x1": 204.452, "r_y1": 659.419, "r_x2": 204.452, "r_y2": 650.867, "r_x3": 62.067, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.833, "r_y0": 671.374, "r_x1": 286.365, "r_y1": 671.374, "r_x2": 286.365, "r_y2": 662.822, "r_x3": 246.833, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "In Sec. 3,", "orig": "In Sec. 3,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 683.329, "r_x1": 227.396, "r_y1": 683.329, "r_x2": 227.396, "r_y2": 674.777, "r_x3": 50.112, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "we describe the datasets on which we train.", "orig": "we describe the datasets on which we train.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 232.467, "r_y0": 683.329, "r_x1": 286.365, "r_y1": 683.329, "r_x2": 286.365, "r_y2": 674.777, "r_x3": 232.467, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "In Sec. 4, we", "orig": "In Sec. 4, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 695.284, "r_x1": 286.365, "r_y1": 695.284, "r_x2": 286.365, "r_y2": 686.732, "r_x3": 50.112, "r_y3": 686.732, "coord_origin": "TOPLEFT"}, "text": "introduce the TableFormer model-architecture and describe", "orig": "introduce the TableFormer model-architecture and describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.971, "t": 704.296, "r": 183.731, "b": 712.653, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 60.971, "r_y0": 709.427, "r_x1": 63.96, "r_y1": 709.427, "r_x2": 63.96, "r_y2": 704.296, "r_x3": 60.971, "r_y3": 704.296, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.458, "r_y0": 712.653, "r_x1": 183.731, "r_y1": 712.653, "r_x2": 183.731, "r_y2": 705.811, "r_x3": 64.458, "r_y3": 705.811, "coord_origin": "TOPLEFT"}, "text": "https://github.com/IBM/SynthTabNet", "orig": "https://github.com/IBM/SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.49, "r": 545.115, "b": 330.369, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.838, "r": 545.115, "b": 449.987, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.845, "r_y0": 342.39, "r_x1": 436.802, "r_y1": 342.39, "r_x2": 436.802, "r_y2": 333.838, "r_x3": 395.845, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "popularity", "orig": "popularity", 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best-of-our knowl-", "orig": "To the best-of-our knowl-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 426.076, "r_x1": 545.115, "r_y1": 426.076, "r_x2": 545.115, "r_y2": 417.524, "r_x3": 308.862, "r_y3": 417.524, "coord_origin": "TOPLEFT"}, "text": "edge, there are currently two different types of network ar-", "orig": "edge, there are currently two different types of network ar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 438.032, "r_x1": 545.115, "r_y1": 438.032, "r_x2": 545.115, "r_y2": 429.48, "r_x3": 308.862, "r_y3": 429.48, "coord_origin": "TOPLEFT"}, "text": "chitecture that are being pursued for state-of-the-art table-", "orig": "chitecture that are being pursued for state-of-the-art table-", "text_direction": "left_to_right", 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Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 453.068, "r": 545.117, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 462.024, "r_x1": 423.262, "r_y1": 462.024, "r_x2": 423.262, "r_y2": 453.068, "r_x3": 320.817, "r_y3": 453.068, "coord_origin": "TOPLEFT"}, "text": "Image-to-Text networks", "orig": "Image-to-Text networks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.267, "r_y0": 462.008, "r_x1": 426.037, "r_y1": 462.008, "r_x2": 426.037, "r_y2": 453.456, "r_x3": 423.267, "r_y3": 453.456, "coord_origin": "TOPLEFT"}, 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In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.045, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 364.045, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "to generate the textual content of each table", "orig": "to generate the textual content of each table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 325.739, "r_y1": 701.111, "r_x2": 325.739, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.746, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 331.746, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "The network architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder \u2192 text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder \u2192 dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 250.151, "r_y1": 84.029, "r_x2": 250.151, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tag-decoder which is constrained to the table-tags.", "orig": "tag-decoder which is constrained to the table-tags.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 96.03999999999996, "r_x1": 70.366, "r_y1": 96.03999999999996, "r_x2": 70.366, "r_y2": 87.48800000000006, "r_x3": 62.067, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.931, "r_y0": 96.03999999999996, "r_x1": 110.953, "r_y1": 96.03999999999996, "r_x2": 110.953, "r_y2": 87.48800000000006, "r_x3": 76.931, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "practice,", "orig": "practice,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 118.545, "r_y0": 96.03999999999996, "r_x1": 136.258, "r_y1": 96.03999999999996, "r_x2": 136.258, "r_y2": 87.48800000000006, "r_x3": 118.545, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "both", "orig": "both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.824, "r_y0": 96.03999999999996, "r_x1": 175.372, "r_y1": 96.03999999999996, "r_x2": 175.372, "r_y2": 87.48800000000006, "r_x3": 142.824, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "network", "orig": "network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.947, "r_y0": 96.03999999999996, "r_x1": 232.836, "r_y1": 96.03999999999996, "r_x2": 232.836, "r_y2": 87.48800000000006, "r_x3": 181.947, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "architectures", "orig": "architectures", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 239.411, "r_y0": 96.03999999999996, "r_x1": 265.414, "r_y1": 96.03999999999996, "r_x2": 265.414, "r_y2": 87.48800000000006, "r_x3": 239.411, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "(IETD", "orig": "(IETD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 271.979, "r_y0": 96.03999999999996, "r_x1": 286.365, "r_y1": 96.03999999999996, "r_x2": 286.365, "r_y2": 87.48800000000006, "r_x3": 271.979, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 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For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. Additionally, OCR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 239.50199999999995, "r_x1": 286.365, "r_y1": 239.50199999999995, "r_x2": 286.365, "r_y2": 230.95000000000005, "r_x3": 50.112, "r_y3": 230.95000000000005, "coord_origin": "TOPLEFT"}, "text": "can be completely omitted if the tables originate from pro-", "orig": "can be completely omitted if the tables originate from pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 251.457, "r_x1": 286.365, "r_y1": 251.457, "r_x2": 286.365, "r_y2": 242.90499999999997, "r_x3": 50.112, "r_y3": 242.90499999999997, "coord_origin": "TOPLEFT"}, "text": "grammatic PDF documents with known positions of each", "orig": "grammatic PDF documents with known positions of each", "text_direction": "left_to_right", 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.849, "r_y0": 311.29, "r_x1": 286.365, "r_y1": 311.29, "r_x2": 286.365, "r_y2": 302.738, "r_x3": 138.849, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "Note that one table cell can consti-", "orig": "Note that one table cell can consti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.245, "r_x1": 286.365, "r_y1": 323.245, "r_x2": 286.365, "r_y2": 314.693, "r_x3": 50.112, "r_y3": 314.693, "coord_origin": "TOPLEFT"}, "text": "tute out of multiple text-cells. To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 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255}, "rect": {"r_x0": 97.305, "r_y0": 394.976, "r_x1": 286.365, "r_y1": 394.976, "r_x2": 286.365, "r_y2": 386.424, "r_x3": 97.305, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "Graph Convolutional Networks (GCN's) based", "orig": "Graph Convolutional Networks (GCN's) based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 406.931, "r_x1": 286.365, "r_y1": 406.931, "r_x2": 286.365, "r_y2": 398.379, "r_x3": 50.112, "r_y3": 398.379, "coord_origin": "TOPLEFT"}, "text": "methods take the image as an input, but also the position of", "orig": "methods take the image as an input, but also the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 418.886, "r_x1": 286.365, "r_y1": 418.886, "r_x2": 286.365, "r_y2": 410.334, "r_x3": 50.112, "r_y3": 410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.882, "r_y0": 502.629, "r_x1": 255.652, "r_y1": 502.629, "r_x2": 255.652, "r_y2": 494.077, "r_x3": 252.882, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.63, "r_y0": 502.629, "r_x1": 286.366, "r_y1": 502.629, "r_x2": 286.366, "r_y2": 494.077, "r_x3": 258.63, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": "Apop-", "orig": "Apop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 514.5840000000001, "r_x1": 286.365, "r_y1": 514.5840000000001, "r_x2": 286.365, "r_y2": 506.032, "r_x3": 50.112, "r_y3": 506.032, "coord_origin": "TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", 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"rule-sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.975, "r_y0": 574.36, "r_x1": 256.726, "r_y1": 574.36, "r_x2": 256.726, "r_y2": 565.808, "r_x3": 248.975, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.459, "r_y0": 574.36, "r_x1": 286.365, "r_y1": 574.36, "r_x2": 286.365, "r_y2": 565.808, "r_x3": 261.459, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "obtain", "orig": "obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.315, "r_x1": 122.64, "r_y1": 586.315, "r_x2": 122.64, "r_y2": 577.763, "r_x3": 50.112, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "its table-structure.", "orig": "its table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.482, "r_y0": 586.315, "r_x1": 286.365, "r_y1": 586.315, "r_x2": 286.365, "r_y2": 577.763, "r_x3": 127.482, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "Currently, this approach achieves state-", "orig": "Currently, this approach achieves state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 598.27, "r_x1": 87.731, "r_y1": 598.27, "r_x2": 87.731, "r_y2": 589.718, "r_x3": 50.112, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "of-the-art", "orig": "of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.074, "r_y0": 598.27, "r_x1": 120.577, "r_y1": 598.27, "r_x2": 120.577, "r_y2": 589.718, "r_x3": 92.074, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "results,", "orig": "results,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.389, "r_y0": 598.27, "r_x1": 137.922, "r_y1": 598.27, "r_x2": 137.922, "r_y2": 589.718, "r_x3": 125.389, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "but", "orig": "but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.266, "r_y0": 598.27, "r_x1": 148.911, "r_y1": 598.27, "r_x2": 148.911, "r_y2": 589.718, "r_x3": 142.266, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.255, "r_y0": 598.27, "r_x1": 165.987, "r_y1": 598.27, "r_x2": 165.987, "r_y2": 589.718, "r_x3": 153.255, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "not", "orig": "not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.331, "r_y0": 598.27, "r_x1": 286.365, "r_y1": 598.27, "r_x2": 286.365, "r_y2": 589.718, "r_x3": 170.331, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "an end-to-end deep-learning", "orig": "an end-to-end deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 610.225, "r_x1": 82.49, "r_y1": 610.225, "r_x2": 82.49, "r_y2": 601.673, "r_x3": 50.112, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.173, "r_y0": 610.225, "r_x1": 286.365, "r_y1": 610.225, "r_x2": 286.365, "r_y2": 601.673, "r_x3": 87.173, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "As such, new rules need to be written if different", "orig": "As such, new rules need to be written if different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 622.181, "r_x1": 175.989, "r_y1": 622.181, "r_x2": 175.989, "r_y2": 613.629, "r_x3": 50.112, "r_y3": 613.629, "coord_origin": "TOPLEFT"}, "text": "types of tables are encountered.", "orig": "types of tables are encountered.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 646.693, "r_x1": 105.225, "r_y1": 646.693, "r_x2": 105.225, "r_y2": 635.9449999999999, "r_x3": 50.112, "r_y3": 635.9449999999999, "coord_origin": "TOPLEFT"}, "text": "3. Datasets", "orig": "3. 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"text": "dimensions in PubTabNet + FinTabNet datasets", "orig": "dimensions in PubTabNet + FinTabNet datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 326.294, "r_x1": 437.27, "r_y1": 326.294, "r_x2": 437.27, "r_y2": 317.742, "r_x3": 308.862, "r_y3": 317.742, "coord_origin": "TOPLEFT"}, "text": "balance in the previous datasets.", "orig": "balance in the previous datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 340.352, "r_x1": 545.115, "r_y1": 340.352, "r_x2": 545.115, "r_y2": 331.8, "r_x3": 320.817, "r_y3": 331.8, "coord_origin": "TOPLEFT"}, "text": "The PubTabNet dataset contains 509k tables delivered as", "orig": "The PubTabNet dataset contains 509k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 352.307, "r_x1": 545.115, "r_y1": 352.307, "r_x2": 545.115, "r_y2": 343.755, "r_x3": 308.862, "r_y3": 343.755, "coord_origin": "TOPLEFT"}, "text": "annotated PNG images. The annotations consist of the table", "orig": "annotated PNG images. The annotations consist of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 364.263, "r_x1": 545.115, "r_y1": 364.263, "r_x2": 545.115, "r_y2": 355.711, "r_x3": 308.862, "r_y3": 355.711, "coord_origin": "TOPLEFT"}, "text": "structure represented in HTML format, the tokenized text", "orig": "structure represented in HTML format, the tokenized text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.218, "r_x1": 545.115, "r_y1": 376.218, "r_x2": 545.115, "r_y2": 367.666, "r_x3": 308.862, "r_y3": 367.666, "coord_origin": "TOPLEFT"}, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "orig": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.173, "r_x1": 545.115, "r_y1": 388.173, "r_x2": 545.115, "r_y2": 379.621, "r_x3": 308.862, "r_y3": 379.621, "coord_origin": "TOPLEFT"}, "text": "pearance style of PubTabNet. Depending on its complexity,", "orig": "pearance style of PubTabNet. Depending on its complexity,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.128, "r_x1": 545.115, "r_y1": 400.128, "r_x2": 545.115, "r_y2": 391.576, "r_x3": 308.862, "r_y3": 391.576, "coord_origin": "TOPLEFT"}, "text": "a table is characterized as 'simple' when it does not contain", "orig": "a table is characterized as 'simple' when it does not contain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.083, "r_x1": 545.115, "r_y1": 412.083, "r_x2": 545.115, "r_y2": 403.531, "r_x3": 308.862, "r_y3": 403.531, "coord_origin": "TOPLEFT"}, "text": "row spans or column spans, otherwise it is 'complex'. The", "orig": "row spans or column spans, otherwise it is 'complex'. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.038, "r_x1": 545.115, "r_y1": 424.038, "r_x2": 545.115, "r_y2": 415.486, "r_x3": 308.862, "r_y3": 415.486, "coord_origin": "TOPLEFT"}, "text": "dataset is divided into Train and Val splits (roughly 98% and", "orig": "dataset is divided into Train and Val splits (roughly 98% and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.994, "r_x1": 545.115, "r_y1": 435.994, "r_x2": 545.115, "r_y2": 427.442, "r_x3": 308.862, "r_y3": 427.442, "coord_origin": "TOPLEFT"}, "text": "2%). The Train split consists of 54% simple and 46% com-", "orig": "2%). The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized text and the bounding boxes on a table cell basis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 519.6800000000001, "r_x1": 545.115, "r_y1": 519.6800000000001, "r_x2": 545.115, "r_y2": 511.128, "r_x3": 308.862, "r_y3": 511.128, "coord_origin": "TOPLEFT"}, "text": "The dataset is divided into Train, Test and Val splits (81%,", "orig": "The dataset is divided into Train, Test and Val splits (81%,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.635, "r_x1": 545.115, "r_y1": 531.635, "r_x2": 545.115, "r_y2": 523.0830000000001, "r_x3": 308.862, "r_y3": 523.0830000000001, "coord_origin": "TOPLEFT"}, "text": "9.5%, 9.5%), and each one is almost equally divided into", "orig": "9.5%, 9.5%), and each one is almost equally divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.59, "r_x1": 545.115, "r_y1": 543.59, "r_x2": 545.115, "r_y2": 535.038, "r_x3": 308.862, "r_y3": 535.038, "coord_origin": "TOPLEFT"}, "text": "simple and complex tables (Train: 48% simple, 52% com-", "orig": "simple and complex tables (Train: 48% simple, 52% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.545, "r_x1": 350.964, "r_y1": 555.545, "r_x2": 350.964, "r_y2": 546.9929999999999, "r_x3": 308.862, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "plex, Test:", "orig": "plex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.467, "r_y0": 555.545, "r_x1": 490.082, "r_y1": 555.545, "r_x2": 490.082, "r_y2": 546.9929999999999, "r_x3": 355.467, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "48% simple, 52% complex, Test:", "orig": "48% simple, 52% complex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.595, "r_y0": 555.545, "r_x1": 545.115, "r_y1": 555.545, "r_x2": 545.115, "r_y2": 546.9929999999999, "r_x3": 494.595, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "53% simple,", "orig": "53% simple,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.501, "r_x1": 370.6, "r_y1": 567.501, "r_x2": 370.6, "r_y2": 558.9490000000001, "r_x3": 308.862, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "47% complex).", "orig": "47% complex).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.731, "r_y0": 603.366, "r_x1": 545.115, "r_y1": 603.366, "r_x2": 545.115, "r_y2": 594.814, "r_x3": 407.731, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "The entire dataset consists of sim-", "orig": "The entire dataset consists of sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 615.321, "r_x1": 545.115, "r_y1": 615.321, "r_x2": 545.115, "r_y2": 606.769, "r_x3": 308.862, "r_y3": 606.769, "coord_origin": "TOPLEFT"}, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "orig": "ple tables and it is divided into 90% Train, 3% Test and 7%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 627.2760000000001, "r_x1": 348.164, "r_y1": 627.2760000000001, "r_x2": 348.164, "r_y2": 618.7239999999999, "r_x3": 308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 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"r_y2": 99.44299999999998, "r_x3": 50.112, "r_y3": 99.44299999999998, "coord_origin": "TOPLEFT"}, "text": "IEDD) require an implicit, custom trained object-character-", "orig": "IEDD) require an implicit, custom trained object-character-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.95100000000002, "r_x1": 286.365, "r_y1": 119.95100000000002, "r_x2": 286.365, "r_y2": 111.399, "r_x3": 50.112, "r_y3": 111.399, "coord_origin": "TOPLEFT"}, "text": "recognition (OCR) to obtain the content of the table-cells.", "orig": "recognition (OCR) to obtain the content of the table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.90599999999995, "r_x1": 286.365, "r_y1": 131.90599999999995, "r_x2": 286.365, "r_y2": 123.35400000000004, "r_x3": 50.112, "r_y3": 123.35400000000004, "coord_origin": "TOPLEFT"}, "text": "In the case of IETD, this OCR engine is implicit in the de-", "orig": "In the case of IETD, this OCR engine is implicit in the de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.861, "r_x1": 286.365, "r_y1": 143.861, "r_x2": 286.365, "r_y2": 135.30899999999997, "r_x3": 50.112, "r_y3": 135.30899999999997, "coord_origin": "TOPLEFT"}, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. Additionally, OCR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 239.50199999999995, "r_x1": 286.365, "r_y1": 239.50199999999995, "r_x2": 286.365, "r_y2": 230.95000000000005, "r_x3": 50.112, "r_y3": 230.95000000000005, "coord_origin": "TOPLEFT"}, "text": "can be completely omitted if the tables originate from pro-", "orig": "can be completely omitted if the tables originate from pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 251.457, "r_x1": 286.365, "r_y1": 251.457, "r_x2": 286.365, "r_y2": 242.90499999999997, "r_x3": 50.112, "r_y3": 242.90499999999997, "coord_origin": "TOPLEFT"}, "text": "grammatic PDF documents with known positions of each", "orig": "grammatic PDF documents with known positions of each", "text_direction": "left_to_right", 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To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.11, "r_x1": 286.365, "r_y1": 359.11, "r_x2": 286.365, "r_y2": 350.558, "r_x3": 50.112, "r_y3": 350.558, "coord_origin": "TOPLEFT"}, "text": "node is then associated with en embedding vector coming", "orig": "node is then associated with en embedding vector coming", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.066, "r_x1": 286.365, "r_y1": 371.066, "r_x2": 286.365, "r_y2": 362.514, "r_x3": 50.112, "r_y3": 362.514, "coord_origin": "TOPLEFT"}, "text": "from the encoded image, its coordinates and the encoded", "orig": "from the encoded image, its coordinates and the encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.021, "r_x1": 67.397, "r_y1": 383.021, "r_x2": 67.397, "r_y2": 374.469, "r_x3": 50.112, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "text.", "orig": "text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 72.976, "r_y0": 383.021, "r_x1": 286.365, "r_y1": 383.021, "r_x2": 286.365, "r_y2": 374.469, "r_x3": 72.976, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "Furthermore, nodes that represent adjacent text-cells", "orig": "Furthermore, nodes that represent adjacent text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 394.976, "r_x1": 92.602, "r_y1": 394.976, "r_x2": 92.602, "r_y2": 386.424, "r_x3": 50.112, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "are linked.", "orig": "are linked.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.305, "r_y0": 394.976, "r_x1": 286.365, "r_y1": 394.976, "r_x2": 286.365, "r_y2": 386.424, "r_x3": 97.305, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "Graph Convolutional Networks (GCN's) based", "orig": "Graph Convolutional Networks (GCN's) based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 406.931, "r_x1": 286.365, "r_y1": 406.931, "r_x2": 286.365, "r_y2": 398.379, "r_x3": 50.112, "r_y3": 398.379, "coord_origin": "TOPLEFT"}, "text": "methods take the image as an input, but also the position of", "orig": "methods take the image as an input, but also the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 418.886, "r_x1": 286.365, "r_y1": 418.886, "r_x2": 286.365, "r_y2": 410.334, "r_x3": 50.112, "r_y3": 410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 493.689, "r": 286.366, "b": 622.181, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.882, "r_y0": 502.629, "r_x1": 255.652, "r_y1": 502.629, "r_x2": 255.652, "r_y2": 494.077, "r_x3": 252.882, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.63, "r_y0": 502.629, "r_x1": 286.366, "r_y1": 502.629, "r_x2": 286.366, "r_y2": 494.077, "r_x3": 258.63, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": "Apop-", "orig": "Apop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 514.5840000000001, "r_x1": 286.365, "r_y1": 514.5840000000001, "r_x2": 286.365, "r_y2": 506.032, "r_x3": 50.112, "r_y3": 506.032, "coord_origin": "TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.828, "r_y0": 574.36, "r_x1": 166.983, "r_y1": 574.36, "r_x2": 166.983, "r_y2": 565.808, "r_x3": 149.828, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "uses", "orig": "uses", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.715, "r_y0": 574.36, "r_x1": 205.768, "r_y1": 574.36, "r_x2": 205.768, "r_y2": 565.808, "r_x3": 171.715, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "different", "orig": "different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.5, "r_y0": 574.36, "r_x1": 244.243, "r_y1": 574.36, "r_x2": 244.243, "r_y2": 565.808, "r_x3": 210.5, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "rule-sets", "orig": "rule-sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.975, "r_y0": 574.36, "r_x1": 256.726, "r_y1": 574.36, "r_x2": 256.726, "r_y2": 565.808, "r_x3": 248.975, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.459, "r_y0": 574.36, "r_x1": 286.365, "r_y1": 574.36, "r_x2": 286.365, "r_y2": 565.808, "r_x3": 261.459, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "obtain", "orig": "obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.315, "r_x1": 122.64, "r_y1": 586.315, "r_x2": 122.64, "r_y2": 577.763, "r_x3": 50.112, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "its 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Fig. 1 shows the ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.173, "r_x1": 545.115, "r_y1": 388.173, "r_x2": 545.115, "r_y2": 379.621, "r_x3": 308.862, "r_y3": 379.621, "coord_origin": "TOPLEFT"}, "text": "pearance style of PubTabNet. Depending on its complexity,", "orig": "pearance style of PubTabNet. Depending on its complexity,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.128, "r_x1": 545.115, "r_y1": 400.128, "r_x2": 545.115, "r_y2": 391.576, "r_x3": 308.862, "r_y3": 391.576, "coord_origin": "TOPLEFT"}, "text": "a table is characterized as 'simple' when it does not contain", "orig": "a table is characterized as 'simple' when it does not contain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.083, "r_x1": 545.115, "r_y1": 412.083, "r_x2": 545.115, "r_y2": 403.531, "r_x3": 308.862, "r_y3": 403.531, "coord_origin": "TOPLEFT"}, "text": "row spans or column spans, otherwise it is 'complex'. The", "orig": "row spans or column spans, otherwise it is 'complex'. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.038, "r_x1": 545.115, "r_y1": 424.038, "r_x2": 545.115, "r_y2": 415.486, "r_x3": 308.862, "r_y3": 415.486, "coord_origin": "TOPLEFT"}, "text": "dataset is divided into Train and Val splits (roughly 98% and", "orig": "dataset is divided into Train and Val splits (roughly 98% and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.994, "r_x1": 545.115, "r_y1": 435.994, "r_x2": 545.115, "r_y2": 427.442, "r_x3": 308.862, "r_y3": 427.442, "coord_origin": "TOPLEFT"}, "text": "2%). The Train split consists of 54% simple and 46% com-", "orig": "2%). The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized text and the bounding boxes on a table cell basis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 519.6800000000001, "r_x1": 545.115, "r_y1": 519.6800000000001, "r_x2": 545.115, "r_y2": 511.128, "r_x3": 308.862, "r_y3": 511.128, "coord_origin": "TOPLEFT"}, "text": "The dataset is divided into Train, Test and Val splits (81%,", "orig": "The dataset is divided into Train, Test and Val splits (81%,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.635, "r_x1": 545.115, "r_y1": 531.635, "r_x2": 545.115, "r_y2": 523.0830000000001, "r_x3": 308.862, "r_y3": 523.0830000000001, "coord_origin": "TOPLEFT"}, "text": "9.5%, 9.5%), and each one is almost equally divided into", "orig": "9.5%, 9.5%), and each one is almost equally divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.59, "r_x1": 545.115, "r_y1": 543.59, "r_x2": 545.115, "r_y2": 535.038, "r_x3": 308.862, "r_y3": 535.038, "coord_origin": "TOPLEFT"}, "text": "simple and complex tables (Train: 48% simple, 52% com-", "orig": "simple and complex tables (Train: 48% simple, 52% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.545, "r_x1": 350.964, "r_y1": 555.545, "r_x2": 350.964, "r_y2": 546.9929999999999, "r_x3": 308.862, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "plex, Test:", "orig": "plex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.467, "r_y0": 555.545, "r_x1": 490.082, "r_y1": 555.545, "r_x2": 490.082, "r_y2": 546.9929999999999, "r_x3": 355.467, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "48% simple, 52% complex, Test:", "orig": "48% simple, 52% complex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.595, "r_y0": 555.545, "r_x1": 545.115, "r_y1": 555.545, "r_x2": 545.115, "r_y2": 546.9929999999999, "r_x3": 494.595, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "53% simple,", "orig": "53% simple,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.501, "r_x1": 370.6, "r_y1": 567.501, "r_x2": 370.6, "r_y2": 558.9490000000001, "r_x3": 308.862, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "47% complex).", "orig": "47% complex).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.731, "r_y0": 603.366, "r_x1": 545.115, "r_y1": 603.366, "r_x2": 545.115, "r_y2": 594.814, "r_x3": 407.731, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "The entire dataset consists of sim-", "orig": "The entire dataset consists of sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 615.321, "r_x1": 545.115, "r_y1": 615.321, "r_x2": 545.115, "r_y2": 606.769, "r_x3": 308.862, "r_y3": 606.769, "coord_origin": "TOPLEFT"}, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "orig": "ple tables and it is divided into 90% Train, 3% Test and 7%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 627.2760000000001, "r_x1": 348.164, "r_y1": 627.2760000000001, "r_x2": 348.164, "r_y2": 618.7239999999999, "r_x3": 308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.783, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 250.151, "b": 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For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. Additionally, OCR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 239.50199999999995, "r_x1": 286.365, "r_y1": 239.50199999999995, "r_x2": 286.365, "r_y2": 230.95000000000005, "r_x3": 50.112, "r_y3": 230.95000000000005, "coord_origin": "TOPLEFT"}, "text": "can be completely omitted if the tables originate from pro-", "orig": "can be completely omitted if the tables originate from pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 251.457, "r_x1": 286.365, "r_y1": 251.457, "r_x2": 286.365, "r_y2": 242.90499999999997, "r_x3": 50.112, "r_y3": 242.90499999999997, "coord_origin": "TOPLEFT"}, "text": "grammatic PDF documents with known positions of each", "orig": "grammatic PDF documents with known positions of each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 263.413, "r_x1": 66.989, "r_y1": 263.413, "r_x2": 66.989, "r_y2": 254.861, "r_x3": 50.112, "r_y3": 254.861, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.382, "r_y0": 263.413, "r_x1": 286.365, "r_y1": 263.413, "r_x2": 286.365, "r_y2": 254.861, "r_x3": 71.382, "r_y3": 254.861, "coord_origin": "TOPLEFT"}, "text": "The latter was the inspiration for the work of this pa-", "orig": "The latter was the inspiration for the work of this pa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 275.36800000000005, "r_x1": 64.777, "r_y1": 275.36800000000005, "r_x2": 64.777, "r_y2": 266.81600000000003, "r_x3": 50.112, "r_y3": 266.81600000000003, "coord_origin": "TOPLEFT"}, "text": "per.", "orig": "per.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. 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"coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.68, "r_y0": 299.335, "r_x1": 286.365, "r_y1": 299.335, "r_x2": 286.365, "r_y2": 290.783, "r_x3": 263.68, "r_y3": 290.783, "coord_origin": "TOPLEFT"}, "text": "table-", "orig": "table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.29, "r_x1": 84.971, "r_y1": 311.29, "r_x2": 84.971, "r_y2": 302.738, "r_x3": 50.112, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 88.986, "r_y0": 311.29, "r_x1": 131.168, "r_y1": 311.29, "r_x2": 131.168, "r_y2": 302.738, "r_x3": 88.986, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "extraction.", "orig": "extraction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.849, "r_y0": 311.29, "r_x1": 286.365, "r_y1": 311.29, "r_x2": 286.365, "r_y2": 302.738, "r_x3": 138.849, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "Note that one table cell can consti-", "orig": "Note that one table cell can consti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.245, "r_x1": 286.365, "r_y1": 323.245, "r_x2": 286.365, "r_y2": 314.693, "r_x3": 50.112, "r_y3": 314.693, "coord_origin": "TOPLEFT"}, "text": "tute out of multiple text-cells. To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.11, "r_x1": 286.365, "r_y1": 359.11, "r_x2": 286.365, "r_y2": 350.558, "r_x3": 50.112, "r_y3": 350.558, "coord_origin": "TOPLEFT"}, "text": "node is then associated with en embedding vector coming", "orig": "node is then associated with en embedding vector coming", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.066, "r_x1": 286.365, "r_y1": 371.066, "r_x2": 286.365, "r_y2": 362.514, "r_x3": 50.112, "r_y3": 362.514, "coord_origin": "TOPLEFT"}, "text": "from the encoded image, its coordinates and the encoded", "orig": "from the encoded image, its coordinates and the encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.021, "r_x1": 67.397, "r_y1": 383.021, "r_x2": 67.397, "r_y2": 374.469, "r_x3": 50.112, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "text.", "orig": "text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 72.976, "r_y0": 383.021, "r_x1": 286.365, "r_y1": 383.021, "r_x2": 286.365, "r_y2": 374.469, "r_x3": 72.976, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "Furthermore, nodes that represent adjacent text-cells", "orig": "Furthermore, nodes that represent adjacent text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 394.976, "r_x1": 92.602, "r_y1": 394.976, "r_x2": 92.602, "r_y2": 386.424, "r_x3": 50.112, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "are linked.", "orig": "are linked.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.305, "r_y0": 394.976, "r_x1": 286.365, "r_y1": 394.976, "r_x2": 286.365, "r_y2": 386.424, "r_x3": 97.305, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "Graph Convolutional Networks (GCN's) based", "orig": "Graph Convolutional Networks (GCN's) based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 406.931, "r_x1": 286.365, "r_y1": 406.931, "r_x2": 286.365, "r_y2": 398.379, "r_x3": 50.112, "r_y3": 398.379, "coord_origin": "TOPLEFT"}, "text": "methods take the image as an input, but also the position of", "orig": "methods take the image as an input, but also the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 418.886, "r_x1": 286.365, "r_y1": 418.886, "r_x2": 286.365, "r_y2": 410.334, "r_x3": 50.112, "r_y3": 410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 493.689, "r": 286.366, "b": 622.181, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.882, "r_y0": 502.629, "r_x1": 255.652, "r_y1": 502.629, "r_x2": 255.652, "r_y2": 494.077, "r_x3": 252.882, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.63, "r_y0": 502.629, "r_x1": 286.366, "r_y1": 502.629, "r_x2": 286.366, "r_y2": 494.077, "r_x3": 258.63, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": "Apop-", "orig": "Apop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 514.5840000000001, "r_x1": 286.365, "r_y1": 514.5840000000001, "r_x2": 286.365, "r_y2": 506.032, "r_x3": 50.112, "r_y3": 506.032, "coord_origin": "TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 574.36, "r_x1": 59.527, "r_y1": 574.36, "r_x2": 59.527, "r_y2": 565.808, "r_x3": 50.112, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "its", "orig": "its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.259, "r_y0": 574.36, "r_x1": 95.8, "r_y1": 574.36, "r_x2": 95.8, "r_y2": 565.808, "r_x3": 64.259, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "images)", "orig": "images)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 100.523, "r_y0": 574.36, "r_x1": 145.105, "r_y1": 574.36, "r_x2": 145.105, "r_y2": 565.808, "r_x3": 100.523, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "and finally", "orig": "and finally", "text_direction": 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table-structure.", "orig": "its table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.482, "r_y0": 586.315, "r_x1": 286.365, "r_y1": 586.315, "r_x2": 286.365, "r_y2": 577.763, "r_x3": 127.482, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "Currently, this approach achieves state-", "orig": "Currently, this approach achieves state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 598.27, "r_x1": 87.731, "r_y1": 598.27, "r_x2": 87.731, "r_y2": 589.718, "r_x3": 50.112, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "of-the-art", "orig": "of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.074, "r_y0": 598.27, "r_x1": 120.577, "r_y1": 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In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.112, "t": 635.9449999999999, "r": 105.225, "b": 646.693, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 646.693, "r_x1": 105.225, "r_y1": 646.693, "r_x2": 105.225, "r_y2": 635.9449999999999, "r_x3": 50.112, "r_y3": 635.9449999999999, "coord_origin": "TOPLEFT"}, "text": "3. Datasets", "orig": "3. Datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 656.694, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 62.067, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "We rely on large-scale datasets such as PubTabNet [37],", "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "orig": "FinTabNet [36], and TableBank [17] datasets to train and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 133.479, "r_y1": 689.156, "r_x2": 133.479, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "evaluate our models.", "orig": "evaluate our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.271, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 138.271, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "These datasets span over various ap-", "orig": "These datasets span over various ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 166.246, "r_y1": 701.111, "r_x2": 166.246, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "pearance styles and content.", "orig": "pearance styles and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.688, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 173.688, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "We also introduce our own", "orig": "We also introduce our own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "synthetically generated SynthTabNet dataset to fix an im-", "orig": "synthetically generated SynthTabNet dataset to fix an im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. 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null}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 268.105, "r": 545.115, "b": 288.612, "coord_origin": "TOPLEFT"}, "confidence": 0.9667505025863647, "cells": [{"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 276.65700000000004, "r_x1": 346.062, "r_y1": 276.65700000000004, "r_x2": 346.062, "r_y2": 268.105, "r_x3": 308.862, "r_y3": 268.105, "coord_origin": "TOPLEFT"}, "text": "Figure 2:", "orig": "Figure 2:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.491, "r_y0": 276.65700000000004, "r_x1": 545.115, "r_y1": 276.65700000000004, "r_x2": 545.115, "r_y2": 268.105, "r_x3": 354.491, "r_y3": 268.105, "coord_origin": "TOPLEFT"}, "text": "Distribution of the tables across different table", "orig": "Distribution of the tables across different table", "text_direction": 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308.862, "r_y3": 317.742, "coord_origin": "TOPLEFT"}, "text": "balance in the previous datasets.", "orig": "balance in the previous datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.8, "r": 545.115, "b": 627.2760000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 340.352, "r_x1": 545.115, "r_y1": 340.352, "r_x2": 545.115, "r_y2": 331.8, "r_x3": 320.817, "r_y3": 331.8, "coord_origin": "TOPLEFT"}, "text": "The PubTabNet dataset contains 509k tables delivered as", "orig": "The PubTabNet dataset contains 509k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 352.307, "r_x1": 545.115, "r_y1": 352.307, "r_x2": 545.115, "r_y2": 343.755, "r_x3": 308.862, "r_y3": 343.755, "coord_origin": "TOPLEFT"}, "text": "annotated PNG images. The annotations consist of the table", "orig": "annotated PNG images. The annotations consist of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 364.263, "r_x1": 545.115, "r_y1": 364.263, "r_x2": 545.115, "r_y2": 355.711, "r_x3": 308.862, "r_y3": 355.711, "coord_origin": "TOPLEFT"}, "text": "structure represented in HTML format, the tokenized text", "orig": "structure represented in HTML format, the tokenized text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.218, "r_x1": 545.115, "r_y1": 376.218, "r_x2": 545.115, "r_y2": 367.666, "r_x3": 308.862, "r_y3": 367.666, "coord_origin": "TOPLEFT"}, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "orig": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.173, "r_x1": 545.115, "r_y1": 388.173, "r_x2": 545.115, "r_y2": 379.621, "r_x3": 308.862, "r_y3": 379.621, "coord_origin": "TOPLEFT"}, "text": "pearance style of PubTabNet. Depending on its complexity,", "orig": "pearance style of PubTabNet. Depending on its complexity,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.128, "r_x1": 545.115, "r_y1": 400.128, "r_x2": 545.115, "r_y2": 391.576, "r_x3": 308.862, "r_y3": 391.576, "coord_origin": "TOPLEFT"}, "text": "a table is characterized as 'simple' when it does not contain", "orig": "a table is characterized as 'simple' when it does not contain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.083, "r_x1": 545.115, "r_y1": 412.083, "r_x2": 545.115, "r_y2": 403.531, "r_x3": 308.862, "r_y3": 403.531, "coord_origin": "TOPLEFT"}, "text": "row spans or column spans, otherwise it is 'complex'. The", "orig": "row spans or column spans, otherwise it is 'complex'. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.038, "r_x1": 545.115, "r_y1": 424.038, "r_x2": 545.115, "r_y2": 415.486, "r_x3": 308.862, "r_y3": 415.486, "coord_origin": "TOPLEFT"}, "text": "dataset is divided into Train and Val splits (roughly 98% and", "orig": "dataset is divided into Train and Val splits (roughly 98% and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.994, "r_x1": 545.115, "r_y1": 435.994, "r_x2": 545.115, "r_y2": 427.442, "r_x3": 308.862, "r_y3": 427.442, "coord_origin": "TOPLEFT"}, "text": "2%). The Train split consists of 54% simple and 46% com-", "orig": "2%). The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized text and the bounding boxes on a table cell basis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 519.6800000000001, "r_x1": 545.115, "r_y1": 519.6800000000001, "r_x2": 545.115, "r_y2": 511.128, "r_x3": 308.862, "r_y3": 511.128, "coord_origin": "TOPLEFT"}, "text": "The dataset is divided into Train, Test and Val splits (81%,", "orig": "The dataset is divided into Train, Test and Val splits (81%,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.635, "r_x1": 545.115, "r_y1": 531.635, "r_x2": 545.115, "r_y2": 523.0830000000001, "r_x3": 308.862, "r_y3": 523.0830000000001, "coord_origin": "TOPLEFT"}, "text": "9.5%, 9.5%), and each one is almost equally divided into", "orig": "9.5%, 9.5%), and each one is almost equally divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.59, "r_x1": 545.115, "r_y1": 543.59, "r_x2": 545.115, "r_y2": 535.038, "r_x3": 308.862, "r_y3": 535.038, "coord_origin": "TOPLEFT"}, "text": "simple and complex tables (Train: 48% simple, 52% com-", "orig": "simple and complex tables (Train: 48% simple, 52% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.545, "r_x1": 350.964, "r_y1": 555.545, "r_x2": 350.964, "r_y2": 546.9929999999999, "r_x3": 308.862, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "plex, Test:", "orig": "plex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.467, "r_y0": 555.545, "r_x1": 490.082, "r_y1": 555.545, "r_x2": 490.082, "r_y2": 546.9929999999999, "r_x3": 355.467, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "48% simple, 52% complex, Test:", "orig": "48% simple, 52% complex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.595, "r_y0": 555.545, "r_x1": 545.115, "r_y1": 555.545, "r_x2": 545.115, "r_y2": 546.9929999999999, "r_x3": 494.595, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "53% simple,", "orig": "53% simple,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.501, "r_x1": 370.6, "r_y1": 567.501, "r_x2": 370.6, "r_y2": 558.9490000000001, "r_x3": 308.862, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "47% complex).", "orig": "47% complex).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.731, "r_y0": 603.366, "r_x1": 545.115, "r_y1": 603.366, "r_x2": 545.115, "r_y2": 594.814, "r_x3": 407.731, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "The entire dataset consists of sim-", "orig": "The entire dataset consists of sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 615.321, "r_x1": 545.115, "r_y1": 615.321, "r_x2": 545.115, "r_y2": 606.769, "r_x3": 308.862, "r_y3": 606.769, "coord_origin": "TOPLEFT"}, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "orig": "ple tables and it is divided into 90% Train, 3% Test and 7%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 627.2760000000001, "r_x1": 348.164, "r_y1": 627.2760000000001, "r_x2": 348.164, "r_y2": 618.7239999999999, "r_x3": 308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.783, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 250.151, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.8767216801643372, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 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111.399, "r_x3": 50.112, "r_y3": 111.399, "coord_origin": "TOPLEFT"}, "text": "recognition (OCR) to obtain the content of the table-cells.", "orig": "recognition (OCR) to obtain the content of the table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.90599999999995, "r_x1": 286.365, "r_y1": 131.90599999999995, "r_x2": 286.365, "r_y2": 123.35400000000004, "r_x3": 50.112, "r_y3": 123.35400000000004, "coord_origin": "TOPLEFT"}, "text": "In the case of IETD, this OCR engine is implicit in the de-", "orig": "In the case of IETD, this OCR engine is implicit in the de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.861, "r_x1": 286.365, "r_y1": 143.861, "r_x2": 286.365, "r_y2": 135.30899999999997, "r_x3": 50.112, "r_y3": 135.30899999999997, 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For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. Additionally, OCR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 239.50199999999995, "r_x1": 286.365, "r_y1": 239.50199999999995, "r_x2": 286.365, "r_y2": 230.95000000000005, "r_x3": 50.112, "r_y3": 230.95000000000005, "coord_origin": "TOPLEFT"}, "text": "can be completely omitted if the tables originate from pro-", "orig": "can be completely omitted if the tables originate from pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 251.457, "r_x1": 286.365, "r_y1": 251.457, "r_x2": 286.365, "r_y2": 242.90499999999997, "r_x3": 50.112, "r_y3": 242.90499999999997, "coord_origin": "TOPLEFT"}, "text": "grammatic PDF documents with known positions of each", "orig": "grammatic PDF documents with known positions of each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 263.413, "r_x1": 66.989, "r_y1": 263.413, "r_x2": 66.989, "r_y2": 254.861, "r_x3": 50.112, "r_y3": 254.861, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.382, "r_y0": 263.413, "r_x1": 286.365, "r_y1": 263.413, "r_x2": 286.365, "r_y2": 254.861, "r_x3": 71.382, "r_y3": 254.861, "coord_origin": "TOPLEFT"}, "text": "The latter was the inspiration for the work of this pa-", "orig": "The latter was the inspiration for the work of this pa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 275.36800000000005, "r_x1": 64.777, "r_y1": 275.36800000000005, "r_x2": 64.777, "r_y2": 266.81600000000003, "r_x3": 50.112, "r_y3": 266.81600000000003, "coord_origin": "TOPLEFT"}, "text": "per.", "orig": "per.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. 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302.738, "coord_origin": "TOPLEFT"}, "text": "extraction.", "orig": "extraction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.849, "r_y0": 311.29, "r_x1": 286.365, "r_y1": 311.29, "r_x2": 286.365, "r_y2": 302.738, "r_x3": 138.849, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "Note that one table cell can consti-", "orig": "Note that one table cell can consti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.245, "r_x1": 286.365, "r_y1": 323.245, "r_x2": 286.365, "r_y2": 314.693, "r_x3": 50.112, "r_y3": 314.693, "coord_origin": "TOPLEFT"}, "text": "tute out of multiple text-cells. To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.11, "r_x1": 286.365, "r_y1": 359.11, "r_x2": 286.365, "r_y2": 350.558, "r_x3": 50.112, "r_y3": 350.558, "coord_origin": "TOPLEFT"}, "text": "node is then associated with en embedding vector coming", "orig": "node is then associated with en embedding vector coming", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.066, "r_x1": 286.365, "r_y1": 371.066, "r_x2": 286.365, "r_y2": 362.514, "r_x3": 50.112, "r_y3": 362.514, "coord_origin": "TOPLEFT"}, "text": "from the encoded image, its coordinates and the encoded", "orig": "from the encoded image, its coordinates and the encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.021, "r_x1": 67.397, "r_y1": 383.021, "r_x2": 67.397, "r_y2": 374.469, 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410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 493.689, "r": 286.366, "b": 622.181, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.882, "r_y0": 502.629, "r_x1": 255.652, "r_y1": 502.629, "r_x2": 255.652, "r_y2": 494.077, "r_x3": 252.882, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.63, "r_y0": 502.629, "r_x1": 286.366, "r_y1": 502.629, "r_x2": 286.366, "r_y2": 494.077, "r_x3": 258.63, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": "Apop-", "orig": "Apop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 514.5840000000001, "r_x1": 286.365, "r_y1": 514.5840000000001, "r_x2": 286.365, "r_y2": 506.032, "r_x3": 50.112, "r_y3": 506.032, "coord_origin": "TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 574.36, "r_x1": 59.527, "r_y1": 574.36, "r_x2": 59.527, "r_y2": 565.808, "r_x3": 50.112, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "its", "orig": "its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.259, "r_y0": 574.36, "r_x1": 95.8, "r_y1": 574.36, "r_x2": 95.8, "r_y2": 565.808, "r_x3": 64.259, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "images)", "orig": "images)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 100.523, "r_y0": 574.36, "r_x1": 145.105, "r_y1": 574.36, "r_x2": 145.105, "r_y2": 565.808, "r_x3": 100.523, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "and finally", "orig": "and finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.828, "r_y0": 574.36, "r_x1": 166.983, "r_y1": 574.36, "r_x2": 166.983, "r_y2": 565.808, "r_x3": 149.828, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "uses", "orig": "uses", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.715, "r_y0": 574.36, "r_x1": 205.768, "r_y1": 574.36, "r_x2": 205.768, "r_y2": 565.808, "r_x3": 171.715, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "different", "orig": "different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.5, "r_y0": 574.36, "r_x1": 244.243, "r_y1": 574.36, "r_x2": 244.243, "r_y2": 565.808, "r_x3": 210.5, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "rule-sets", "orig": "rule-sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.975, "r_y0": 574.36, "r_x1": 256.726, "r_y1": 574.36, "r_x2": 256.726, "r_y2": 565.808, "r_x3": 248.975, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.459, "r_y0": 574.36, "r_x1": 286.365, "r_y1": 574.36, "r_x2": 286.365, "r_y2": 565.808, "r_x3": 261.459, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "obtain", "orig": "obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.315, "r_x1": 122.64, "r_y1": 586.315, "r_x2": 122.64, "r_y2": 577.763, "r_x3": 50.112, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "its table-structure.", "orig": "its table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.482, "r_y0": 586.315, "r_x1": 286.365, "r_y1": 586.315, "r_x2": 286.365, "r_y2": 577.763, "r_x3": 127.482, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "Currently, this approach achieves state-", "orig": "Currently, this approach achieves state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 598.27, "r_x1": 87.731, "r_y1": 598.27, "r_x2": 87.731, "r_y2": 589.718, "r_x3": 50.112, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "of-the-art", "orig": "of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.074, "r_y0": 598.27, "r_x1": 120.577, "r_y1": 598.27, "r_x2": 120.577, "r_y2": 589.718, "r_x3": 92.074, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "results,", "orig": "results,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.389, "r_y0": 598.27, "r_x1": 137.922, "r_y1": 598.27, "r_x2": 137.922, "r_y2": 589.718, "r_x3": 125.389, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "but", "orig": "but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.266, "r_y0": 598.27, "r_x1": 148.911, "r_y1": 598.27, "r_x2": 148.911, "r_y2": 589.718, "r_x3": 142.266, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.255, "r_y0": 598.27, "r_x1": 165.987, "r_y1": 598.27, "r_x2": 165.987, "r_y2": 589.718, "r_x3": 153.255, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "not", "orig": "not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.331, "r_y0": 598.27, "r_x1": 286.365, "r_y1": 598.27, "r_x2": 286.365, "r_y2": 589.718, "r_x3": 170.331, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "an end-to-end deep-learning", "orig": "an end-to-end deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 610.225, "r_x1": 82.49, "r_y1": 610.225, "r_x2": 82.49, "r_y2": 601.673, "r_x3": 50.112, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.173, "r_y0": 610.225, "r_x1": 286.365, "r_y1": 610.225, "r_x2": 286.365, "r_y2": 601.673, "r_x3": 87.173, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "As such, new rules need to be written if different", "orig": "As such, new rules need to be written if different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 622.181, "r_x1": 175.989, "r_y1": 622.181, "r_x2": 175.989, "r_y2": 613.629, "r_x3": 50.112, "r_y3": 613.629, "coord_origin": "TOPLEFT"}, "text": "types of tables are encountered.", "orig": "types of tables are encountered.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.112, "t": 635.9449999999999, "r": 105.225, "b": 646.693, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 646.693, "r_x1": 105.225, "r_y1": 646.693, "r_x2": 105.225, "r_y2": 635.9449999999999, "r_x3": 50.112, "r_y3": 635.9449999999999, "coord_origin": "TOPLEFT"}, "text": "3. Datasets", "orig": "3. Datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 656.694, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 62.067, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "We rely on large-scale datasets such as PubTabNet [37],", "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "orig": "FinTabNet [36], and TableBank [17] datasets to train and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 133.479, "r_y1": 689.156, "r_x2": 133.479, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "evaluate our models.", "orig": "evaluate our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.271, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 138.271, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "These datasets span over various ap-", "orig": "These datasets span over various ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 166.246, "r_y1": 701.111, "r_x2": 166.246, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "pearance styles and content.", "orig": "pearance styles and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.688, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 173.688, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "We also introduce our own", "orig": "We also introduce our own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "synthetically generated SynthTabNet dataset to fix an im-", "orig": "synthetically generated SynthTabNet dataset to fix an im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. 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The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized text and the bounding boxes on a table cell basis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 519.6800000000001, "r_x1": 545.115, "r_y1": 519.6800000000001, "r_x2": 545.115, "r_y2": 511.128, "r_x3": 308.862, "r_y3": 511.128, "coord_origin": "TOPLEFT"}, "text": "The dataset is divided into Train, Test and Val splits (81%,", "orig": "The dataset is divided into Train, Test and Val splits (81%,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.635, "r_x1": 545.115, "r_y1": 531.635, "r_x2": 545.115, "r_y2": 523.0830000000001, "r_x3": 308.862, "r_y3": 523.0830000000001, "coord_origin": "TOPLEFT"}, "text": "9.5%, 9.5%), and each one is almost equally divided into", "orig": "9.5%, 9.5%), and each one is almost equally divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.59, "r_x1": 545.115, "r_y1": 543.59, "r_x2": 545.115, "r_y2": 535.038, "r_x3": 308.862, "r_y3": 535.038, "coord_origin": "TOPLEFT"}, "text": "simple and complex tables (Train: 48% simple, 52% com-", "orig": "simple and complex tables (Train: 48% simple, 52% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.545, "r_x1": 350.964, "r_y1": 555.545, "r_x2": 350.964, "r_y2": 546.9929999999999, "r_x3": 308.862, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "plex, Test:", "orig": "plex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.467, "r_y0": 555.545, "r_x1": 490.082, "r_y1": 555.545, "r_x2": 490.082, "r_y2": 546.9929999999999, "r_x3": 355.467, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "48% simple, 52% complex, Test:", "orig": "48% simple, 52% complex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.595, "r_y0": 555.545, "r_x1": 545.115, "r_y1": 555.545, "r_x2": 545.115, "r_y2": 546.9929999999999, "r_x3": 494.595, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "53% simple,", "orig": "53% simple,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.501, "r_x1": 370.6, "r_y1": 567.501, "r_x2": 370.6, "r_y2": 558.9490000000001, "r_x3": 308.862, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "47% complex).", "orig": "47% complex).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.731, "r_y0": 603.366, "r_x1": 545.115, "r_y1": 603.366, "r_x2": 545.115, "r_y2": 594.814, "r_x3": 407.731, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "The entire dataset consists of sim-", "orig": "The entire dataset consists of sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 615.321, "r_x1": 545.115, "r_y1": 615.321, "r_x2": 545.115, "r_y2": 606.769, "r_x3": 308.862, "r_y3": 606.769, "coord_origin": "TOPLEFT"}, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "orig": "ple tables and it is divided into 90% Train, 3% Test and 7%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 627.2760000000001, "r_x1": 348.164, "r_y1": 627.2760000000001, "r_x2": 348.164, "r_y2": 618.7239999999999, "r_x3": 308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.783, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "amount of such tables, and kept only those ones ranging", "orig": "amount of such tables, and kept only those ones ranging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 212.283, "r_y1": 95.98400000000004, "r_x2": 212.283, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "between 1*1 and 20*10 (rows/columns).", "orig": "between 1*1 and 20*10 (rows/columns).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 109.78099999999995, "r_x1": 286.365, "r_y1": 109.78099999999995, "r_x2": 286.365, "r_y2": 101.22900000000004, "r_x3": 62.067, "r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 121.73599999999999, "r_x1": 176.458, "r_y1": 121.73599999999999, "r_x2": 176.458, "r_y2": 113.18399999999997, "r_x3": 50.112, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "is essential to train our models.", "orig": "is essential to train our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.971, "r_y0": 121.73599999999999, "r_x1": 286.365, "r_y1": 121.73599999999999, "r_x2": 286.365, "r_y2": 113.18399999999997, "r_x3": 180.971, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "In order to distinguish be-", "orig": "In order to distinguish be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 133.69100000000003, "r_x1": 286.365, "r_y1": 133.69100000000003, "r_x2": 286.365, "r_y2": 125.13900000000001, "r_x3": 50.112, "r_y3": 125.13900000000001, "coord_origin": "TOPLEFT"}, "text": "tween empty and non-empty bounding boxes, we have in-", "orig": "tween empty and non-empty bounding boxes, we have in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 145.64700000000005, "r_x1": 286.365, "r_y1": 145.64700000000005, "r_x2": 286.365, "r_y2": 137.09500000000003, "r_x3": 50.112, "r_y3": 137.09500000000003, "coord_origin": "TOPLEFT"}, "text": "troduced a binary class in the annotation. Unfortunately, the", "orig": "troduced a binary class in the annotation. Unfortunately, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 157.60199999999998, "r_x1": 286.365, "r_y1": 157.60199999999998, "r_x2": 286.365, "r_y2": 149.04999999999995, "r_x3": 50.112, "r_y3": 149.04999999999995, "coord_origin": "TOPLEFT"}, "text": "original datasets either omit the bounding boxes for whole", "orig": "original datasets either omit the bounding boxes for whole", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 169.55700000000002, "r_x1": 94.127, "r_y1": 169.55700000000002, "r_x2": 94.127, "r_y2": 161.005, "r_x3": 50.112, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "tables (e.g.", "orig": "tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.959, "r_y0": 169.55700000000002, "r_x1": 286.365, "r_y1": 169.55700000000002, "r_x2": 286.365, "r_y2": 161.005, "r_x3": 98.959, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "TableBank) or they narrow their scope only to", "orig": "TableBank) or they narrow their scope only to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 181.51199999999994, "r_x1": 116.951, "r_y1": 181.51199999999994, "r_x2": 116.951, "r_y2": 172.96000000000004, "r_x3": 50.112, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "non-empty cells.", "orig": "non-empty cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 121.345, "r_y0": 181.51199999999994, "r_x1": 286.365, "r_y1": 181.51199999999994, "r_x2": 286.365, "r_y2": 172.96000000000004, "r_x3": 121.345, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Therefore, it was imperative to introduce", "orig": "Therefore, it was imperative to introduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 193.46699999999998, "r_x1": 286.365, "r_y1": 193.46699999999998, "r_x2": 286.365, "r_y2": 184.91499999999996, "r_x3": 50.112, "r_y3": 184.91499999999996, "coord_origin": "TOPLEFT"}, "text": "a data pre-processing procedure that generates the missing", "orig": "a data pre-processing procedure that generates the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 205.423, "r_x1": 286.365, "r_y1": 205.423, "r_x2": 286.365, "r_y2": 196.87099999999998, "r_x3": 50.112, "r_y3": 196.87099999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes out of the annotation information. This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 289.109, "r_x1": 68.652, "r_y1": 289.109, "r_x2": 68.652, "r_y2": 280.557, "r_x3": 50.112, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "bles.", "orig": "bles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.566, "r_y0": 289.109, "r_x1": 286.365, "r_y1": 289.109, "r_x2": 286.365, "r_y2": 280.557, "r_x3": 75.566, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "Regarding FinTabNet, 68% of the simple and 98%", "orig": "Regarding FinTabNet, 68% of the simple and 98%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 301.064, "r_x1": 286.365, "r_y1": 301.064, "r_x2": 286.365, "r_y2": 292.512, "r_x3": 50.112, "r_y3": 292.512, "coord_origin": "TOPLEFT"}, "text": "of the complex tables require the generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, 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Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. The corpora to gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 50.112, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "ate the table text consists of the most frequent terms appear-", "orig": "ate the table text consists of the most frequent terms appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "ing in PubTabNet and FinTabNet together with randomly", "orig": "ing in PubTabNet and FinTabNet together with randomly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 109.519, "r_y1": 689.156, "r_x2": 109.519, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "generated text.", "orig": "generated text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.337, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 115.337, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "The first two synthetic datasets have been", "orig": "The first two synthetic datasets have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "fine-tuned to mimic the appearance of the original datasets", "orig": "fine-tuned to mimic the appearance of the original datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "but encompass more complicated table structures. The third", "orig": "but encompass more complicated table structures. The third", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 412.332, "r_y0": 82.43499999999995, "r_x1": 430.902, "r_y1": 82.43499999999995, "r_x2": 430.902, "r_y2": 73.88300000000004, "r_x3": 412.332, "r_y3": 73.88300000000004, "coord_origin": "TOPLEFT"}, "text": "Tags", "orig": "Tags", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.857, "r_y0": 82.43499999999995, "r_x1": 464.446, "r_y1": 82.43499999999995, "r_x2": 464.446, "r_y2": 73.88300000000004, "r_x3": 442.857, "r_y3": 73.88300000000004, "coord_origin": "TOPLEFT"}, "text": "Bbox", "orig": "Bbox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.786, "r_y0": 82.43499999999995, "r_x1": 494.942, "r_y1": 82.43499999999995, 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Lastly, we have", "orig": "last one contains tables with sparse content. Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 319.153, "r_y0": 497.541, "r_x1": 509.021, "r_y1": 497.541, "r_x2": 509.021, "r_y2": 488.989, "r_x3": 319.153, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "composed of three main components, see Fig.", "orig": "composed of three main components, see Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 515.586, "r_y0": 497.541, "r_x1": 523.058, "r_y1": 497.541, "r_x2": 523.058, "r_y2": 488.989, "r_x3": 515.586, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 497.541, "r_x1": 545.115, "r_y1": 497.541, "r_x2": 545.115, "r_y2": 488.989, "r_x3": 529.623, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.452, "r_x1": 319.93, "r_y1": 521.452, "r_x2": 319.93, "r_y2": 512.9, "r_x3": 308.862, "r_y3": 512.9, "coord_origin": "TOPLEFT"}, "text": "tor", "orig": "tor", "text_direction": 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As for spanning cells, such as row", "orig": "the Cell BBox Decoder. As for spanning cells, such as row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.183, "r_x1": 483.118, "r_y1": 593.183, "r_x2": 483.118, "r_y2": 584.631, "r_x3": 308.862, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "or column span, the tag is broken down to '", "orig": "or column span, the tag is broken down to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.119, "r_y0": 593.019, "r_x1": 490.868, "r_y1": 593.019, "r_x2": 490.868, "r_y2": 584.313, "r_x3": 483.119, "r_y3": 584.313, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.868, "r_y0": 593.183, "r_x1": 545.114, "r_y1": 593.183, "r_x2": 545.114, "r_y2": 584.631, "r_x3": 490.868, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "', 'rowspan='", "orig": "', 'rowspan='", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.138, "r_x1": 545.115, "r_y1": 605.138, "r_x2": 545.115, "r_y2": 596.586, "r_x3": 308.862, "r_y3": 596.586, "coord_origin": "TOPLEFT"}, "text": "or 'colspan=', with the number of spanning cells (attribute),", "orig": "or 'colspan=', with the number of spanning cells (attribute),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.093, "r_x1": 329.644, "r_y1": 617.093, "r_x2": 329.644, "r_y2": 608.5409999999999, "r_x3": 308.862, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "and '", "orig": "and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.646, "r_y0": 616.929, "r_x1": 337.395, "r_y1": 616.929, "r_x2": 337.395, "r_y2": 608.223, "r_x3": 329.646, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.394, "r_y0": 617.093, "r_x1": 343.202, "r_y1": 617.093, "r_x2": 343.202, "r_y2": 608.5409999999999, "r_x3": 337.394, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "'.", "orig": "'.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.064, "r_y0": 617.093, "r_x1": 468.591, "r_y1": 617.093, "r_x2": 468.591, "r_y2": 608.5409999999999, "r_x3": 348.064, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "The hidden state attached to '", "orig": "The hidden state attached to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 468.595, "r_y0": 616.929, "r_x1": 476.344, "r_y1": 616.929, "r_x2": 476.344, "r_y2": 608.223, "r_x3": 468.595, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.344, "r_y0": 617.093, "r_x1": 545.116, "r_y1": 617.093, "r_x2": 545.116, "r_y2": 608.5409999999999, "r_x3": 476.344, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "' is passed to the", "orig": "' is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.048, "r_x1": 545.115, "r_y1": 629.048, "r_x2": 545.115, "r_y2": 620.496, "r_x3": 308.862, "r_y3": 620.496, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "orig": "Cell BBox Decoder. A shared feed forward network (FFN)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.0029999999999, "r_x1": 533.2, "r_y1": 641.0029999999999, "r_x2": 533.2, "r_y2": 632.451, "r_x3": 308.862, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "receives the hidden states from the Structure Decoder,", "orig": "receives the hidden states from the Structure Decoder,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.364, "r_y0": 641.0029999999999, "r_x1": 545.115, "r_y1": 641.0029999999999, "r_x2": 545.115, "r_y2": 632.451, "r_x3": 537.364, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.9590000000001, "r_x1": 545.115, "r_y1": 652.9590000000001, "r_x2": 545.115, "r_y2": 644.407, "r_x3": 308.862, "r_y3": 644.407, "coord_origin": "TOPLEFT"}, "text": "provide the final detection predictions of the bounding box", "orig": "provide the final detection predictions of the bounding box", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.914, "r_x1": 449.424, "r_y1": 664.914, "r_x2": 449.424, "r_y2": 656.362, "r_x3": 308.862, "r_y3": 656.362, "coord_origin": "TOPLEFT"}, "text": "coordinates and their classification.", "orig": "coordinates and their classification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.217, "r_x1": 431.91, "r_y1": 677.217, "r_x2": 431.91, "r_y2": 668.261, "r_x3": 320.817, "r_y3": 668.261, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network.", "orig": "CNN Backbone Network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.499, "r_y0": 677.201, "r_x1": 545.113, "r_y1": 677.201, "r_x2": 545.113, "r_y2": 668.649, "r_x3": 439.499, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "A ResNet-18 CNN is the", "orig": "A ResNet-18 CNN is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "backbone that receives the table image and encodes it as a", "orig": "backbone that receives the table image and encodes it as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "vector of predefined length. The network has been modified", "orig": "vector of predefined length. The network has been modified", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "by removing the linear and pooling layer, as we are not per-", "orig": "by removing the linear and pooling layer, as we are not per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 95.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9611433148384094, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "amount of such tables, and kept only those ones ranging", "orig": "amount of such tables, and kept only those ones ranging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 212.283, "r_y1": 95.98400000000004, "r_x2": 212.283, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "between 1*1 and 20*10 (rows/columns).", "orig": "between 1*1 and 20*10 (rows/columns).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 101.22900000000004, "r": 286.365, "b": 313.019, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 109.78099999999995, "r_x1": 286.365, "r_y1": 109.78099999999995, "r_x2": 286.365, "r_y2": 101.22900000000004, "r_x3": 62.067, "r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 121.73599999999999, "r_x1": 176.458, "r_y1": 121.73599999999999, "r_x2": 176.458, "r_y2": 113.18399999999997, "r_x3": 50.112, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "is essential to train our models.", "orig": "is essential to train our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.971, "r_y0": 121.73599999999999, "r_x1": 286.365, "r_y1": 121.73599999999999, "r_x2": 286.365, "r_y2": 113.18399999999997, "r_x3": 180.971, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "In order to distinguish be-", "orig": "In order to distinguish be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 133.69100000000003, "r_x1": 286.365, "r_y1": 133.69100000000003, "r_x2": 286.365, "r_y2": 125.13900000000001, "r_x3": 50.112, "r_y3": 125.13900000000001, "coord_origin": "TOPLEFT"}, "text": "tween empty and non-empty bounding boxes, we have in-", "orig": "tween empty and non-empty bounding boxes, we have in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 145.64700000000005, "r_x1": 286.365, "r_y1": 145.64700000000005, "r_x2": 286.365, "r_y2": 137.09500000000003, "r_x3": 50.112, "r_y3": 137.09500000000003, "coord_origin": "TOPLEFT"}, "text": "troduced a binary class in the annotation. Unfortunately, the", "orig": "troduced a binary class in the annotation. Unfortunately, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 157.60199999999998, "r_x1": 286.365, "r_y1": 157.60199999999998, "r_x2": 286.365, "r_y2": 149.04999999999995, "r_x3": 50.112, "r_y3": 149.04999999999995, "coord_origin": "TOPLEFT"}, "text": "original datasets either omit the bounding boxes for whole", "orig": "original datasets either omit the bounding boxes for whole", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 169.55700000000002, "r_x1": 94.127, "r_y1": 169.55700000000002, "r_x2": 94.127, "r_y2": 161.005, "r_x3": 50.112, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "tables (e.g.", "orig": "tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.959, "r_y0": 169.55700000000002, "r_x1": 286.365, "r_y1": 169.55700000000002, "r_x2": 286.365, "r_y2": 161.005, "r_x3": 98.959, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "TableBank) or they narrow their scope only to", "orig": "TableBank) or they narrow their scope only to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 181.51199999999994, "r_x1": 116.951, "r_y1": 181.51199999999994, "r_x2": 116.951, "r_y2": 172.96000000000004, "r_x3": 50.112, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "non-empty cells.", "orig": "non-empty cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 121.345, "r_y0": 181.51199999999994, "r_x1": 286.365, "r_y1": 181.51199999999994, "r_x2": 286.365, "r_y2": 172.96000000000004, "r_x3": 121.345, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Therefore, it was imperative to introduce", "orig": "Therefore, it was imperative to introduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 193.46699999999998, "r_x1": 286.365, "r_y1": 193.46699999999998, "r_x2": 286.365, "r_y2": 184.91499999999996, "r_x3": 50.112, "r_y3": 184.91499999999996, "coord_origin": "TOPLEFT"}, "text": "a data pre-processing procedure that generates the missing", "orig": "a data pre-processing procedure that generates the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 205.423, "r_x1": 286.365, "r_y1": 205.423, "r_x2": 286.365, "r_y2": 196.87099999999998, "r_x3": 50.112, "r_y3": 196.87099999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes out of the annotation information. This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 289.109, "r_x1": 68.652, "r_y1": 289.109, "r_x2": 68.652, "r_y2": 280.557, "r_x3": 50.112, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "bles.", "orig": "bles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.566, "r_y0": 289.109, "r_x1": 286.365, "r_y1": 289.109, "r_x2": 286.365, "r_y2": 280.557, "r_x3": 75.566, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "Regarding FinTabNet, 68% of the simple and 98%", "orig": "Regarding FinTabNet, 68% of the simple and 98%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 301.064, "r_x1": 286.365, "r_y1": 301.064, "r_x2": 286.365, "r_y2": 292.512, "r_x3": 50.112, "r_y3": 292.512, "coord_origin": "TOPLEFT"}, "text": "of the complex tables require the generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 318.264, "r": 286.365, "b": 434.413, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 338.772, "r_x1": 60.075, "r_y1": 338.772, "r_x2": 60.075, "r_y2": 330.22, "r_x3": 50.112, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "all", "orig": "all", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 65.146, "r_y0": 338.772, "r_x1": 96.687, "r_y1": 338.772, "r_x2": 96.687, "r_y2": 330.22, "r_x3": 65.146, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.768, "r_y0": 338.772, "r_x1": 113.932, "r_y1": 338.772, "r_x2": 113.932, "r_y2": 330.22, "r_x3": 101.768, "r_y3": 330.22, 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"r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "simpler", "orig": "simpler", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.836, "r_y0": 338.772, "r_x1": 263.571, "r_y1": 338.772, "r_x2": 263.571, "r_y2": 330.22, "r_x3": 224.836, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "structures", "orig": "structures", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 268.652, "r_y0": 338.772, "r_x1": 286.365, "r_y1": 338.772, "r_x2": 286.365, "r_y2": 330.22, "r_x3": 268.652, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 350.727, "r_x1": 286.365, "r_y1": 350.727, "r_x2": 286.365, "r_y2": 342.175, "r_x3": 50.112, "r_y3": 342.175, "coord_origin": "TOPLEFT"}, "text": "fewer number of rows/columns. Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.658, "r": 286.367, "b": 627.538, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 632.783, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. 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Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 320.817, "t": 298.041, "r": 542.744, "b": 306.593, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 342.208, "r": 545.115, "b": 446.401, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 477.034, "r": 545.116, "b": 664.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": 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488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, 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{"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.4069999999999, "r_x1": 436.194, "r_y1": 533.4069999999999, "r_x2": 436.194, "r_y2": 524.855, "r_x3": 308.862, "r_y3": 524.855, "coord_origin": "TOPLEFT"}, "text": "encoded image is passed to the", "orig": "encoded image is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.526, "r_y0": 533.264, "r_x1": 513.867, "r_y1": 533.264, "r_x2": 513.867, "r_y2": 524.6759999999999, "r_x3": 439.526, "r_y3": 524.6759999999999, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 517.432, "r_y0": 533.4069999999999, "r_x1": 545.108, "r_y1": 533.4069999999999, "r_x2": 545.108, "r_y2": 524.855, "r_x3": 517.432, 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Unfortunately, the", "orig": "troduced a binary class in the annotation. 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This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 289.109, "r_x1": 68.652, "r_y1": 289.109, "r_x2": 68.652, "r_y2": 280.557, "r_x3": 50.112, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "bles.", "orig": "bles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.566, "r_y0": 289.109, "r_x1": 286.365, "r_y1": 289.109, "r_x2": 286.365, "r_y2": 280.557, "r_x3": 75.566, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "Regarding FinTabNet, 68% of the simple and 98%", "orig": "Regarding FinTabNet, 68% of the simple and 98%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 301.064, "r_x1": 286.365, "r_y1": 301.064, "r_x2": 286.365, "r_y2": 292.512, "r_x3": 50.112, "r_y3": 292.512, "coord_origin": "TOPLEFT"}, "text": "of the complex tables require the generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 318.264, "r": 286.365, "b": 434.413, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 338.772, "r_x1": 60.075, "r_y1": 338.772, "r_x2": 60.075, "r_y2": 330.22, "r_x3": 50.112, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "all", "orig": "all", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 65.146, "r_y0": 338.772, "r_x1": 96.687, "r_y1": 338.772, "r_x2": 96.687, "r_y2": 330.22, "r_x3": 65.146, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.768, "r_y0": 338.772, "r_x1": 113.932, "r_y1": 338.772, "r_x2": 113.932, "r_y2": 330.22, "r_x3": 101.768, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.003, "r_y0": 338.772, "r_x1": 148.533, "r_y1": 338.772, "r_x2": 148.533, "r_y2": 330.22, "r_x3": 119.003, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "skewed", "orig": "skewed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.613, "r_y0": 338.772, "r_x1": 184.806, "r_y1": 338.772, "r_x2": 184.806, "r_y2": 330.22, "r_x3": 153.613, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "towards", "orig": "towards", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 189.877, "r_y0": 338.772, "r_x1": 219.765, "r_y1": 338.772, "r_x2": 219.765, "r_y2": 330.22, "r_x3": 189.877, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "simpler", "orig": "simpler", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.836, "r_y0": 338.772, "r_x1": 263.571, "r_y1": 338.772, "r_x2": 263.571, "r_y2": 330.22, "r_x3": 224.836, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "structures", "orig": "structures", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 268.652, "r_y0": 338.772, "r_x1": 286.365, "r_y1": 338.772, "r_x2": 286.365, "r_y2": 330.22, "r_x3": 268.652, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 350.727, "r_x1": 286.365, "r_y1": 350.727, "r_x2": 286.365, "r_y2": 342.175, "r_x3": 50.112, "r_y3": 342.175, "coord_origin": "TOPLEFT"}, "text": "fewer number of rows/columns. Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.658, "r": 286.367, "b": 627.538, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 632.783, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. The corpora to gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 50.112, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "ate the table text consists of the most frequent terms appear-", "orig": "ate the table text consists of the most frequent terms appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "ing in PubTabNet and FinTabNet together with randomly", "orig": "ing in PubTabNet and FinTabNet together with randomly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 109.519, "r_y1": 689.156, "r_x2": 109.519, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "generated text.", "orig": "generated text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.337, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 115.337, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "The first two synthetic datasets have been", "orig": "The first two synthetic datasets have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "fine-tuned to mimic the appearance of the original datasets", "orig": "fine-tuned to mimic the appearance of the original datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "but encompass more complicated table structures. The third", "orig": "but encompass more complicated table structures. The third", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. 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366.276, "r_y2": 203.79499999999996, "r_x3": 308.862, "r_y3": 203.79499999999996, "coord_origin": "TOPLEFT"}, "text": "and Fintabnet.", "orig": "and Fintabnet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.58, "r_y0": 212.34699999999998, "r_x1": 545.115, "r_y1": 212.34699999999998, "r_x2": 545.115, "r_y2": 203.79499999999996, "r_x3": 370.58, "r_y3": 203.79499999999996, "coord_origin": "TOPLEFT"}, "text": "(**) The combined dataset is the processed", "orig": "(**) The combined dataset is the processed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 224.303, "r_x1": 523.935, "r_y1": 224.303, "r_x2": 523.935, "r_y2": 215.75099999999998, "r_x3": 308.862, "r_y3": 215.75099999999998, "coord_origin": "TOPLEFT"}, "text": "combination of PubTabNet, Fintabnet and TableBank.", "orig": "combination of PubTabNet, Fintabnet and TableBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.889, "r": 545.115, "b": 294.306, "coord_origin": "TOPLEFT"}, "confidence": 0.978954017162323, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.44100000000003, "r_x1": 545.115, "r_y1": 258.44100000000003, "r_x2": 545.115, "r_y2": 249.889, "r_x3": 308.862, "r_y3": 249.889, "coord_origin": "TOPLEFT"}, "text": "one adopts a colorful appearance with high contrast and the", "orig": "one adopts a colorful appearance with high contrast and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.39599999999996, "r_x1": 545.115, "r_y1": 270.39599999999996, "r_x2": 545.115, "r_y2": 261.84400000000005, "r_x3": 308.862, "r_y3": 261.84400000000005, "coord_origin": "TOPLEFT"}, "text": "last one contains tables with sparse content. Lastly, we have", "orig": "last one contains tables with sparse content. Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.817, "t": 298.041, "r": 542.744, "b": 306.593, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 342.208, "r": 545.115, "b": 446.401, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 477.034, "r": 545.116, "b": 664.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 319.153, "r_y0": 497.541, "r_x1": 509.021, "r_y1": 497.541, "r_x2": 509.021, "r_y2": 488.989, "r_x3": 319.153, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "composed of three main components, see Fig.", "orig": "composed of three main components, see Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 515.586, "r_y0": 497.541, "r_x1": 523.058, "r_y1": 497.541, "r_x2": 523.058, "r_y2": 488.989, "r_x3": 515.586, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 497.541, "r_x1": 545.115, "r_y1": 497.541, "r_x2": 545.115, "r_y2": 488.989, "r_x3": 529.623, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.452, "r_x1": 319.93, "r_y1": 521.452, 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"text": "to pro-", "orig": "to pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.362, "r_x1": 545.115, "r_y1": 545.362, "r_x2": 545.115, "r_y2": 536.81, "r_x3": 308.862, "r_y3": 536.81, "coord_origin": "TOPLEFT"}, "text": "duce a sequence of HTML tags that represent the structure", "orig": "duce a sequence of HTML tags that represent the structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.317, "r_x1": 358.545, "r_y1": 557.317, "r_x2": 358.545, "r_y2": 548.765, "r_x3": 308.862, "r_y3": 548.765, "coord_origin": "TOPLEFT"}, "text": "of the table.", "orig": "of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.191, 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state of that cell is passed to", "orig": "') the hidden state of that cell is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.2280000000001, "r_x1": 545.115, "r_y1": 581.2280000000001, "r_x2": 545.115, "r_y2": 572.6759999999999, "r_x3": 308.862, "r_y3": 572.6759999999999, "coord_origin": "TOPLEFT"}, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "orig": "the Cell BBox Decoder. As for spanning cells, such as row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.183, "r_x1": 483.118, "r_y1": 593.183, "r_x2": 483.118, "r_y2": 584.631, "r_x3": 308.862, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "or column span, the tag is broken down to '", "orig": "or column span, the tag is broken down to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.119, "r_y0": 593.019, "r_x1": 490.868, "r_y1": 593.019, "r_x2": 490.868, "r_y2": 584.313, "r_x3": 483.119, "r_y3": 584.313, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.868, "r_y0": 593.183, "r_x1": 545.114, "r_y1": 593.183, "r_x2": 545.114, "r_y2": 584.631, "r_x3": 490.868, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "', 'rowspan='", "orig": "', 'rowspan='", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.138, "r_x1": 545.115, "r_y1": 605.138, "r_x2": 545.115, "r_y2": 596.586, "r_x3": 308.862, "r_y3": 596.586, "coord_origin": "TOPLEFT"}, "text": "or 'colspan=', with the number of spanning cells (attribute),", "orig": "or 'colspan=', with the number of spanning cells (attribute),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.093, "r_x1": 329.644, "r_y1": 617.093, "r_x2": 329.644, "r_y2": 608.5409999999999, "r_x3": 308.862, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "and '", "orig": "and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.646, "r_y0": 616.929, "r_x1": 337.395, "r_y1": 616.929, "r_x2": 337.395, "r_y2": 608.223, "r_x3": 329.646, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.394, "r_y0": 617.093, "r_x1": 343.202, "r_y1": 617.093, "r_x2": 343.202, "r_y2": 608.5409999999999, "r_x3": 337.394, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "'.", "orig": "'.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.064, "r_y0": 617.093, "r_x1": 468.591, "r_y1": 617.093, "r_x2": 468.591, "r_y2": 608.5409999999999, "r_x3": 348.064, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "The hidden state attached to '", "orig": "The hidden state attached to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 468.595, "r_y0": 616.929, "r_x1": 476.344, "r_y1": 616.929, "r_x2": 476.344, "r_y2": 608.223, "r_x3": 468.595, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.344, "r_y0": 617.093, "r_x1": 545.116, "r_y1": 617.093, "r_x2": 545.116, "r_y2": 608.5409999999999, "r_x3": 476.344, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "' is passed to the", "orig": "' is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.048, "r_x1": 545.115, "r_y1": 629.048, "r_x2": 545.115, "r_y2": 620.496, "r_x3": 308.862, "r_y3": 620.496, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "orig": "Cell BBox Decoder. A shared feed forward network (FFN)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.0029999999999, "r_x1": 533.2, "r_y1": 641.0029999999999, "r_x2": 533.2, "r_y2": 632.451, "r_x3": 308.862, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "receives the hidden states from the Structure Decoder,", "orig": "receives the hidden states from the Structure Decoder,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.364, "r_y0": 641.0029999999999, "r_x1": 545.115, "r_y1": 641.0029999999999, "r_x2": 545.115, "r_y2": 632.451, "r_x3": 537.364, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.9590000000001, "r_x1": 545.115, "r_y1": 652.9590000000001, "r_x2": 545.115, "r_y2": 644.407, "r_x3": 308.862, "r_y3": 644.407, "coord_origin": "TOPLEFT"}, "text": "provide the final detection predictions of the bounding box", "orig": "provide the final detection predictions of the bounding box", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.914, "r_x1": 449.424, "r_y1": 664.914, "r_x2": 449.424, "r_y2": 656.362, "r_x3": 308.862, "r_y3": 656.362, "coord_origin": "TOPLEFT"}, "text": "coordinates and their classification.", "orig": "coordinates and their classification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 668.261, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.217, "r_x1": 431.91, "r_y1": 677.217, "r_x2": 431.91, "r_y2": 668.261, "r_x3": 320.817, "r_y3": 668.261, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network.", "orig": "CNN Backbone Network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.499, "r_y0": 677.201, "r_x1": 545.113, "r_y1": 677.201, "r_x2": 545.113, "r_y2": 668.649, "r_x3": 439.499, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "A ResNet-18 CNN is the", "orig": "A ResNet-18 CNN is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "backbone that receives the table image and encodes it as a", "orig": "backbone that receives the table image and encodes it as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "vector of predefined length. The network has been modified", "orig": "vector of predefined length. The network has been modified", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "by removing the linear and pooling layer, as we are not per-", "orig": "by removing the linear and pooling layer, as we are not per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8565718531608582, "cells": [{"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}], "body": [{"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 95.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9611433148384094, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "amount of such tables, and kept only those ones ranging", "orig": "amount of such tables, and kept only those ones ranging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 212.283, "r_y1": 95.98400000000004, "r_x2": 212.283, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "between 1*1 and 20*10 (rows/columns).", "orig": "between 1*1 and 20*10 (rows/columns).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 101.22900000000004, "r": 286.365, "b": 313.019, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 109.78099999999995, "r_x1": 286.365, "r_y1": 109.78099999999995, "r_x2": 286.365, "r_y2": 101.22900000000004, "r_x3": 62.067, "r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 121.73599999999999, "r_x1": 176.458, "r_y1": 121.73599999999999, "r_x2": 176.458, "r_y2": 113.18399999999997, "r_x3": 50.112, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "is essential to train our models.", "orig": "is essential to train our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.971, "r_y0": 121.73599999999999, "r_x1": 286.365, "r_y1": 121.73599999999999, "r_x2": 286.365, "r_y2": 113.18399999999997, "r_x3": 180.971, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "In order to distinguish be-", "orig": "In order to distinguish be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 133.69100000000003, "r_x1": 286.365, "r_y1": 133.69100000000003, "r_x2": 286.365, "r_y2": 125.13900000000001, "r_x3": 50.112, "r_y3": 125.13900000000001, "coord_origin": "TOPLEFT"}, "text": "tween empty and non-empty bounding boxes, we have in-", "orig": "tween empty and non-empty bounding boxes, we have in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 145.64700000000005, "r_x1": 286.365, "r_y1": 145.64700000000005, "r_x2": 286.365, "r_y2": 137.09500000000003, "r_x3": 50.112, "r_y3": 137.09500000000003, "coord_origin": "TOPLEFT"}, "text": "troduced a binary class in the annotation. Unfortunately, the", "orig": "troduced a binary class in the annotation. Unfortunately, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 157.60199999999998, "r_x1": 286.365, "r_y1": 157.60199999999998, "r_x2": 286.365, "r_y2": 149.04999999999995, "r_x3": 50.112, "r_y3": 149.04999999999995, "coord_origin": "TOPLEFT"}, "text": "original datasets either omit the bounding boxes for whole", "orig": "original datasets either omit the bounding boxes for whole", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 169.55700000000002, "r_x1": 94.127, "r_y1": 169.55700000000002, "r_x2": 94.127, "r_y2": 161.005, "r_x3": 50.112, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "tables (e.g.", "orig": "tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.959, "r_y0": 169.55700000000002, "r_x1": 286.365, "r_y1": 169.55700000000002, "r_x2": 286.365, "r_y2": 161.005, "r_x3": 98.959, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "TableBank) or they narrow their scope only to", "orig": "TableBank) or they narrow their scope only to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 181.51199999999994, "r_x1": 116.951, "r_y1": 181.51199999999994, "r_x2": 116.951, "r_y2": 172.96000000000004, "r_x3": 50.112, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "non-empty cells.", "orig": "non-empty cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 121.345, "r_y0": 181.51199999999994, "r_x1": 286.365, "r_y1": 181.51199999999994, "r_x2": 286.365, "r_y2": 172.96000000000004, "r_x3": 121.345, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Therefore, it was imperative to introduce", "orig": "Therefore, it was imperative to introduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 193.46699999999998, "r_x1": 286.365, "r_y1": 193.46699999999998, "r_x2": 286.365, "r_y2": 184.91499999999996, "r_x3": 50.112, "r_y3": 184.91499999999996, "coord_origin": "TOPLEFT"}, "text": "a data pre-processing procedure that generates the missing", "orig": "a data pre-processing procedure that generates the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 205.423, "r_x1": 286.365, "r_y1": 205.423, "r_x2": 286.365, "r_y2": 196.87099999999998, "r_x3": 50.112, "r_y3": 196.87099999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes out of the annotation information. This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 289.109, "r_x1": 68.652, "r_y1": 289.109, "r_x2": 68.652, "r_y2": 280.557, "r_x3": 50.112, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "bles.", "orig": "bles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.566, "r_y0": 289.109, "r_x1": 286.365, "r_y1": 289.109, "r_x2": 286.365, "r_y2": 280.557, "r_x3": 75.566, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "Regarding FinTabNet, 68% of the simple and 98%", "orig": "Regarding FinTabNet, 68% of the simple and 98%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 301.064, "r_x1": 286.365, "r_y1": 301.064, "r_x2": 286.365, "r_y2": 292.512, "r_x3": 50.112, "r_y3": 292.512, "coord_origin": "TOPLEFT"}, "text": "of the complex tables require the generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 318.264, "r": 286.365, "b": 434.413, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 338.772, "r_x1": 60.075, "r_y1": 338.772, "r_x2": 60.075, "r_y2": 330.22, "r_x3": 50.112, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "all", "orig": "all", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 65.146, "r_y0": 338.772, "r_x1": 96.687, "r_y1": 338.772, "r_x2": 96.687, "r_y2": 330.22, "r_x3": 65.146, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.768, "r_y0": 338.772, "r_x1": 113.932, "r_y1": 338.772, "r_x2": 113.932, "r_y2": 330.22, "r_x3": 101.768, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.003, "r_y0": 338.772, "r_x1": 148.533, "r_y1": 338.772, "r_x2": 148.533, "r_y2": 330.22, "r_x3": 119.003, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "skewed", "orig": "skewed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.613, "r_y0": 338.772, "r_x1": 184.806, "r_y1": 338.772, "r_x2": 184.806, "r_y2": 330.22, "r_x3": 153.613, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "towards", "orig": "towards", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 189.877, "r_y0": 338.772, "r_x1": 219.765, "r_y1": 338.772, "r_x2": 219.765, "r_y2": 330.22, "r_x3": 189.877, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "simpler", "orig": "simpler", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.836, "r_y0": 338.772, "r_x1": 263.571, "r_y1": 338.772, "r_x2": 263.571, "r_y2": 330.22, "r_x3": 224.836, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "structures", "orig": "structures", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 268.652, "r_y0": 338.772, "r_x1": 286.365, "r_y1": 338.772, "r_x2": 286.365, "r_y2": 330.22, "r_x3": 268.652, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 350.727, "r_x1": 286.365, "r_y1": 350.727, "r_x2": 286.365, "r_y2": 342.175, "r_x3": 50.112, "r_y3": 342.175, "coord_origin": "TOPLEFT"}, "text": "fewer number of rows/columns. Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.658, "r": 286.367, "b": 627.538, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 632.783, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. The corpora to gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 50.112, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "ate the table text consists of the most frequent terms appear-", "orig": "ate the table text consists of the most frequent terms appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "ing in PubTabNet and FinTabNet together with randomly", "orig": "ing in PubTabNet and FinTabNet together with randomly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 109.519, "r_y1": 689.156, "r_x2": 109.519, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "generated text.", "orig": "generated text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.337, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 115.337, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "The first two synthetic datasets have been", "orig": "The first two synthetic datasets have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "fine-tuned to mimic the appearance of the original datasets", "orig": "fine-tuned to mimic the appearance of the original datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "but encompass more complicated table structures. The third", "orig": "but encompass more complicated table structures. The third", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. 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366.276, "r_y2": 203.79499999999996, "r_x3": 308.862, "r_y3": 203.79499999999996, "coord_origin": "TOPLEFT"}, "text": "and Fintabnet.", "orig": "and Fintabnet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.58, "r_y0": 212.34699999999998, "r_x1": 545.115, "r_y1": 212.34699999999998, "r_x2": 545.115, "r_y2": 203.79499999999996, "r_x3": 370.58, "r_y3": 203.79499999999996, "coord_origin": "TOPLEFT"}, "text": "(**) The combined dataset is the processed", "orig": "(**) The combined dataset is the processed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 224.303, "r_x1": 523.935, "r_y1": 224.303, "r_x2": 523.935, "r_y2": 215.75099999999998, "r_x3": 308.862, "r_y3": 215.75099999999998, "coord_origin": "TOPLEFT"}, "text": "combination of PubTabNet, Fintabnet and TableBank.", "orig": "combination of PubTabNet, Fintabnet and TableBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.889, "r": 545.115, "b": 294.306, "coord_origin": "TOPLEFT"}, "confidence": 0.978954017162323, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.44100000000003, "r_x1": 545.115, "r_y1": 258.44100000000003, "r_x2": 545.115, "r_y2": 249.889, "r_x3": 308.862, "r_y3": 249.889, "coord_origin": "TOPLEFT"}, "text": "one adopts a colorful appearance with high contrast and the", "orig": "one adopts a colorful appearance with high contrast and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.39599999999996, "r_x1": 545.115, "r_y1": 270.39599999999996, "r_x2": 545.115, "r_y2": 261.84400000000005, "r_x3": 308.862, "r_y3": 261.84400000000005, "coord_origin": "TOPLEFT"}, "text": "last one contains tables with sparse content. Lastly, we have", "orig": "last one contains tables with sparse content. Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.817, "t": 298.041, "r": 542.744, "b": 306.593, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 342.208, "r": 545.115, "b": 446.401, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 477.034, "r": 545.116, "b": 664.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 319.153, "r_y0": 497.541, "r_x1": 509.021, "r_y1": 497.541, "r_x2": 509.021, "r_y2": 488.989, "r_x3": 319.153, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "composed of three main components, see Fig.", "orig": "composed of three main components, see Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 515.586, "r_y0": 497.541, "r_x1": 523.058, "r_y1": 497.541, "r_x2": 523.058, "r_y2": 488.989, "r_x3": 515.586, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 497.541, "r_x1": 545.115, "r_y1": 497.541, "r_x2": 545.115, "r_y2": 488.989, "r_x3": 529.623, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.452, "r_x1": 319.93, "r_y1": 521.452, 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"text": "to pro-", "orig": "to pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.362, "r_x1": 545.115, "r_y1": 545.362, "r_x2": 545.115, "r_y2": 536.81, "r_x3": 308.862, "r_y3": 536.81, "coord_origin": "TOPLEFT"}, "text": "duce a sequence of HTML tags that represent the structure", "orig": "duce a sequence of HTML tags that represent the structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.317, "r_x1": 358.545, "r_y1": 557.317, "r_x2": 358.545, "r_y2": 548.765, "r_x3": 308.862, "r_y3": 548.765, "coord_origin": "TOPLEFT"}, "text": "of the table.", "orig": "of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.191, 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state of that cell is passed to", "orig": "') the hidden state of that cell is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.2280000000001, "r_x1": 545.115, "r_y1": 581.2280000000001, "r_x2": 545.115, "r_y2": 572.6759999999999, "r_x3": 308.862, "r_y3": 572.6759999999999, "coord_origin": "TOPLEFT"}, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "orig": "the Cell BBox Decoder. As for spanning cells, such as row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.183, "r_x1": 483.118, "r_y1": 593.183, "r_x2": 483.118, "r_y2": 584.631, "r_x3": 308.862, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "or column span, the tag is broken down to '", "orig": "or column span, the tag is broken down to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.119, "r_y0": 593.019, "r_x1": 490.868, "r_y1": 593.019, "r_x2": 490.868, "r_y2": 584.313, "r_x3": 483.119, "r_y3": 584.313, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.868, "r_y0": 593.183, "r_x1": 545.114, "r_y1": 593.183, "r_x2": 545.114, "r_y2": 584.631, "r_x3": 490.868, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "', 'rowspan='", "orig": "', 'rowspan='", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.138, "r_x1": 545.115, "r_y1": 605.138, "r_x2": 545.115, "r_y2": 596.586, "r_x3": 308.862, "r_y3": 596.586, "coord_origin": "TOPLEFT"}, "text": "or 'colspan=', with the number of spanning cells (attribute),", "orig": "or 'colspan=', with the number of spanning cells (attribute),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.093, "r_x1": 329.644, "r_y1": 617.093, "r_x2": 329.644, "r_y2": 608.5409999999999, "r_x3": 308.862, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "and '", "orig": "and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.646, "r_y0": 616.929, "r_x1": 337.395, "r_y1": 616.929, "r_x2": 337.395, "r_y2": 608.223, "r_x3": 329.646, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.394, "r_y0": 617.093, "r_x1": 343.202, "r_y1": 617.093, "r_x2": 343.202, "r_y2": 608.5409999999999, "r_x3": 337.394, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "'.", "orig": "'.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.064, "r_y0": 617.093, "r_x1": 468.591, "r_y1": 617.093, "r_x2": 468.591, "r_y2": 608.5409999999999, "r_x3": 348.064, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "The hidden state attached to '", "orig": "The hidden state attached to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 468.595, "r_y0": 616.929, "r_x1": 476.344, "r_y1": 616.929, "r_x2": 476.344, "r_y2": 608.223, "r_x3": 468.595, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.344, "r_y0": 617.093, "r_x1": 545.116, "r_y1": 617.093, "r_x2": 545.116, "r_y2": 608.5409999999999, "r_x3": 476.344, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "' is passed to the", "orig": "' is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.048, "r_x1": 545.115, "r_y1": 629.048, "r_x2": 545.115, "r_y2": 620.496, "r_x3": 308.862, "r_y3": 620.496, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "orig": "Cell BBox Decoder. A shared feed forward network (FFN)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.0029999999999, "r_x1": 533.2, "r_y1": 641.0029999999999, "r_x2": 533.2, "r_y2": 632.451, "r_x3": 308.862, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "receives the hidden states from the Structure Decoder,", "orig": "receives the hidden states from the Structure Decoder,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.364, "r_y0": 641.0029999999999, "r_x1": 545.115, "r_y1": 641.0029999999999, "r_x2": 545.115, "r_y2": 632.451, "r_x3": 537.364, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.9590000000001, "r_x1": 545.115, "r_y1": 652.9590000000001, "r_x2": 545.115, "r_y2": 644.407, "r_x3": 308.862, "r_y3": 644.407, "coord_origin": "TOPLEFT"}, "text": "provide the final detection predictions of the bounding box", "orig": "provide the final detection predictions of the bounding box", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.914, "r_x1": 449.424, "r_y1": 664.914, "r_x2": 449.424, "r_y2": 656.362, "r_x3": 308.862, "r_y3": 656.362, "coord_origin": "TOPLEFT"}, "text": "coordinates and their classification.", "orig": "coordinates and their classification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 668.261, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.217, "r_x1": 431.91, "r_y1": 677.217, "r_x2": 431.91, "r_y2": 668.261, "r_x3": 320.817, "r_y3": 668.261, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network.", "orig": "CNN Backbone Network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.499, "r_y0": 677.201, "r_x1": 545.113, "r_y1": 677.201, "r_x2": 545.113, "r_y2": 668.649, "r_x3": 439.499, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "A ResNet-18 CNN is the", "orig": "A ResNet-18 CNN is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "backbone that receives the table image and encodes it as a", "orig": "backbone that receives the table image and encodes it as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "vector of predefined length. The network has been modified", "orig": "vector of predefined length. The network has been modified", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "by removing the linear and pooling layer, as we are not per-", "orig": "by removing the linear and pooling layer, as we are not per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. 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As our model utilizes a transformer architecture, the", "orig": "tions). 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Afterwards, a transformer en-", "orig": "represent the table structure. 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Simultaneously, the Cell BBox Decoder", "orig": "predict the tags. Simultaneously, the Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 656.275, "r_x1": 220.582, "r_y1": 656.275, "r_x2": 220.582, "r_y2": 647.723, "r_x3": 50.112, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "selects features referring to the data cells ('", "orig": "selects features referring to the data cells ('", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.578, "r_y0": 656.111, "r_x1": 228.327, "r_y1": 656.111, "r_x2": 228.327, "r_y2": 647.405, "r_x3": 220.578, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.327, "r_y0": 656.275, "r_x1": 236.078, "r_y1": 656.275, "r_x2": 236.078, "r_y2": 647.723, "r_x3": 228.327, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "td", "orig": "td", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 236.078, "r_y0": 656.111, "r_x1": 243.827, "r_y1": 656.111, "r_x2": 243.827, "r_y2": 647.405, "r_x3": 236.078, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 243.826, "r_y0": 656.275, "r_x1": 255.293, "r_y1": 656.275, "r_x2": 255.293, "r_y2": 647.723, "r_x3": 243.826, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "', '", "orig": "', '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.291, "r_y0": 656.111, "r_x1": 263.04, "r_y1": 656.111, "r_x2": 263.04, "r_y2": 647.405, "r_x3": 255.291, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.04, "r_y0": 656.275, "r_x1": 286.362, "r_y1": 656.275, "r_x2": 286.362, "r_y2": 647.723, "r_x3": 263.04, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "') and", "orig": "') and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 668.23, "r_x1": 286.365, "r_y1": 668.23, "r_x2": 286.365, "r_y2": 659.678, "r_x3": 50.112, "r_y3": 659.678, "coord_origin": "TOPLEFT"}, "text": "passes them through an attention network, an MLP, and a", "orig": "passes them through an attention network, an MLP, and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 680.185, "r_x1": 218.47, "r_y1": 680.185, "r_x2": 218.47, "r_y2": 671.633, "r_x3": 50.112, "r_y3": 671.633, "coord_origin": "TOPLEFT"}, "text": "linear layer to predict the bounding boxes.", "orig": "linear layer to predict the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.803, "r": 545.115, "b": 294.221, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.355, "r_x1": 545.115, "r_y1": 258.355, "r_x2": 545.115, "r_y2": 249.803, "r_x3": 308.862, "r_y3": 249.803, "coord_origin": "TOPLEFT"}, "text": "forming classification, and adding an adaptive pooling layer", "orig": "forming classification, and adding an adaptive pooling layer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.31100000000004, "r_x1": 366.466, "r_y1": 270.31100000000004, "r_x2": 366.466, "r_y2": 261.759, "r_x3": 308.862, "r_y3": 261.759, "coord_origin": "TOPLEFT"}, "text": "of size 28*28.", "orig": "of size 28*28.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 371.706, "r_y0": 270.31100000000004, "r_x1": 545.115, "r_y1": 270.31100000000004, "r_x2": 545.115, "r_y2": 261.759, "r_x3": 371.706, "r_y3": 261.759, "coord_origin": "TOPLEFT"}, "text": "ResNet by default downsamples the image", "orig": "ResNet by default downsamples the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.266, "r_x1": 545.115, "r_y1": 282.266, "r_x2": 545.115, "r_y2": 273.71400000000006, "r_x3": 308.862, "r_y3": 273.71400000000006, "coord_origin": "TOPLEFT"}, "text": "resolution by 32 and then the encoded image is provided to", "orig": "resolution by 32 and then the encoded image is provided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.221, "r_x1": 341.24, "r_y1": 294.221, "r_x2": 341.24, "r_y2": 285.669, "r_x3": 308.862, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": "both the", "orig": "both the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 343.731, "r_y0": 294.078, "r_x1": 417.235, "r_y1": 294.078, "r_x2": 417.235, "r_y2": 285.49, "r_x3": 343.731, "r_y3": 285.49, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 417.234, "r_y0": 294.221, "r_x1": 436.601, "r_y1": 294.221, "r_x2": 436.601, "r_y2": 285.669, "r_x3": 417.234, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.092, "r_y0": 294.078, "r_x1": 516.561, "r_y1": 294.078, "r_x2": 516.561, "r_y2": 285.49, "r_x3": 439.092, "r_y3": 285.49, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.561, "r_y0": 294.221, "r_x1": 519.052, "r_y1": 294.221, "r_x2": 519.052, "r_y2": 285.669, "r_x3": 516.561, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 297.34, "r": 545.115, "b": 413.876, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.296, "r_x1": 400.866, "r_y1": 306.296, "r_x2": 400.866, "r_y2": 297.34, "r_x3": 320.817, "r_y3": 297.34, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder.", "orig": "Structure Decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.914, "r_y0": 306.28, "r_x1": 545.114, "r_y1": 306.28, "r_x2": 545.114, "r_y2": 297.728, "r_x3": 403.914, "r_y3": 297.728, "coord_origin": "TOPLEFT"}, "text": "The transformer 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the simplicity of table images.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 417.383, "r": 545.115, "b": 545.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 425.935, "r_x1": 336.309, "r_y1": 425.935, "r_x2": 336.309, "r_y2": 417.383, "r_x3": 320.817, "r_y3": 417.383, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.579, "r_y0": 425.935, "r_x1": 388.055, "r_y1": 425.935, "r_x2": 388.055, "r_y2": 417.383, "r_x3": 341.579, "r_y3": 417.383, "coord_origin": "TOPLEFT"}, 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"de-", "orig": "de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.756, "r_x1": 545.115, "r_y1": 473.756, "r_x2": 545.115, "r_y2": 465.204, "r_x3": 308.862, "r_y3": 465.204, "coord_origin": "TOPLEFT"}, "text": "coder receives as input the output feature produced by the", "orig": "coder receives as input the output feature produced by the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.711, "r_x1": 545.115, "r_y1": 485.711, "r_x2": 545.115, "r_y2": 477.159, "r_x3": 308.862, "r_y3": 477.159, "coord_origin": "TOPLEFT"}, "text": "transformer encoder, and the tokenized input of the HTML", "orig": "transformer encoder, and the tokenized input of the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, 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Using a stack of multi-head attention lay-", "orig": "ground-truth tags. Using a stack of multi-head attention lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.621, "r_x1": 545.115, "r_y1": 509.621, "r_x2": 545.115, "r_y2": 501.069, "r_x3": 308.862, "r_y3": 501.069, "coord_origin": "TOPLEFT"}, "text": "ers, different aspects of the tag sequence could be inferred.", "orig": "ers, different aspects of the tag sequence could be inferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.576, "r_x1": 545.115, "r_y1": 521.576, "r_x2": 545.115, "r_y2": 513.024, "r_x3": 308.862, "r_y3": 513.024, "coord_origin": "TOPLEFT"}, "text": "This is achieved by each attention head on a layer operating", "orig": "This is achieved by each attention head on a layer operating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.5319999999999, "r_x1": 545.115, "r_y1": 533.5319999999999, "r_x2": 545.115, "r_y2": 524.98, "r_x3": 308.862, "r_y3": 524.98, "coord_origin": "TOPLEFT"}, "text": "in a different subspace, and then combining altogether their", "orig": "in a different subspace, and then combining altogether their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.487, "r_x1": 369.733, "r_y1": 545.487, "r_x2": 369.733, "r_y2": 536.935, "r_x3": 308.862, "r_y3": 536.935, "coord_origin": "TOPLEFT"}, "text": "attention score.", "orig": "attention score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. 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As our model utilizes a transformer architecture, the", "orig": "tions). 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Simultaneously, the Cell BBox Decoder", "orig": "predict the tags. Simultaneously, the Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 656.275, "r_x1": 220.582, "r_y1": 656.275, "r_x2": 220.582, "r_y2": 647.723, "r_x3": 50.112, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "selects features referring to the data cells ('", "orig": "selects features referring to the data cells ('", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.578, "r_y0": 656.111, "r_x1": 228.327, "r_y1": 656.111, "r_x2": 228.327, "r_y2": 647.405, "r_x3": 220.578, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.327, "r_y0": 656.275, "r_x1": 236.078, "r_y1": 656.275, "r_x2": 236.078, "r_y2": 647.723, "r_x3": 228.327, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "td", "orig": "td", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 236.078, "r_y0": 656.111, "r_x1": 243.827, "r_y1": 656.111, "r_x2": 243.827, "r_y2": 647.405, "r_x3": 236.078, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 243.826, "r_y0": 656.275, "r_x1": 255.293, "r_y1": 656.275, "r_x2": 255.293, "r_y2": 647.723, "r_x3": 243.826, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "', '", "orig": "', '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.291, "r_y0": 656.111, "r_x1": 263.04, "r_y1": 656.111, "r_x2": 263.04, "r_y2": 647.405, "r_x3": 255.291, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.04, "r_y0": 656.275, "r_x1": 286.362, "r_y1": 656.275, "r_x2": 286.362, "r_y2": 647.723, "r_x3": 263.04, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "') and", "orig": "') and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 668.23, "r_x1": 286.365, "r_y1": 668.23, "r_x2": 286.365, "r_y2": 659.678, "r_x3": 50.112, "r_y3": 659.678, "coord_origin": "TOPLEFT"}, "text": "passes them through an attention network, an MLP, and a", "orig": "passes them through an attention network, an MLP, and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 680.185, "r_x1": 218.47, "r_y1": 680.185, "r_x2": 218.47, "r_y2": 671.633, "r_x3": 50.112, "r_y3": 671.633, "coord_origin": "TOPLEFT"}, "text": "linear layer to predict the bounding boxes.", "orig": "linear layer to predict the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.803, "r": 545.115, "b": 294.221, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.355, "r_x1": 545.115, "r_y1": 258.355, "r_x2": 545.115, "r_y2": 249.803, "r_x3": 308.862, "r_y3": 249.803, "coord_origin": "TOPLEFT"}, "text": "forming classification, and adding an adaptive pooling layer", "orig": "forming classification, and adding an adaptive pooling layer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.31100000000004, "r_x1": 366.466, "r_y1": 270.31100000000004, "r_x2": 366.466, "r_y2": 261.759, "r_x3": 308.862, "r_y3": 261.759, "coord_origin": "TOPLEFT"}, "text": "of size 28*28.", "orig": "of size 28*28.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 371.706, "r_y0": 270.31100000000004, "r_x1": 545.115, "r_y1": 270.31100000000004, "r_x2": 545.115, "r_y2": 261.759, "r_x3": 371.706, "r_y3": 261.759, "coord_origin": "TOPLEFT"}, "text": "ResNet by default downsamples the image", "orig": "ResNet by default downsamples the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.266, "r_x1": 545.115, "r_y1": 282.266, "r_x2": 545.115, "r_y2": 273.71400000000006, "r_x3": 308.862, "r_y3": 273.71400000000006, "coord_origin": "TOPLEFT"}, "text": "resolution by 32 and then the encoded image is provided to", "orig": "resolution by 32 and then the encoded image is provided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.221, "r_x1": 341.24, "r_y1": 294.221, "r_x2": 341.24, "r_y2": 285.669, "r_x3": 308.862, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": "both the", "orig": "both the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 343.731, "r_y0": 294.078, "r_x1": 417.235, "r_y1": 294.078, "r_x2": 417.235, "r_y2": 285.49, "r_x3": 343.731, "r_y3": 285.49, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 417.234, "r_y0": 294.221, "r_x1": 436.601, "r_y1": 294.221, "r_x2": 436.601, "r_y2": 285.669, "r_x3": 417.234, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.092, "r_y0": 294.078, "r_x1": 516.561, "r_y1": 294.078, "r_x2": 516.561, "r_y2": 285.49, "r_x3": 439.092, "r_y3": 285.49, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.561, "r_y0": 294.221, "r_x1": 519.052, "r_y1": 294.221, "r_x2": 519.052, "r_y2": 285.669, "r_x3": 516.561, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "forming classification, and adding an adaptive pooling layer of size 28*28. 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The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 417.383, "r": 545.115, "b": 545.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 425.935, "r_x1": 336.309, "r_y1": 425.935, "r_x2": 336.309, "r_y2": 417.383, "r_x3": 320.817, "r_y3": 417.383, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.579, "r_y0": 425.935, "r_x1": 388.055, "r_y1": 425.935, "r_x2": 388.055, "r_y2": 417.383, "r_x3": 341.579, "r_y3": 417.383, "coord_origin": "TOPLEFT"}, 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During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 548.605, "r": 545.115, "b": 653.187, "coord_origin": "TOPLEFT"}, "confidence": 0.9869195818901062, "cells": [{"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 557.561, "r_x1": 404.762, "r_y1": 557.561, "r_x2": 404.762, "r_y2": 548.605, "r_x3": 320.817, "r_y3": 548.605, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder.", "orig": "Cell BBox Decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.341, "r_y0": 557.545, "r_x1": 545.115, "r_y1": 557.545, "r_x2": 545.115, "r_y2": 548.9929999999999, "r_x3": 410.341, "r_y3": 548.9929999999999, "coord_origin": "TOPLEFT"}, "text": "Our architecture allows to simul-", "orig": "Our architecture allows to simul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.501, "r_x1": 545.115, "r_y1": 569.501, "r_x2": 545.115, "r_y2": 560.9490000000001, "r_x3": 308.862, "r_y3": 560.9490000000001, "coord_origin": "TOPLEFT"}, "text": "taneously predict HTML tags and bounding boxes for each", "orig": "taneously predict HTML tags and bounding boxes for each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.456, "r_x1": 545.115, "r_y1": 581.456, "r_x2": 545.115, "r_y2": 572.904, "r_x3": 308.862, "r_y3": 572.904, "coord_origin": "TOPLEFT"}, "text": "table cell without the need of a separate object detector end", "orig": "table cell without the need of a separate object detector end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.4110000000001, "r_x1": 336.458, "r_y1": 593.4110000000001, "r_x2": 336.458, "r_y2": 584.859, "r_x3": 308.862, "r_y3": 584.859, "coord_origin": "TOPLEFT"}, "text": "to end.", "orig": "to end.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.971, "r_y0": 593.4110000000001, "r_x1": 545.115, "r_y1": 593.4110000000001, "r_x2": 545.115, "r_y2": 584.859, "r_x3": 340.971, "r_y3": 584.859, "coord_origin": "TOPLEFT"}, "text": "This approach is inspired by DETR [1] which em-", "orig": "This approach is inspired by DETR [1] which em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.366, "r_x1": 545.115, "r_y1": 605.366, "r_x2": 545.115, "r_y2": 596.814, "r_x3": 308.862, "r_y3": 596.814, "coord_origin": "TOPLEFT"}, "text": "ploys a Transformer Encoder, and Decoder that looks for", "orig": "ploys a Transformer Encoder, and Decoder that looks for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.321, "r_x1": 545.115, "r_y1": 617.321, "r_x2": 545.115, "r_y2": 608.769, "r_x3": 308.862, "r_y3": 608.769, "coord_origin": "TOPLEFT"}, "text": "a specific number of object queries (potential object detec-", "orig": "a specific number of object queries (potential object detec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.2760000000001, "r_x1": 545.115, "r_y1": 629.2760000000001, "r_x2": 545.115, "r_y2": 620.7239999999999, "r_x3": 308.862, "r_y3": 620.7239999999999, "coord_origin": "TOPLEFT"}, "text": "tions). As our model utilizes a transformer architecture, the", "orig": "tions). As our model utilizes a transformer architecture, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.232, "r_x1": 381.679, "r_y1": 641.232, "r_x2": 381.679, "r_y2": 632.6800000000001, "r_x3": 308.862, "r_y3": 632.6800000000001, "coord_origin": "TOPLEFT"}, "text": "hidden state of the", "orig": "hidden state of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.997, "r_y0": 641.068, "r_x1": 391.746, "r_y1": 641.068, "r_x2": 391.746, "r_y2": 632.362, "r_x3": 383.997, "r_y3": 632.362, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.746, "r_y0": 641.232, "r_x1": 399.497, "r_y1": 641.232, "r_x2": 399.497, "r_y2": 632.6800000000001, "r_x3": 391.746, "r_y3": 632.6800000000001, "coord_origin": "TOPLEFT"}, "text": "td", "orig": "td", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 399.497, "r_y0": 641.068, "r_x1": 407.246, "r_y1": 641.068, "r_x2": 407.246, "r_y2": 632.362, "r_x3": 399.497, "r_y3": 632.362, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.246, "r_y0": 641.232, "r_x1": 432.91, "r_y1": 641.232, "r_x2": 432.91, "r_y2": 632.6800000000001, "r_x3": 407.246, "r_y3": 632.6800000000001, "coord_origin": "TOPLEFT"}, "text": "' and '", "orig": "' and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 432.908, "r_y0": 641.068, "r_x1": 440.657, "r_y1": 641.068, "r_x2": 440.657, "r_y2": 632.362, "r_x3": 432.908, "r_y3": 632.362, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.657, "r_y0": 641.232, "r_x1": 545.115, "r_y1": 641.232, "r_x2": 545.115, "r_y2": 632.6800000000001, "r_x3": 440.657, "r_y3": 632.6800000000001, "coord_origin": "TOPLEFT"}, "text": "' HTML structure tags be-", "orig": "' HTML structure tags be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.187, "r_x1": 398.964, "r_y1": 653.187, "r_x2": 398.964, "r_y2": 644.635, "r_x3": 308.862, "r_y3": 644.635, "coord_origin": "TOPLEFT"}, "text": "come the object query.", "orig": "come the object query.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 656.515, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 665.246, "r_x1": 444.343, "r_y1": 665.246, "r_x2": 444.343, "r_y2": 656.694, "r_x3": 320.817, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "The encoding generated by the", "orig": "The encoding generated by the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.006, "r_y0": 665.103, "r_x1": 545.108, "r_y1": 665.103, "r_x2": 545.108, "r_y2": 656.515, "r_x3": 447.006, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "along with the features acquired for every data cell from the", "orig": "along with the features acquired for every data cell from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Transformer Decoder are then passed to the attention net-", "orig": "Transformer Decoder are then passed to the attention net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "work. The attention network takes both inputs and learns to", "orig": "work. The attention network takes both inputs and learns to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 470.425, "r_y1": 713.066, "r_x2": 470.425, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "provide an attention weighted encoding.", "orig": "provide an attention weighted encoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.63, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 474.63, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "This weighted at-", "orig": "This weighted at-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8719564080238342, "cells": [{"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 286.365, "r_y1": 95.98400000000004, "r_x2": 286.365, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "produce a feature for each table cell. Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": 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As for the", "orig": "structure tokens. 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In", "orig": ") to be scale invariant as explained in [25]. 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173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.711, "r_y0": 405.174, "r_x1": 341.462, "r_y1": 405.174, "r_x2": 341.462, "r_y2": 396.622, "r_x3": 333.711, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.447, "r_y0": 405.174, "r_x1": 360.391, "r_y1": 405.174, "r_x2": 360.391, "r_y2": 396.622, "r_x3": 345.447, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "0.5.", "orig": "0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.963, "r_y0": 405.174, "r_x1": 415.166, "r_y1": 405.174, "r_x2": 415.166, "r_y2": 396.622, "r_x3": 367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 429.084, "r_x1": 360.966, "r_y1": 429.084, "r_x2": 360.966, "r_y2": 420.532, "r_x3": 308.862, "r_y3": 420.532, "coord_origin": "TOPLEFT"}, "text": "convergence.", "orig": "convergence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, "r_y3": 461.553, "coord_origin": "TOPLEFT"}, "text": "undergoes a single forward pass through the", "orig": "undergoes a single forward pass through the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.078, "r_y0": 469.962, "r_x1": 545.111, "r_y1": 469.962, "r_x2": 545.111, "r_y2": 461.374, "r_x3": 498.078, "r_y3": 461.374, "coord_origin": "TOPLEFT"}, "text": "CNN Back-", "orig": "CNN Back-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 481.917, "r_x1": 364.443, "r_y1": 481.917, "r_x2": 364.443, "r_y2": 473.329, "r_x3": 308.862, "r_y3": 473.329, "coord_origin": "TOPLEFT"}, "text": "bone Network", "orig": "bone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.061, "r_y0": 482.06, "r_x1": 545.113, "r_y1": 482.06, "r_x2": 545.113, "r_y2": 473.508, "r_x3": 367.061, "r_y3": 473.508, "coord_origin": "TOPLEFT"}, "text": "and transformer encoder. This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 624.225, "r_x1": 545.115, "r_y1": 624.225, "r_x2": 545.115, "r_y2": 615.673, "r_x3": 308.862, "r_y3": 615.673, "coord_origin": "TOPLEFT"}, "text": "able datasets of different nature to prove the generalization", "orig": "able datasets of different nature to prove the generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 636.1800000000001, "r_x1": 545.115, "r_y1": 636.1800000000001, "r_x2": 545.115, "r_y2": 627.6279999999999, "r_x3": 308.862, "r_y3": 627.6279999999999, "coord_origin": "TOPLEFT"}, "text": "and effectiveness of our model. The datasets used for eval-", "orig": "and effectiveness of our model. The datasets used for eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 648.135, "r_x1": 545.115, "r_y1": 648.135, "r_x2": 545.115, "r_y2": 639.583, "r_x3": 308.862, "r_y3": 639.583, "coord_origin": "TOPLEFT"}, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "orig": "uation are the PubTabNet, FinTabNet and TableBank which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 660.091, "r_x1": 545.115, "r_y1": 660.091, "r_x2": 545.115, "r_y2": 651.539, "r_x3": 308.862, "r_y3": 651.539, "coord_origin": "TOPLEFT"}, "text": "stem from the scientific, financial and general domains re-", "orig": "stem from the scientific, financial and general domains re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 672.046, "r_x1": 350.705, "r_y1": 672.046, "r_x2": 350.705, "r_y2": 663.494, "r_x3": 308.862, "r_y3": 663.494, "coord_origin": "TOPLEFT"}, "text": "spectively.", "orig": "spectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 320.817, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "We also share our baseline results on the challenging", "orig": "We also share our baseline results on the challenging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 361.196, "r_y1": 701.111, "r_x2": 361.196, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.057, "r_y0": 701.111, "r_x1": 396.214, "r_y1": 701.111, "r_x2": 396.214, "r_y2": 692.559, "r_x3": 366.057, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.406, "r_y0": 701.111, "r_x1": 453.449, "r_y1": 701.111, "r_x2": 453.449, "r_y2": 692.559, "r_x3": 406.406, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Throughout", "orig": "Throughout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.311, "r_y0": 701.111, "r_x1": 471.591, "r_y1": 701.111, "r_x2": 471.591, 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713.067, "r_x2": 495.94, "r_y2": 704.515, "r_x3": 308.862, "r_y3": 704.515, "coord_origin": "TOPLEFT"}, "text": "same parameters stated in Sec. 5.1 are utilized.", "orig": "same parameters stated in Sec. 5.1 are utilized.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 155.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 286.365, "r_y1": 95.98400000000004, "r_x2": 286.365, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "produce a feature for each table cell. Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.84900000000005, "r_x1": 135.691, "r_y1": 131.84900000000005, "r_x2": 135.691, "r_y2": 123.29700000000003, "r_x3": 50.112, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "of objects may exist.", "orig": "of objects may exist.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.047, "r_y0": 131.84900000000005, "r_x1": 286.365, "r_y1": 131.84900000000005, "r_x2": 286.365, "r_y2": 123.29700000000003, "r_x3": 142.047, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "In our case, we know up front that", "orig": "In our case, we know up front that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.80499999999995, "r_x1": 286.365, "r_y1": 143.80499999999995, "r_x2": 286.365, "r_y2": 135.25300000000004, "r_x3": 50.112, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "the produced detections always match with the table cells", "orig": "the produced detections always match with the table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.76, "r_x1": 175.163, "r_y1": 155.76, "r_x2": 175.163, "r_y2": 147.20799999999997, "r_x3": 50.112, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "in number and correspondence.", "orig": "in number and correspondence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.89300000000003, "r": 286.365, "b": 240.17700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": 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The FFN consists", "orig": "network (FFN). The FFN consists", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.356, "r_x1": 286.365, "r_y1": 192.356, "r_x2": 286.365, "r_y2": 183.80399999999997, "r_x3": 50.112, "r_y3": 183.80399999999997, "coord_origin": "TOPLEFT"}, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "orig": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.31100000000004, "r_x1": 286.365, "r_y1": 204.31100000000004, "r_x2": 286.365, "r_y2": 195.75900000000001, "r_x3": 50.112, "r_y3": 195.75900000000001, "coord_origin": "TOPLEFT"}, "text": "tion function) that predicts the normalized coordinates for", "orig": "tion function) that predicts the normalized coordinates for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 216.26599999999996, "r_x1": 196.004, "r_y1": 216.26599999999996, "r_x2": 196.004, "r_y2": 207.71400000000006, "r_x3": 50.112, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table cell.", "orig": "the bounding box of each table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.786, "r_y0": 216.26599999999996, "r_x1": 286.365, "r_y1": 216.26599999999996, "r_x2": 286.365, "r_y2": 207.71400000000006, "r_x3": 200.786, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "Finally, the predicted", "orig": "Finally, the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 228.221, "r_x1": 286.365, "r_y1": 228.221, "r_x2": 286.365, "r_y2": 219.66899999999998, "r_x3": 50.112, "r_y3": 219.66899999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes are classified based on whether they are", "orig": "bounding boxes are classified based on whether they are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 240.17700000000002, "r_x1": 181.549, "r_y1": 240.17700000000002, "r_x2": 181.549, "r_y2": 231.625, "r_x3": 50.112, "r_y3": 231.625, "coord_origin": "TOPLEFT"}, "text": "empty or not using a linear layer.", "orig": "empty or not using a linear layer.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92200000000003, "r": 286.366, "b": 444.145, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.451, "r_y0": 252.86199999999997, "r_x1": 276.139, "r_y1": 252.86199999999997, "r_x2": 276.139, "r_y2": 244.30999999999995, "r_x3": 134.451, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "We formulate a multi-task loss Eq.", "orig": "We formulate a multi-task loss Eq.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.379, "r_y0": 252.86199999999997, "r_x1": 286.361, "r_y1": 252.86199999999997, "r_x2": 286.361, "r_y2": 244.30999999999995, "r_x3": 281.379, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.817, "r_x1": 134.685, "r_y1": 264.817, "r_x2": 134.685, "r_y2": 256.265, "r_x3": 50.112, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "to train our network.", "orig": "to train our network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.543, "r_y0": 264.817, "r_x1": 286.365, "r_y1": 264.817, "r_x2": 286.365, "r_y2": 256.265, "r_x3": 140.543, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "The Cross-Entropy loss (denoted as", "orig": "The Cross-Entropy loss (denoted as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": 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280.176, "coord_origin": "TOPLEFT"}, "text": "structure tokens. As for the", "orig": "structure tokens. As for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 161.318, "r_y0": 288.585, "r_x1": 238.797, "r_y1": 288.585, "r_x2": 238.797, "r_y2": 279.99699999999996, "r_x3": 161.318, "r_y3": 279.99699999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.521, "r_y0": 288.728, "r_x1": 286.363, "r_y1": 288.728, "r_x2": 286.363, "r_y2": 280.176, "r_x3": 241.521, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "it is trained", "orig": "it is trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.683, "r_x1": 211.377, "r_y1": 300.683, "r_x2": 211.377, "r_y2": 292.131, 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In", "orig": ") to be scale invariant as explained in [25]. 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415.166, "r_y2": 396.622, "r_x3": 367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": 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"confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, 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This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 308.862, "t": 579.554, "r": 397.443, "b": 589.406, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. 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The datasets used for eval-", "orig": "and effectiveness of our model. 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false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 155.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 286.365, "r_y1": 95.98400000000004, "r_x2": 286.365, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "produce a feature for each table cell. Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.84900000000005, "r_x1": 135.691, "r_y1": 131.84900000000005, "r_x2": 135.691, "r_y2": 123.29700000000003, "r_x3": 50.112, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "of objects may exist.", "orig": "of objects may exist.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.047, "r_y0": 131.84900000000005, "r_x1": 286.365, "r_y1": 131.84900000000005, "r_x2": 286.365, "r_y2": 123.29700000000003, "r_x3": 142.047, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "In our case, we know up front that", "orig": "In our case, we know up front that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.80499999999995, "r_x1": 286.365, "r_y1": 143.80499999999995, "r_x2": 286.365, "r_y2": 135.25300000000004, "r_x3": 50.112, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "the produced detections always match with the table cells", "orig": "the produced detections always match with the table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.76, "r_x1": 175.163, "r_y1": 155.76, "r_x2": 175.163, "r_y2": 147.20799999999997, "r_x3": 50.112, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "in number and correspondence.", "orig": "in number and correspondence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.89300000000003, "r": 286.365, "b": 240.17700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 168.44500000000005, "r_x1": 108.154, "r_y1": 168.44500000000005, "r_x2": 108.154, "r_y2": 159.89300000000003, "r_x3": 62.067, "r_y3": 159.89300000000003, "coord_origin": "TOPLEFT"}, "text": "The output", "orig": "The output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 113.275, "r_y0": 168.44500000000005, "r_x1": 144.806, "r_y1": 168.44500000000005, "r_x2": 144.806, "r_y2": 159.89300000000003, "r_x3": 113.275, "r_y3": 159.89300000000003, 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"g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.153, "r_y0": 180.40099999999995, "r_x1": 286.365, "r_y1": 180.40099999999995, "r_x2": 286.365, "r_y2": 171.84900000000005, "r_x3": 143.153, "r_y3": 171.84900000000005, "coord_origin": "TOPLEFT"}, "text": "network (FFN). The FFN consists", "orig": "network (FFN). The FFN consists", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.356, "r_x1": 286.365, "r_y1": 192.356, "r_x2": 286.365, "r_y2": 183.80399999999997, "r_x3": 50.112, "r_y3": 183.80399999999997, "coord_origin": "TOPLEFT"}, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "orig": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.31100000000004, "r_x1": 286.365, "r_y1": 204.31100000000004, "r_x2": 286.365, "r_y2": 195.75900000000001, "r_x3": 50.112, "r_y3": 195.75900000000001, "coord_origin": "TOPLEFT"}, "text": "tion function) that predicts the normalized coordinates for", "orig": "tion function) that predicts the normalized coordinates for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 216.26599999999996, "r_x1": 196.004, "r_y1": 216.26599999999996, "r_x2": 196.004, "r_y2": 207.71400000000006, "r_x3": 50.112, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table cell.", "orig": "the bounding box of each table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.786, "r_y0": 216.26599999999996, "r_x1": 286.365, "r_y1": 216.26599999999996, "r_x2": 286.365, "r_y2": 207.71400000000006, "r_x3": 200.786, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "Finally, the predicted", "orig": "Finally, the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 228.221, "r_x1": 286.365, "r_y1": 228.221, "r_x2": 286.365, "r_y2": 219.66899999999998, "r_x3": 50.112, "r_y3": 219.66899999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes are classified based on whether they are", "orig": "bounding boxes are classified based on whether they are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 240.17700000000002, "r_x1": 181.549, "r_y1": 240.17700000000002, "r_x2": 181.549, "r_y2": 231.625, "r_x3": 50.112, "r_y3": 231.625, "coord_origin": "TOPLEFT"}, "text": "empty or not using a linear layer.", "orig": "empty or not using a linear layer.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92200000000003, "r": 286.366, "b": 444.145, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.451, "r_y0": 252.86199999999997, "r_x1": 276.139, "r_y1": 252.86199999999997, "r_x2": 276.139, "r_y2": 244.30999999999995, "r_x3": 134.451, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "We formulate a multi-task loss Eq.", "orig": "We formulate a multi-task loss Eq.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.379, "r_y0": 252.86199999999997, "r_x1": 286.361, "r_y1": 252.86199999999997, "r_x2": 286.361, "r_y2": 244.30999999999995, "r_x3": 281.379, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.817, "r_x1": 134.685, "r_y1": 264.817, "r_x2": 134.685, "r_y2": 256.265, "r_x3": 50.112, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "to train our network.", "orig": "to train our network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.543, "r_y0": 264.817, "r_x1": 286.365, "r_y1": 264.817, "r_x2": 286.365, "r_y2": 256.265, "r_x3": 140.543, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "The Cross-Entropy loss (denoted as", "orig": "The Cross-Entropy loss (denoted as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.60900000000004, "r_x1": 53.085, "r_y1": 276.60900000000004, "r_x2": 53.085, "r_y2": 267.903, "r_x3": 50.112, "r_y3": 267.903, "coord_origin": "TOPLEFT"}, "text": "l", "orig": "l", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.085, "r_y0": 277.53200000000004, "r_x1": 56.845, "r_y1": 277.53200000000004, "r_x2": 56.845, "r_y2": 271.438, "r_x3": 53.085, "r_y3": 271.438, "coord_origin": "TOPLEFT"}, "text": "s", "orig": "s", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 57.343, "r_y0": 276.773, "r_x1": 135.4, "r_y1": 276.773, "r_x2": 135.4, "r_y2": 268.221, "r_x3": 57.343, "r_y3": 268.221, "coord_origin": "TOPLEFT"}, "text": ") is used to train the", "orig": ") is used to train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.735, "r_y0": 276.63, "r_x1": 211.08, "r_y1": 276.63, "r_x2": 211.08, "r_y2": 268.04200000000003, "r_x3": 137.735, "r_y3": 268.04200000000003, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 213.637, "r_y0": 276.773, "r_x1": 286.364, "r_y1": 276.773, "r_x2": 286.364, "r_y2": 268.221, "r_x3": 213.637, "r_y3": 268.221, "coord_origin": "TOPLEFT"}, "text": "which predicts the", "orig": "which predicts the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.728, "r_x1": 158.824, "r_y1": 288.728, "r_x2": 158.824, "r_y2": 280.176, "r_x3": 50.112, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "structure tokens. As for the", "orig": "structure tokens. As for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 161.318, "r_y0": 288.585, "r_x1": 238.797, "r_y1": 288.585, "r_x2": 238.797, "r_y2": 279.99699999999996, "r_x3": 161.318, "r_y3": 279.99699999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.521, "r_y0": 288.728, "r_x1": 286.363, "r_y1": 288.728, "r_x2": 286.363, "r_y2": 280.176, "r_x3": 241.521, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "it is trained", "orig": "it is trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.683, "r_x1": 211.377, "r_y1": 300.683, "r_x2": 211.377, "r_y2": 292.131, 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In", "orig": ") to be scale invariant as explained in [25]. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.549, "r_x1": 286.365, "r_y1": 336.549, "r_x2": 286.365, "r_y2": 327.997, "r_x3": 50.112, "r_y3": 327.997, "coord_origin": "TOPLEFT"}, "text": "comparison to DETR, we do not use the Hungarian algo-", "orig": "comparison to DETR, we do not use the Hungarian algo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.504, "r_x1": 286.365, "r_y1": 348.504, "r_x2": 286.365, "r_y2": 339.952, "r_x3": 50.112, "r_y3": 339.952, "coord_origin": "TOPLEFT"}, "text": "rithm [15] to match the predicted bounding boxes with the", "orig": "rithm [15] to match the predicted bounding boxes with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, 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"and 2) Our bounding boxes", "orig": "and 2) Our bounding boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.235, "r_x1": 181.967, "r_y1": 420.235, "r_x2": 181.967, "r_y2": 411.683, "r_x3": 50.112, "r_y3": 411.683, "coord_origin": "TOPLEFT"}, "text": "generation mechanism (see Sec.", "orig": "generation mechanism (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 189.09, "r_y0": 420.235, "r_x1": 286.365, "r_y1": 420.235, "r_x2": 286.365, "r_y2": 411.683, "r_x3": 189.09, "r_y3": 411.683, "coord_origin": "TOPLEFT"}, "text": "3) ensures a one-to-one", "orig": "3) ensures a one-to-one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.19, "r_x1": 286.365, "r_y1": 432.19, "r_x2": 286.365, "r_y2": 423.638, "r_x3": 50.112, "r_y3": 423.638, "coord_origin": "TOPLEFT"}, "text": "mapping between the cell content and its bounding box for", "orig": "mapping between the cell content and its bounding box for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.145, "r_x1": 158.296, "r_y1": 444.145, "r_x2": 158.296, "r_y2": 435.593, "r_x3": 50.112, "r_y3": 435.593, "coord_origin": "TOPLEFT"}, "text": "all post-processed datasets.", "orig": "all post-processed datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 448.279, "r": 286.365, "b": 468.786, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 456.831, "r_x1": 286.365, "r_y1": 456.831, "r_x2": 286.365, "r_y2": 448.279, "r_x3": 62.067, "r_y3": 448.279, "coord_origin": "TOPLEFT"}, "text": "The loss used to train the TableFormer can be defined as", "orig": "The loss used to train the TableFormer can be defined as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 468.786, "r_x1": 91.377, "r_y1": 468.786, "r_x2": 91.377, "r_y2": 460.234, "r_x3": 50.112, "r_y3": 460.234, "coord_origin": "TOPLEFT"}, "text": "following:", "orig": "following:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33, "t": 493.391, "r": 286.362, "b": 517.9639999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.715, "r_y0": 502.097, "r_x1": 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Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 595.8240000000001, "r": 286.365, "b": 640.421, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 604.5550000000001, "r_x1": 202.978, "r_y1": 604.5550000000001, "r_x2": 202.978, "r_y2": 596.0029999999999, "r_x3": 62.067, "r_y3": 596.0029999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer uses ResNet-18 as the", "orig": "TableFormer uses ResNet-18 as the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.384, "r_y0": 604.412, "r_x1": 286.36, "r_y1": 604.412, "r_x2": 286.36, "r_y2": 595.8240000000001, "r_x3": 205.384, "r_y3": 595.8240000000001, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Net-", "orig": "CNN Backbone Net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 616.367, "r_x1": 70.037, "r_y1": 616.367, "r_x2": 70.037, "r_y2": 607.779, "r_x3": 50.112, "r_y3": 607.779, "coord_origin": "TOPLEFT"}, "text": "work", "orig": "work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 616.51, "r_x1": 72.528, "r_y1": 616.51, "r_x2": 72.528, "r_y2": 607.958, "r_x3": 70.037, "r_y3": 607.958, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.14, "r_y0": 616.51, "r_x1": 286.365, "r_y1": 616.51, "r_x2": 286.365, "r_y2": 607.958, "r_x3": 77.14, "r_y3": 607.958, "coord_origin": "TOPLEFT"}, "text": "The input images are resized to 448*448 pixels and", "orig": "The input images are resized to 448*448 pixels and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 628.465, "r_x1": 286.365, "r_y1": 628.465, "r_x2": 286.365, "r_y2": 619.913, "r_x3": 50.112, "r_y3": 619.913, "coord_origin": "TOPLEFT"}, "text": "the feature map has a dimension of 28*28. Additionally, we", "orig": "the feature map has a dimension of 28*28. Additionally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 640.421, "r_x1": 207.033, "r_y1": 640.421, "r_x2": 207.033, "r_y2": 631.869, "r_x3": 50.112, "r_y3": 631.869, "coord_origin": "TOPLEFT"}, "text": "enforce the following input constraints:", "orig": "enforce the following input constraints:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. 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(2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "such as EDD, ours are less restrictive due to the improved", "orig": "such as EDD, ours are less restrictive due to the improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "runtime performance and lower memory footprint of Table-", "orig": "runtime performance and lower memory footprint of Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 339.985, "r_y1": 95.98400000000004, "r_x2": 339.985, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "Former.", "orig": "Former.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.889, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 346.889, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "This allows to utilize input samples with longer", "orig": "This allows to utilize input samples with longer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 492.961, "r_y1": 107.93899999999996, "r_x2": 492.961, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "sequences and images with larger dimensions.", "orig": "sequences and images with larger dimensions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. 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instead. Lastly our dropout layers are set to 0.5.", "orig": "weights instead. Lastly our dropout layers are set to 0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 336.846, "r": 545.115, "b": 429.084, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 357.353, "r_x1": 403.736, "r_y1": 357.353, "r_x2": 403.736, "r_y2": 348.801, "r_x3": 308.862, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": "mizers, each one for the", "orig": "mizers, each one for 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367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 429.084, "r_x1": 360.966, "r_y1": 429.084, "r_x2": 360.966, "r_y2": 420.532, "r_x3": 308.862, "r_y3": 420.532, "coord_origin": "TOPLEFT"}, "text": "convergence.", "orig": "convergence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 437.643, "r": 545.115, "b": 553.7909999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, "r_y3": 461.553, "coord_origin": "TOPLEFT"}, "text": "undergoes a single forward pass through the", "orig": "undergoes a single forward pass through the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.078, "r_y0": 469.962, "r_x1": 545.111, "r_y1": 469.962, "r_x2": 545.111, "r_y2": 461.374, "r_x3": 498.078, "r_y3": 461.374, "coord_origin": "TOPLEFT"}, "text": "CNN Back-", "orig": "CNN Back-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 481.917, "r_x1": 364.443, "r_y1": 481.917, "r_x2": 364.443, "r_y2": 473.329, "r_x3": 308.862, "r_y3": 473.329, "coord_origin": "TOPLEFT"}, "text": "bone Network", "orig": "bone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.061, "r_y0": 482.06, "r_x1": 545.113, "r_y1": 482.06, "r_x2": 545.113, "r_y2": 473.508, "r_x3": 367.061, "r_y3": 473.508, "coord_origin": "TOPLEFT"}, "text": "and transformer encoder. This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.862, "t": 579.554, "r": 397.443, "b": 589.406, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 603.718, "r": 545.115, "b": 672.046, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 624.225, "r_x1": 545.115, "r_y1": 624.225, "r_x2": 545.115, "r_y2": 615.673, "r_x3": 308.862, "r_y3": 615.673, "coord_origin": "TOPLEFT"}, "text": "able datasets of different nature to prove the generalization", "orig": "able datasets of different nature to prove the generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 636.1800000000001, "r_x1": 545.115, "r_y1": 636.1800000000001, "r_x2": 545.115, "r_y2": 627.6279999999999, "r_x3": 308.862, "r_y3": 627.6279999999999, "coord_origin": "TOPLEFT"}, "text": "and effectiveness of our model. The datasets used for eval-", "orig": "and effectiveness of our model. The datasets used for eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 648.135, "r_x1": 545.115, "r_y1": 648.135, "r_x2": 545.115, "r_y2": 639.583, "r_x3": 308.862, "r_y3": 639.583, "coord_origin": "TOPLEFT"}, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "orig": "uation are the PubTabNet, FinTabNet and TableBank which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 660.091, "r_x1": 545.115, "r_y1": 660.091, "r_x2": 545.115, "r_y2": 651.539, "r_x3": 308.862, "r_y3": 651.539, "coord_origin": "TOPLEFT"}, "text": "stem from the scientific, financial and general domains re-", "orig": "stem from the scientific, financial and general domains re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 672.046, "r_x1": 350.705, "r_y1": 672.046, "r_x2": 350.705, "r_y2": 663.494, "r_x3": 308.862, "r_y3": 663.494, "coord_origin": "TOPLEFT"}, "text": "spectively.", "orig": "spectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 680.604, "r": 545.115, "b": 713.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 320.817, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "We also share our baseline results on the challenging", "orig": "We also share our baseline results on the challenging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 361.196, "r_y1": 701.111, "r_x2": 361.196, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.057, "r_y0": 701.111, "r_x1": 396.214, "r_y1": 701.111, "r_x2": 396.214, "r_y2": 692.559, "r_x3": 366.057, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.406, "r_y0": 701.111, "r_x1": 453.449, "r_y1": 701.111, "r_x2": 453.449, "r_y2": 692.559, "r_x3": 406.406, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Throughout", "orig": "Throughout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.311, "r_y0": 701.111, "r_x1": 471.591, "r_y1": 701.111, "r_x2": 471.591, "r_y2": 692.559, "r_x3": 458.311, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.453, "r_y0": 701.111, "r_x1": 527.481, "r_y1": 701.111, "r_x2": 527.481, "r_y2": 692.559, "r_x3": 476.453, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "experiments,", "orig": "experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.941, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 532.941, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.067, "r_x1": 495.94, "r_y1": 713.067, "r_x2": 495.94, "r_y2": 704.515, "r_x3": 308.862, "r_y3": 704.515, "coord_origin": "TOPLEFT"}, "text": "same parameters stated in Sec. 5.1 are utilized.", "orig": "same parameters stated in Sec. 5.1 are utilized.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 155.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 286.365, "r_y1": 95.98400000000004, "r_x2": 286.365, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "produce a feature for each table cell. Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.84900000000005, "r_x1": 135.691, "r_y1": 131.84900000000005, "r_x2": 135.691, "r_y2": 123.29700000000003, "r_x3": 50.112, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "of objects may exist.", "orig": "of objects may exist.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.047, "r_y0": 131.84900000000005, "r_x1": 286.365, "r_y1": 131.84900000000005, "r_x2": 286.365, "r_y2": 123.29700000000003, "r_x3": 142.047, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "In our case, we know up front that", "orig": "In our case, we know up front that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.80499999999995, "r_x1": 286.365, "r_y1": 143.80499999999995, "r_x2": 286.365, "r_y2": 135.25300000000004, "r_x3": 50.112, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "the produced detections always match with the table cells", "orig": "the produced detections always match with the table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.76, "r_x1": 175.163, "r_y1": 155.76, "r_x2": 175.163, "r_y2": 147.20799999999997, "r_x3": 50.112, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "in number and correspondence.", "orig": "in number and correspondence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.89300000000003, "r": 286.365, "b": 240.17700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 168.44500000000005, "r_x1": 108.154, "r_y1": 168.44500000000005, "r_x2": 108.154, "r_y2": 159.89300000000003, "r_x3": 62.067, "r_y3": 159.89300000000003, "coord_origin": "TOPLEFT"}, "text": "The output", "orig": "The output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 113.275, "r_y0": 168.44500000000005, "r_x1": 144.806, "r_y1": 168.44500000000005, "r_x2": 144.806, "r_y2": 159.89300000000003, "r_x3": 113.275, "r_y3": 159.89300000000003, 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"g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.153, "r_y0": 180.40099999999995, "r_x1": 286.365, "r_y1": 180.40099999999995, "r_x2": 286.365, "r_y2": 171.84900000000005, "r_x3": 143.153, "r_y3": 171.84900000000005, "coord_origin": "TOPLEFT"}, "text": "network (FFN). The FFN consists", "orig": "network (FFN). The FFN consists", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.356, "r_x1": 286.365, "r_y1": 192.356, "r_x2": 286.365, "r_y2": 183.80399999999997, "r_x3": 50.112, "r_y3": 183.80399999999997, "coord_origin": "TOPLEFT"}, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "orig": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.31100000000004, "r_x1": 286.365, "r_y1": 204.31100000000004, "r_x2": 286.365, "r_y2": 195.75900000000001, "r_x3": 50.112, "r_y3": 195.75900000000001, "coord_origin": "TOPLEFT"}, "text": "tion function) that predicts the normalized coordinates for", "orig": "tion function) that predicts the normalized coordinates for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 216.26599999999996, "r_x1": 196.004, "r_y1": 216.26599999999996, "r_x2": 196.004, "r_y2": 207.71400000000006, "r_x3": 50.112, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table cell.", "orig": "the bounding box of each table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.786, "r_y0": 216.26599999999996, "r_x1": 286.365, "r_y1": 216.26599999999996, "r_x2": 286.365, "r_y2": 207.71400000000006, "r_x3": 200.786, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "Finally, the predicted", "orig": "Finally, the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 228.221, "r_x1": 286.365, "r_y1": 228.221, "r_x2": 286.365, "r_y2": 219.66899999999998, "r_x3": 50.112, "r_y3": 219.66899999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes are classified based on whether they are", "orig": "bounding boxes are classified based on whether they are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 240.17700000000002, "r_x1": 181.549, "r_y1": 240.17700000000002, "r_x2": 181.549, "r_y2": 231.625, "r_x3": 50.112, "r_y3": 231.625, "coord_origin": "TOPLEFT"}, "text": "empty or not using a linear layer.", "orig": "empty or not using a linear layer.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92200000000003, "r": 286.366, "b": 444.145, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.451, "r_y0": 252.86199999999997, "r_x1": 276.139, "r_y1": 252.86199999999997, "r_x2": 276.139, "r_y2": 244.30999999999995, "r_x3": 134.451, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "We formulate a multi-task loss Eq.", "orig": "We formulate a multi-task loss Eq.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.379, "r_y0": 252.86199999999997, "r_x1": 286.361, "r_y1": 252.86199999999997, "r_x2": 286.361, "r_y2": 244.30999999999995, "r_x3": 281.379, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.817, "r_x1": 134.685, "r_y1": 264.817, "r_x2": 134.685, "r_y2": 256.265, "r_x3": 50.112, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "to train our network.", "orig": "to train our network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.543, "r_y0": 264.817, "r_x1": 286.365, "r_y1": 264.817, "r_x2": 286.365, "r_y2": 256.265, "r_x3": 140.543, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "The Cross-Entropy loss (denoted as", "orig": "The Cross-Entropy loss (denoted as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.60900000000004, "r_x1": 53.085, "r_y1": 276.60900000000004, "r_x2": 53.085, "r_y2": 267.903, "r_x3": 50.112, "r_y3": 267.903, "coord_origin": "TOPLEFT"}, "text": "l", "orig": "l", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.085, "r_y0": 277.53200000000004, "r_x1": 56.845, "r_y1": 277.53200000000004, "r_x2": 56.845, "r_y2": 271.438, "r_x3": 53.085, "r_y3": 271.438, "coord_origin": "TOPLEFT"}, "text": "s", "orig": "s", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 57.343, "r_y0": 276.773, "r_x1": 135.4, "r_y1": 276.773, "r_x2": 135.4, "r_y2": 268.221, "r_x3": 57.343, "r_y3": 268.221, "coord_origin": "TOPLEFT"}, "text": ") is used to train the", "orig": ") is used to train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.735, "r_y0": 276.63, "r_x1": 211.08, "r_y1": 276.63, "r_x2": 211.08, "r_y2": 268.04200000000003, "r_x3": 137.735, "r_y3": 268.04200000000003, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 213.637, "r_y0": 276.773, "r_x1": 286.364, "r_y1": 276.773, "r_x2": 286.364, "r_y2": 268.221, "r_x3": 213.637, "r_y3": 268.221, "coord_origin": "TOPLEFT"}, "text": "which predicts the", "orig": "which predicts the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.728, "r_x1": 158.824, "r_y1": 288.728, "r_x2": 158.824, "r_y2": 280.176, "r_x3": 50.112, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "structure tokens. As for the", "orig": "structure tokens. As for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 161.318, "r_y0": 288.585, "r_x1": 238.797, "r_y1": 288.585, "r_x2": 238.797, "r_y2": 279.99699999999996, "r_x3": 161.318, "r_y3": 279.99699999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.521, "r_y0": 288.728, "r_x1": 286.363, "r_y1": 288.728, "r_x2": 286.363, "r_y2": 280.176, "r_x3": 241.521, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "it is trained", "orig": "it is trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.683, "r_x1": 211.377, "r_y1": 300.683, "r_x2": 211.377, "r_y2": 292.131, 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In", "orig": ") to be scale invariant as explained in [25]. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.549, "r_x1": 286.365, "r_y1": 336.549, "r_x2": 286.365, "r_y2": 327.997, "r_x3": 50.112, "r_y3": 327.997, "coord_origin": "TOPLEFT"}, "text": "comparison to DETR, we do not use the Hungarian algo-", "orig": "comparison to DETR, we do not use the Hungarian algo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.504, "r_x1": 286.365, "r_y1": 348.504, "r_x2": 286.365, "r_y2": 339.952, "r_x3": 50.112, "r_y3": 339.952, "coord_origin": "TOPLEFT"}, "text": "rithm [15] to match the predicted bounding boxes with the", "orig": "rithm [15] to match the predicted bounding boxes with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.459, "r_x1": 286.365, "r_y1": 360.459, "r_x2": 286.365, "r_y2": 351.907, "r_x3": 50.112, "r_y3": 351.907, "coord_origin": "TOPLEFT"}, "text": "ground-truth boxes, as we have already achieved a one-to-", "orig": "ground-truth boxes, as we have already achieved a one-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.414, "r_x1": 286.365, "r_y1": 372.414, "r_x2": 286.365, "r_y2": 363.862, "r_x3": 50.112, "r_y3": 363.862, "coord_origin": "TOPLEFT"}, "text": "one match through two steps: 1) Our token input sequence", "orig": "one match through two steps: 1) Our token input sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.369, "r_x1": 286.365, "r_y1": 384.369, "r_x2": 286.365, "r_y2": 375.817, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.646, "r_y0": 408.137, "r_x1": 170.052, "r_y1": 408.137, "r_x2": 170.052, "r_y2": 399.549, "r_x3": 91.646, "r_y3": 399.549, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.051, "r_y0": 408.28, "r_x1": 172.542, "r_y1": 408.28, "r_x2": 172.542, "r_y2": 399.728, "r_x3": 170.051, "r_y3": 399.728, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.62, "r_y0": 408.28, "r_x1": 286.364, "r_y1": 408.28, "r_x2": 286.364, "r_y2": 399.728, "r_x3": 175.62, "r_y3": 399.728, "coord_origin": "TOPLEFT"}, "text": "and 2) Our bounding boxes", "orig": "and 2) Our bounding boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.235, "r_x1": 181.967, "r_y1": 420.235, "r_x2": 181.967, "r_y2": 411.683, "r_x3": 50.112, "r_y3": 411.683, "coord_origin": "TOPLEFT"}, "text": "generation mechanism (see Sec.", "orig": "generation mechanism (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 189.09, "r_y0": 420.235, "r_x1": 286.365, "r_y1": 420.235, "r_x2": 286.365, "r_y2": 411.683, "r_x3": 189.09, "r_y3": 411.683, "coord_origin": "TOPLEFT"}, "text": "3) ensures a one-to-one", "orig": "3) ensures a one-to-one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.19, "r_x1": 286.365, "r_y1": 432.19, "r_x2": 286.365, "r_y2": 423.638, "r_x3": 50.112, "r_y3": 423.638, "coord_origin": "TOPLEFT"}, "text": "mapping between the cell content and its bounding box for", "orig": "mapping between the cell content and its bounding box for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.145, "r_x1": 158.296, "r_y1": 444.145, "r_x2": 158.296, "r_y2": 435.593, "r_x3": 50.112, "r_y3": 435.593, "coord_origin": "TOPLEFT"}, "text": "all post-processed datasets.", "orig": "all post-processed datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 448.279, "r": 286.365, "b": 468.786, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 456.831, "r_x1": 286.365, "r_y1": 456.831, "r_x2": 286.365, "r_y2": 448.279, "r_x3": 62.067, "r_y3": 448.279, "coord_origin": "TOPLEFT"}, "text": "The loss used to train the TableFormer can be defined as", "orig": "The loss used to train the TableFormer can be defined as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 468.786, "r_x1": 91.377, "r_y1": 468.786, "r_x2": 91.377, "r_y2": 460.234, "r_x3": 50.112, "r_y3": 460.234, "coord_origin": "TOPLEFT"}, "text": "following:", "orig": "following:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33, "t": 493.391, "r": 286.362, "b": 517.9639999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.715, "r_y0": 502.097, "r_x1": 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(2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "such as EDD, ours are less restrictive due to the improved", "orig": "such as EDD, ours are less restrictive due to the improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "runtime performance and lower memory footprint of Table-", "orig": "runtime performance and lower memory footprint of Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 339.985, "r_y1": 95.98400000000004, "r_x2": 339.985, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "Former.", "orig": "Former.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.889, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 346.889, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "This allows to utilize input samples with longer", "orig": "This allows to utilize input samples with longer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 492.961, "r_y1": 107.93899999999996, "r_x2": 492.961, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "sequences and images with larger dimensions.", "orig": "sequences and images with larger dimensions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. 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COCO", "orig": "the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.51199999999994, "r_x1": 342.336, "r_y1": 268.51199999999994, "r_x2": 342.336, "r_y2": 259.96000000000004, "r_x3": 308.862, "r_y3": 259.96000000000004, "coord_origin": "TOPLEFT"}, "text": "dataset).", "orig": "dataset).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.952, "r_y0": 268.51199999999994, "r_x1": 545.115, "r_y1": 268.51199999999994, "r_x2": 545.115, "r_y2": 259.96000000000004, "r_x3": 348.952, "r_y3": 259.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Moreover, we have added ResNet blocks to the", "orig": "Moreover, we have added ResNet blocks to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.467, "r_x1": 545.115, "r_y1": 280.467, "r_x2": 545.115, "r_y2": 271.91499999999996, "r_x3": 308.862, "r_y3": 271.91499999999996, "coord_origin": "TOPLEFT"}, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "orig": "inputs of the Structure Decoder and Cell BBox Decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.422, "r_x1": 545.115, "r_y1": 292.422, "r_x2": 545.115, "r_y2": 283.87, "r_x3": 308.862, "r_y3": 283.87, "coord_origin": "TOPLEFT"}, "text": "This prevents a decoder having a stronger influence over the", "orig": "This prevents a decoder having a stronger influence over the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.377, "r_x1": 545.115, "r_y1": 304.377, "r_x2": 545.115, "r_y2": 295.825, "r_x3": 308.862, "r_y3": 295.825, "coord_origin": "TOPLEFT"}, "text": "learned weights which would damage the other prediction", "orig": "learned weights which would damage the other prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.332, "r_x1": 545.115, "r_y1": 316.332, "r_x2": 545.115, "r_y2": 307.78, "r_x3": 308.862, "r_y3": 307.78, "coord_origin": "TOPLEFT"}, "text": "task (structure vs bounding boxes), but learn task specific", "orig": "task (structure vs bounding boxes), but learn task specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.288, "r_x1": 532.483, "r_y1": 328.288, "r_x2": 532.483, "r_y2": 319.736, "r_x3": 308.862, "r_y3": 319.736, "coord_origin": "TOPLEFT"}, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "orig": "weights instead. Lastly our dropout layers are set to 0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 336.846, "r": 545.115, "b": 429.084, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 357.353, "r_x1": 403.736, "r_y1": 357.353, "r_x2": 403.736, "r_y2": 348.801, "r_x3": 308.862, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": "mizers, each one for the", "orig": "mizers, each one for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.076, "r_y0": 357.21, "r_x1": 545.112, "r_y1": 357.21, "r_x2": 545.112, "r_y2": 348.622, "r_x3": 406.076, "r_y3": 348.622, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network Structure", "orig": "CNN Backbone Network Structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 503.539, "r_y0": 357.353, "r_x1": 506.03, "r_y1": 357.353, "r_x2": 506.03, "r_y2": 348.801, "r_x3": 503.539, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 369.165, "r_x1": 343.163, "r_y1": 369.165, "r_x2": 343.163, "r_y2": 360.577, "r_x3": 308.862, "r_y3": 360.577, "coord_origin": "TOPLEFT"}, "text": "Decoder", "orig": "Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 343.163, "r_y0": 369.308, "r_x1": 362.202, "r_y1": 369.308, "r_x2": 362.202, "r_y2": 360.756, "r_x3": 343.163, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.286, "r_y0": 369.165, "r_x1": 440.938, "r_y1": 369.165, "r_x2": 440.938, "r_y2": 360.577, "r_x3": 364.286, "r_y3": 360.577, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.939, "r_y0": 369.308, "r_x1": 443.43, "r_y1": 369.308, "r_x2": 443.43, "r_y2": 360.756, "r_x3": 440.939, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.379, "r_y0": 369.308, "r_x1": 545.108, "r_y1": 369.308, "r_x2": 545.108, "r_y2": 360.756, "r_x3": 446.379, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": "Taking the PubTabNet as", "orig": "Taking the PubTabNet as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 381.263, "r_x1": 545.115, "r_y1": 381.263, "r_x2": 545.115, "r_y2": 372.711, "r_x3": 308.862, "r_y3": 372.711, "coord_origin": "TOPLEFT"}, "text": "an example for our parameter set up, the initializing learn-", "orig": "an example for our parameter set up, the initializing learn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 393.219, "r_x1": 545.115, "r_y1": 393.219, "r_x2": 545.115, "r_y2": 384.667, "r_x3": 308.862, "r_y3": 384.667, "coord_origin": "TOPLEFT"}, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "orig": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 405.01, "r_x1": 314.673, "r_y1": 405.01, "r_x2": 314.673, "r_y2": 396.304, "r_x3": 308.862, "r_y3": 396.304, "coord_origin": "TOPLEFT"}, "text": "\u03bb", "orig": "\u03bb", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.658, "r_y0": 405.174, "r_x1": 329.726, "r_y1": 405.174, "r_x2": 329.726, "r_y2": 396.622, "r_x3": 318.658, 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367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 429.084, "r_x1": 360.966, "r_y1": 429.084, "r_x2": 360.966, "r_y2": 420.532, "r_x3": 308.862, "r_y3": 420.532, "coord_origin": "TOPLEFT"}, "text": "convergence.", "orig": "convergence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 437.643, "r": 545.115, "b": 553.7909999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, "r_y3": 461.553, "coord_origin": "TOPLEFT"}, "text": "undergoes a single forward pass through the", "orig": "undergoes a single forward pass through the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.078, "r_y0": 469.962, "r_x1": 545.111, "r_y1": 469.962, "r_x2": 545.111, "r_y2": 461.374, "r_x3": 498.078, "r_y3": 461.374, "coord_origin": "TOPLEFT"}, "text": "CNN Back-", "orig": "CNN Back-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 481.917, "r_x1": 364.443, "r_y1": 481.917, "r_x2": 364.443, "r_y2": 473.329, "r_x3": 308.862, "r_y3": 473.329, "coord_origin": "TOPLEFT"}, "text": "bone Network", "orig": "bone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.061, "r_y0": 482.06, "r_x1": 545.113, "r_y1": 482.06, "r_x2": 545.113, "r_y2": 473.508, "r_x3": 367.061, "r_y3": 473.508, "coord_origin": "TOPLEFT"}, "text": "and transformer encoder. This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.862, "t": 579.554, "r": 397.443, "b": 589.406, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 603.718, "r": 545.115, "b": 672.046, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 624.225, "r_x1": 545.115, "r_y1": 624.225, "r_x2": 545.115, "r_y2": 615.673, "r_x3": 308.862, "r_y3": 615.673, "coord_origin": "TOPLEFT"}, "text": "able datasets of different nature to prove the generalization", "orig": "able datasets of different nature to prove the generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 636.1800000000001, "r_x1": 545.115, "r_y1": 636.1800000000001, "r_x2": 545.115, "r_y2": 627.6279999999999, "r_x3": 308.862, "r_y3": 627.6279999999999, "coord_origin": "TOPLEFT"}, "text": "and effectiveness of our model. The datasets used for eval-", "orig": "and effectiveness of our model. The datasets used for eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 648.135, "r_x1": 545.115, "r_y1": 648.135, "r_x2": 545.115, "r_y2": 639.583, "r_x3": 308.862, "r_y3": 639.583, "coord_origin": "TOPLEFT"}, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "orig": "uation are the PubTabNet, FinTabNet and TableBank which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 660.091, "r_x1": 545.115, "r_y1": 660.091, "r_x2": 545.115, "r_y2": 651.539, "r_x3": 308.862, "r_y3": 651.539, "coord_origin": "TOPLEFT"}, "text": "stem from the scientific, financial and general domains re-", "orig": "stem from the scientific, financial and general domains re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 672.046, "r_x1": 350.705, "r_y1": 672.046, "r_x2": 350.705, "r_y2": 663.494, "r_x3": 308.862, "r_y3": 663.494, "coord_origin": "TOPLEFT"}, "text": "spectively.", "orig": "spectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 680.604, "r": 545.115, "b": 713.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 320.817, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "We also share our baseline results on the challenging", "orig": "We also share our baseline results on the challenging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 361.196, "r_y1": 701.111, "r_x2": 361.196, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.057, "r_y0": 701.111, "r_x1": 396.214, "r_y1": 701.111, "r_x2": 396.214, "r_y2": 692.559, "r_x3": 366.057, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.406, "r_y0": 701.111, "r_x1": 453.449, "r_y1": 701.111, "r_x2": 453.449, "r_y2": 692.559, "r_x3": 406.406, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Throughout", "orig": "Throughout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.311, "r_y0": 701.111, "r_x1": 471.591, "r_y1": 701.111, "r_x2": 471.591, "r_y2": 692.559, "r_x3": 458.311, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.453, "r_y0": 701.111, "r_x1": 527.481, "r_y1": 701.111, "r_x2": 527.481, "r_y2": 692.559, "r_x3": 476.453, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "experiments,", "orig": "experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.941, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 532.941, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.067, "r_x1": 495.94, "r_y1": 713.067, "r_x2": 495.94, "r_y2": 704.515, "r_x3": 308.862, "r_y3": 704.515, "coord_origin": "TOPLEFT"}, "text": "same parameters stated in Sec. 5.1 are utilized.", "orig": "same parameters stated in Sec. 5.1 are utilized.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.25300000000004, "r_x1": 167.898, "r_y1": 84.25300000000004, "r_x2": 167.898, "r_y2": 74.40099999999995, "r_x3": 50.112, "r_y3": 74.40099999999995, "coord_origin": "TOPLEFT"}, "text": "5.3. Datasets and Metrics", "orig": "5.3. Datasets and Metrics", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 102.17100000000005, "r_x1": 286.365, "r_y1": 102.17100000000005, "r_x2": 286.365, "r_y2": 93.61900000000003, "r_x3": 62.067, "r_y3": 93.61900000000003, "coord_origin": "TOPLEFT"}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 114.12599999999998, "r_x1": 286.365, "r_y1": 114.12599999999998, "r_x2": 286.365, "r_y2": 105.57399999999996, "r_x3": 50.112, "r_y3": 105.57399999999996, "coord_origin": "TOPLEFT"}, "text": "ric was introduced in [37]. It represents the prediction, and", "orig": "ric was introduced in [37]. It represents the prediction, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 126.08100000000002, "r_x1": 239.999, "r_y1": 126.08100000000002, "r_x2": 239.999, "r_y2": 117.529, "r_x3": 50.112, "r_y3": 117.529, "coord_origin": "TOPLEFT"}, "text": "ground-truth as a tree structure of HTML tags.", "orig": "ground-truth as a tree structure of HTML tags.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.03, "r_y0": 126.08100000000002, "r_x1": 286.365, "r_y1": 126.08100000000002, "r_x2": 286.365, "r_y2": 117.529, "r_x3": 245.03, "r_y3": 117.529, "coord_origin": "TOPLEFT"}, "text": "This simi-", "orig": "This simi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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"r_x3": 50.112, "r_y3": 279.903, "coord_origin": "TOPLEFT"}, "text": "All the more, our model outperforms pre-trained methods.", "orig": "All the more, our model outperforms pre-trained methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.411, "r_x1": 286.365, "r_y1": 300.411, "r_x2": 286.365, "r_y2": 291.859, "r_x3": 50.112, "r_y3": 291.859, "coord_origin": "TOPLEFT"}, "text": "During the evaluation we do not apply any table filtering.", "orig": "During the evaluation we do not apply any table filtering.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.366, "r_x1": 286.365, "r_y1": 312.366, "r_x2": 286.365, "r_y2": 303.814, "r_x3": 50.112, "r_y3": 303.814, "coord_origin": "TOPLEFT"}, "text": "We also provide our baseline results on the SynthTabNet", "orig": "We also provide our baseline results on the SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.321, "r_x1": 80.269, "r_y1": 324.321, "r_x2": 80.269, "r_y2": 315.769, "r_x3": 50.112, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 85.579, "r_y0": 324.321, "r_x1": 91.666, "r_y1": 324.321, "r_x2": 91.666, "r_y2": 315.769, "r_x3": 85.579, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "It", "orig": "It", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 94.894, "r_y0": 324.321, "r_x1": 257.812, "r_y1": 324.321, "r_x2": 257.812, "r_y2": 315.769, "r_x3": 94.894, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "has been observed that large tables (e.g.", "orig": "has been observed that large tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.122, "r_y0": 324.321, "r_x1": 286.365, "r_y1": 324.321, "r_x2": 286.365, "r_y2": 315.769, "r_x3": 263.122, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "tables", "orig": "tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.276, "r_x1": 286.365, "r_y1": 336.276, "r_x2": 286.365, "r_y2": 327.724, "r_x3": 50.112, "r_y3": 327.724, "coord_origin": "TOPLEFT"}, "text": "that occupy half of the page or more) yield poor predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.203, "r_y0": 360.186, "r_x1": 134.147, "r_y1": 360.186, "r_x2": 134.147, "r_y2": 351.634, "r_x3": 119.203, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "that", "orig": "that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.952, "r_y0": 360.186, "r_x1": 286.365, "r_y1": 360.186, "r_x2": 286.365, "r_y2": 351.634, "r_x3": 137.952, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "produces downsampled images with", "orig": "produces downsampled images with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.142, "r_x1": 155.357, "r_y1": 372.142, "r_x2": 155.357, "r_y2": 363.59, "r_x3": 50.112, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "indistinguishable features.", "orig": "indistinguishable features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 396.052, "r_x1": 170.012, "r_y1": 396.052, "r_x2": 170.012, "r_y2": 387.5, "r_x3": 50.112, "r_y3": 387.5, "coord_origin": 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We make use of the", "orig": "with post-processing during inference. 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75.298, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.104, "r_y0": 84.029, "r_x1": 545.11, "r_y1": 84.029, "r_x2": 545.11, "r_y2": 75.47699999999998, "r_x3": 408.104, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "accuracy for cells with a class la-", "orig": "accuracy for cells with a class la-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "bel of 'content' only using the PASCAL VOC mAP metric", "orig": "bel of 'content' only using the PASCAL VOC mAP metric", 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"r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "are only provided. To compare the performance of our pro-", "orig": "are only provided. To compare the performance of our pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 143.80499999999995, "r_x1": 502.017, "r_y1": 143.80499999999995, "r_x2": 502.017, "r_y2": 135.25300000000004, "r_x3": 308.862, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "posed approach, we've integrated TableFormer's", "orig": "posed approach, we've integrated TableFormer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 504.473, "r_y0": 143.66200000000003, "r_x1": 545.11, "r_y1": 143.66200000000003, "r_x2": 545.11, "r_y2": 135.07399999999996, "r_x3": 504.473, "r_y3": 135.07399999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox", "orig": "Cell BBox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 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"from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.455, "r_x1": 286.365, "r_y1": 288.455, "r_x2": 286.365, "r_y2": 279.903, "r_x3": 50.112, "r_y3": 279.903, "coord_origin": "TOPLEFT"}, "text": "All the more, our model outperforms pre-trained methods.", "orig": "All the more, our model outperforms pre-trained methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.411, "r_x1": 286.365, "r_y1": 300.411, "r_x2": 286.365, "r_y2": 291.859, "r_x3": 50.112, "r_y3": 291.859, "coord_origin": "TOPLEFT"}, "text": "During the evaluation we do not apply any table filtering.", "orig": "During the evaluation we do not apply any table filtering.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 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"text": "that occupy half of the page or more) yield poor predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.203, "r_y0": 360.186, "r_x1": 134.147, "r_y1": 360.186, "r_x2": 134.147, "r_y2": 351.634, "r_x3": 119.203, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "that", "orig": "that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.952, "r_y0": 360.186, "r_x1": 286.365, "r_y1": 360.186, "r_x2": 286.365, "r_y2": 351.634, "r_x3": 137.952, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "produces downsampled images with", "orig": "produces downsampled images with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.142, "r_x1": 155.357, "r_y1": 372.142, "r_x2": 155.357, "r_y2": 363.59, "r_x3": 50.112, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "indistinguishable features.", "orig": "indistinguishable features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.529, "r_y0": 107.93899999999996, "r_x1": 545.115, "r_y1": 107.93899999999996, "r_x2": 545.115, "r_y2": 99.38699999999994, "r_x3": 477.529, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "Note that we do", "orig": "Note that we do", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 119.894, "r_x1": 545.115, "r_y1": 119.894, "r_x2": 545.115, "r_y2": 111.34199999999998, "r_x3": 308.862, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "not have post-processing results for SynthTabNet as images", "orig": "not have post-processing results for SynthTabNet as images", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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To compare the performance of our pro-", "orig": "are only provided. To compare the performance of our pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 143.80499999999995, "r_x1": 502.017, "r_y1": 143.80499999999995, "r_x2": 502.017, "r_y2": 135.25300000000004, "r_x3": 308.862, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "posed approach, we've integrated TableFormer's", "orig": "posed approach, we've integrated TableFormer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 504.473, "r_y0": 143.66200000000003, "r_x1": 545.11, "r_y1": 143.66200000000003, "r_x2": 545.11, "r_y2": 135.07399999999996, "r_x3": 504.473, "r_y3": 135.07399999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox", "orig": "Cell BBox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 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Datasets and Metrics", "orig": "5.3. Datasets and Metrics", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.61900000000003, "r": 286.365, "b": 138.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 102.17100000000005, "r_x1": 286.365, "r_y1": 102.17100000000005, "r_x2": 286.365, "r_y2": 93.61900000000003, "r_x3": 62.067, "r_y3": 93.61900000000003, "coord_origin": "TOPLEFT"}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 114.12599999999998, "r_x1": 286.365, "r_y1": 114.12599999999998, "r_x2": 286.365, "r_y2": 105.57399999999996, "r_x3": 50.112, "r_y3": 105.57399999999996, "coord_origin": "TOPLEFT"}, "text": "ric was introduced in [37]. It represents the prediction, and", "orig": "ric was introduced in [37]. 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EditDist denotes the tree-edit distance, and", "orig": "format. EditDist denotes the tree-edit distance, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.182, "r_y0": 201.63199999999995, "r_x1": 257.95, "r_y1": 201.63199999999995, "r_x2": 257.95, "r_y2": 193.06799999999998, "r_x3": 255.182, "r_y3": 193.06799999999998, "coord_origin": "TOPLEFT"}, "text": "|", "orig": "|", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.949, "r_y0": 201.774, "r_x1": 263.771, "r_y1": 201.774, "r_x2": 263.771, "r_y2": 193.06799999999998, "r_x3": 257.949, "r_y3": 193.06799999999998, "coord_origin": "TOPLEFT"}, "text": "T", "orig": "T", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.155, "r_y0": 201.63199999999995, "r_x1": 267.923, "r_y1": 201.63199999999995, "r_x2": 267.923, "r_y2": 193.06799999999998, "r_x3": 265.155, "r_y3": 193.06799999999998, "coord_origin": "TOPLEFT"}, "text": "|", "orig": "|", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.322, "r_y0": 201.938, "r_x1": 286.362, "r_y1": 201.938, "r_x2": 286.362, "r_y2": 193.38599999999997, "r_x3": 270.322, "r_y3": 193.38599999999997, "coord_origin": "TOPLEFT"}, "text": "rep-", "orig": "rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 213.89300000000003, "r_x1": 172.134, "r_y1": 213.89300000000003, "r_x2": 172.134, "r_y2": 205.341, "r_x3": 50.112, "r_y3": 205.341, "coord_origin": "TOPLEFT"}, "text": "resents the number of nodes in", "orig": "resents the number of nodes in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.624, "r_y0": 213.72900000000004, "r_x1": 180.446, "r_y1": 213.72900000000004, "r_x2": 180.446, "r_y2": 205.02300000000002, "r_x3": 174.624, "r_y3": 205.02300000000002, "coord_origin": "TOPLEFT"}, "text": "T", "orig": "T", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.829, "r_y0": 213.89300000000003, "r_x1": 184.32, "r_y1": 213.89300000000003, "r_x2": 184.32, "r_y2": 205.341, "r_x3": 181.829, "r_y3": 205.341, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81899999999996, "r": 170.452, "b": 234.67100000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9588837027549744, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 234.67100000000005, "r_x1": 170.452, "r_y1": 234.67100000000005, "r_x2": 170.452, "r_y2": 224.81899999999996, "r_x3": 50.112, "r_y3": 224.81899999999996, "coord_origin": "TOPLEFT"}, "text": "5.4. Quantitative Analysis", "orig": "5.4. Quantitative Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.64999999999998, "r": 286.365, "b": 396.052, "coord_origin": "TOPLEFT"}, "confidence": 0.9855090975761414, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.606, "r_x1": 105.325, "r_y1": 252.606, "r_x2": 105.325, "r_y2": 243.64999999999998, "r_x3": 62.067, "r_y3": 243.64999999999998, "coord_origin": "TOPLEFT"}, "text": "Structure.", "orig": "Structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.126, "r_y0": 252.59000000000003, "r_x1": 184.684, "r_y1": 252.59000000000003, "r_x2": 184.684, "r_y2": 244.038, "r_x3": 112.126, "r_y3": 244.038, "coord_origin": "TOPLEFT"}, "text": "As shown in Tab.", "orig": "As shown in Tab.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.478, "r_y0": 252.59000000000003, "r_x1": 198.95, "r_y1": 252.59000000000003, "r_x2": 198.95, "r_y2": 244.038, "r_x3": 191.478, "r_y3": 244.038, "coord_origin": "TOPLEFT"}, "text": "2,", "orig": "2,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.995, "r_y0": 252.59000000000003, "r_x1": 286.362, "r_y1": 252.59000000000003, "r_x2": 286.362, "r_y2": 244.038, "r_x3": 202.995, "r_y3": 244.038, "coord_origin": "TOPLEFT"}, "text": "TableFormer outper-", "orig": "TableFormer outper-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.54499999999996, "r_x1": 286.365, "r_y1": 264.54499999999996, "r_x2": 286.365, "r_y2": 255.99300000000005, "r_x3": 50.112, "r_y3": 255.99300000000005, "coord_origin": "TOPLEFT"}, "text": "forms all SOTA methods across different datasets by a large", "orig": "forms all SOTA methods across different datasets by a large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.5, "r_x1": 138.719, "r_y1": 276.5, "r_x2": 138.719, "r_y2": 267.948, "r_x3": 50.112, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "margin for predicting", "orig": "margin for predicting", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.993, "r_y0": 276.5, "r_x1": 155.168, "r_y1": 276.5, "r_x2": 155.168, "r_y2": 267.948, "r_x3": 142.993, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.432, "r_y0": 276.5, "r_x1": 178.799, "r_y1": 276.5, "r_x2": 178.799, "r_y2": 267.948, "r_x3": 159.432, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "table", "orig": "table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.073, "r_y0": 276.5, "r_x1": 217.932, "r_y1": 276.5, "r_x2": 217.932, "r_y2": 267.948, "r_x3": 183.073, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 222.206, "r_y0": 276.5, "r_x1": 286.365, "r_y1": 276.5, "r_x2": 286.365, "r_y2": 267.948, "r_x3": 222.206, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "from an image.", "orig": "from an image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.455, "r_x1": 286.365, "r_y1": 288.455, "r_x2": 286.365, "r_y2": 279.903, "r_x3": 50.112, "r_y3": 279.903, "coord_origin": "TOPLEFT"}, "text": "All the more, our model outperforms pre-trained methods.", "orig": "All the more, our model outperforms pre-trained methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.411, "r_x1": 286.365, "r_y1": 300.411, "r_x2": 286.365, "r_y2": 291.859, "r_x3": 50.112, "r_y3": 291.859, "coord_origin": "TOPLEFT"}, "text": "During the evaluation we do not apply any table filtering.", "orig": "During the evaluation we do not apply any table filtering.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.366, "r_x1": 286.365, "r_y1": 312.366, "r_x2": 286.365, "r_y2": 303.814, "r_x3": 50.112, "r_y3": 303.814, "coord_origin": "TOPLEFT"}, "text": "We also provide our baseline results on the SynthTabNet", "orig": "We also provide our baseline results on the SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.321, "r_x1": 80.269, "r_y1": 324.321, "r_x2": 80.269, "r_y2": 315.769, "r_x3": 50.112, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 85.579, "r_y0": 324.321, "r_x1": 91.666, "r_y1": 324.321, "r_x2": 91.666, "r_y2": 315.769, "r_x3": 85.579, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "It", "orig": "It", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 94.894, "r_y0": 324.321, "r_x1": 257.812, "r_y1": 324.321, "r_x2": 257.812, "r_y2": 315.769, "r_x3": 94.894, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "has been observed that large tables (e.g.", "orig": "has been observed that large tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.122, "r_y0": 324.321, "r_x1": 286.365, "r_y1": 324.321, "r_x2": 286.365, "r_y2": 315.769, "r_x3": 263.122, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "tables", "orig": "tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.276, "r_x1": 286.365, "r_y1": 336.276, "r_x2": 286.365, "r_y2": 327.724, "r_x3": 50.112, "r_y3": 327.724, "coord_origin": "TOPLEFT"}, "text": "that occupy half of the page or more) yield poor predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.203, "r_y0": 360.186, "r_x1": 134.147, "r_y1": 360.186, "r_x2": 134.147, "r_y2": 351.634, "r_x3": 119.203, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "that", "orig": "that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.952, "r_y0": 360.186, "r_x1": 286.365, "r_y1": 360.186, "r_x2": 286.365, "r_y2": 351.634, "r_x3": 137.952, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "produces downsampled images with", "orig": "produces downsampled images with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.142, "r_x1": 155.357, "r_y1": 372.142, "r_x2": 155.357, "r_y2": 363.59, "r_x3": 50.112, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "indistinguishable features.", "orig": "indistinguishable features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1357727050781, "r": 283.0443420410156, "b": 582.3977661132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9892510175704956, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.843, "r_y0": 429.511, "r_x1": 104.855, "r_y1": 429.511, "r_x2": 104.855, "r_y2": 420.959, "r_x3": 78.843, "r_y3": 420.959, "coord_origin": "TOPLEFT"}, "text": "Model", "orig": "Model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.2, "r_y0": 423.533, "r_x1": 236.107, "r_y1": 423.533, "r_x2": 236.107, "r_y2": 414.981, "r_x3": 211.2, "r_y3": 414.981, "coord_origin": "TOPLEFT"}, "text": "TEDS", "orig": "TEDS", "text_direction": 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"bounding boxes that can be improved", "orig": "bounding boxes that can be improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.2, "r_x1": 286.365, "r_y1": 677.2, "r_x2": 286.365, "r_y2": 668.648, "r_x3": 50.112, "r_y3": 668.648, "coord_origin": "TOPLEFT"}, "text": "with post-processing during inference. We make use of the", "orig": "with post-processing during inference. We make use of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 259.655, "r_y1": 689.156, "r_x2": 259.655, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "grid-like structure of tables to refine the predictions.", "orig": "grid-like structure of tables to refine the predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.65, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 263.65, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "A de-", "orig": "A de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tailed explanation on the post-processing is available in the", "orig": "tailed explanation on the post-processing is available in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 147.068, "r_y1": 713.066, "r_x2": 147.068, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "supplementary material.", "orig": "supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.159, "r_y0": 713.066, "r_x1": 223.003, "r_y1": 713.066, "r_x2": 223.003, "r_y2": 704.514, "r_x3": 152.159, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "As shown in Tab.", "orig": "As shown in Tab.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.094, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 228.094, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "3, we evaluate", "orig": "3, we evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.298, "r": 545.115, "b": 227.49099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9713197946548462, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 322.142, "r_y1": 84.029, "r_x2": 322.142, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 325.454, "r_y0": 83.88599999999997, "r_x1": 404.567, "r_y1": 83.88599999999997, "r_x2": 404.567, "r_y2": 75.298, "r_x3": 325.454, "r_y3": 75.298, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.104, "r_y0": 84.029, "r_x1": 545.11, "r_y1": 84.029, "r_x2": 545.11, "r_y2": 75.47699999999998, "r_x3": 408.104, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "accuracy for cells with a class la-", "orig": "accuracy for cells with a class la-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "bel of 'content' only using the PASCAL VOC mAP metric", "orig": "bel of 'content' only using the PASCAL VOC mAP metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 320.478, "r_y1": 107.93899999999996, "r_x2": 320.478, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 324.374, "r_y0": 107.93899999999996, "r_x1": 470.226, "r_y1": 107.93899999999996, "r_x2": 470.226, "r_y2": 99.38699999999994, "r_x3": 324.374, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "pre-processing and post-processing.", "orig": "pre-processing and post-processing.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.529, "r_y0": 107.93899999999996, "r_x1": 545.115, "r_y1": 107.93899999999996, "r_x2": 545.115, "r_y2": 99.38699999999994, "r_x3": 477.529, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "Note that we do", "orig": "Note that we do", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 119.894, "r_x1": 545.115, "r_y1": 119.894, "r_x2": 545.115, "r_y2": 111.34199999999998, "r_x3": 308.862, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "not have post-processing results for SynthTabNet as images", "orig": "not have post-processing results for SynthTabNet as images", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 131.84900000000005, "r_x1": 545.115, "r_y1": 131.84900000000005, "r_x2": 545.115, "r_y2": 123.29700000000003, "r_x3": 308.862, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "are only provided. To compare the performance of our pro-", "orig": "are only provided. To compare the performance of our pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 143.80499999999995, "r_x1": 502.017, "r_y1": 143.80499999999995, "r_x2": 502.017, "r_y2": 135.25300000000004, "r_x3": 308.862, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "posed approach, we've integrated TableFormer's", "orig": "posed approach, we've integrated TableFormer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 504.473, "r_y0": 143.66200000000003, "r_x1": 545.11, "r_y1": 143.66200000000003, "r_x2": 545.11, "r_y2": 135.07399999999996, "r_x3": 504.473, "r_y3": 135.07399999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox", "orig": "Cell BBox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 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Our method", "orig": "data cell and compared with the ground-truth. Our method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 460.306, "r_x1": 350.237, "r_y1": 460.306, "r_x2": 350.237, "r_y2": 451.754, "r_x3": 308.862, "r_y3": 451.754, "coord_origin": "TOPLEFT"}, "text": "achieved a", "orig": "achieved a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.176, "r_y0": 460.322, "r_x1": 374.592, "r_y1": 460.322, "r_x2": 374.592, "r_y2": 451.366, "r_x3": 352.176, "r_y3": 451.366, "coord_origin": "TOPLEFT"}, "text": "5.3%", "orig": "5.3%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.533, "r_y0": 460.306, "r_x1": 545.11, "r_y1": 460.306, "r_x2": 545.11, "r_y2": 451.754, "r_x3": 376.533, "r_y3": 451.754, "coord_origin": "TOPLEFT"}, "text": "increase over the state-of-the-art, and com-", "orig": "increase over the state-of-the-art, and com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 472.261, "r_x1": 380.523, "r_y1": 472.261, "r_x2": 380.523, "r_y2": 463.709, "r_x3": 308.862, "r_y3": 463.709, "coord_origin": "TOPLEFT"}, "text": "mercial solutions.", "orig": "mercial solutions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 386.042, "r_y0": 472.261, "r_x1": 545.115, "r_y1": 472.261, "r_x2": 545.115, "r_y2": 463.709, "r_x3": 386.042, "r_y3": 463.709, "coord_origin": "TOPLEFT"}, "text": "We believe our scores would be higher", "orig": "We believe our scores would be higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.217, "r_x1": 314.949, "r_y1": 484.217, "r_x2": 314.949, "r_y2": 475.665, "r_x3": 308.862, "r_y3": 475.665, "coord_origin": "TOPLEFT"}, "text": "if", "orig": "if", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.008, "r_y0": 484.217, "r_x1": 545.115, "r_y1": 484.217, "r_x2": 545.115, "r_y2": 475.665, "r_x3": 318.008, "r_y3": 475.665, "coord_origin": "TOPLEFT"}, "text": "the HTML ground-truth matched the extracted PDF cell", "orig": "the HTML ground-truth matched the extracted PDF cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.172, "r_x1": 340.683, "r_y1": 496.172, "r_x2": 340.683, "r_y2": 487.62, "r_x3": 308.862, "r_y3": 487.62, "coord_origin": "TOPLEFT"}, "text": "content.", "orig": "content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.793, "r_y0": 496.172, "r_x1": 545.115, "r_y1": 496.172, "r_x2": 545.115, "r_y2": 487.62, "r_x3": 345.793, "r_y3": 487.62, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, there are small discrepancies such", "orig": "Unfortunately, there are small discrepancies such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.127, "r_x1": 545.115, "r_y1": 508.127, "r_x2": 545.115, "r_y2": 499.575, "r_x3": 308.862, "r_y3": 499.575, "coord_origin": "TOPLEFT"}, "text": "as spacings around words or special characters with various", "orig": "as spacings around words or special characters with various", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.082, "r_x1": 405.698, "r_y1": 520.082, "r_x2": 405.698, "r_y2": 511.53, "r_x3": 308.862, "r_y3": 511.53, "coord_origin": "TOPLEFT"}, "text": "unicode representations.", "orig": "unicode representations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835693359375, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775567650794983, "cells": [{"index": 215, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.011, "r_y0": 561.054, "r_x1": 384.023, "r_y1": 561.054, "r_x2": 384.023, "r_y2": 552.502, "r_x3": 358.011, "r_y3": 552.502, "coord_origin": "TOPLEFT"}, "text": "Model", "orig": "Model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 216, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.034, "r_y0": 555.076, "r_x1": 473.94, "r_y1": 555.076, "r_x2": 473.94, "r_y2": 546.524, "r_x3": 449.034, "r_y3": 546.524, "coord_origin": "TOPLEFT"}, "text": "TEDS", "orig": "TEDS", 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677.637, "r_x2": 545.115, "r_y2": 669.085, "r_x3": 430.336, "r_y3": 669.085, "coord_origin": "TOPLEFT"}, "text": "In all cases the input is PDF", "orig": "In all cases the input is PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 248, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.592, "r_x1": 435.038, "r_y1": 689.592, "r_x2": 435.038, "r_y2": 681.04, "r_x3": 308.862, "r_y3": 681.04, "coord_origin": "TOPLEFT"}, "text": "documents with cropped tables.", "orig": "documents with cropped tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. 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Datasets and Metrics", "orig": "5.3. Datasets and Metrics", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.61900000000003, "r": 286.365, "b": 138.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 102.17100000000005, "r_x1": 286.365, "r_y1": 102.17100000000005, "r_x2": 286.365, "r_y2": 93.61900000000003, "r_x3": 62.067, "r_y3": 93.61900000000003, "coord_origin": "TOPLEFT"}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 114.12599999999998, "r_x1": 286.365, "r_y1": 114.12599999999998, "r_x2": 286.365, "r_y2": 105.57399999999996, "r_x3": 50.112, "r_y3": 105.57399999999996, "coord_origin": "TOPLEFT"}, "text": "ric was introduced in [37]. It represents the prediction, and", "orig": "ric was introduced in [37]. It represents the prediction, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 126.08100000000002, "r_x1": 239.999, "r_y1": 126.08100000000002, "r_x2": 239.999, "r_y2": 117.529, "r_x3": 50.112, "r_y3": 117.529, "coord_origin": "TOPLEFT"}, "text": "ground-truth as a tree structure of HTML tags.", "orig": "ground-truth as a tree structure of HTML tags.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.03, "r_y0": 126.08100000000002, "r_x1": 286.365, "r_y1": 126.08100000000002, "r_x2": 286.365, "r_y2": 117.529, "r_x3": 245.03, "r_y3": 117.529, "coord_origin": "TOPLEFT"}, "text": "This simi-", "orig": "This simi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 138.03700000000003, "r_x1": 136.717, "r_y1": 138.03700000000003, "r_x2": 136.717, "r_y2": 129.485, "r_x3": 50.112, "r_y3": 129.485, "coord_origin": "TOPLEFT"}, "text": "larity is calculated as:", "orig": "larity is calculated as:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. 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286.365, "r_y2": 303.814, "r_x3": 50.112, "r_y3": 303.814, "coord_origin": "TOPLEFT"}, "text": "We also provide our baseline results on the SynthTabNet", "orig": "We also provide our baseline results on the SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.321, "r_x1": 80.269, "r_y1": 324.321, "r_x2": 80.269, "r_y2": 315.769, "r_x3": 50.112, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 85.579, "r_y0": 324.321, "r_x1": 91.666, "r_y1": 324.321, "r_x2": 91.666, "r_y2": 315.769, "r_x3": 85.579, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "It", "orig": "It", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 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predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, 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features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1357727050781, "r": 283.0443420410156, "b": 582.3977661132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9892510175704956, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.843, "r_y0": 429.511, "r_x1": 104.855, "r_y1": 429.511, "r_x2": 104.855, "r_y2": 420.959, "r_x3": 78.843, "r_y3": 420.959, "coord_origin": "TOPLEFT"}, "text": "Model", "orig": "Model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.2, "r_y0": 423.533, "r_x1": 236.107, "r_y1": 423.533, "r_x2": 236.107, "r_y2": 414.981, "r_x3": 211.2, "r_y3": 414.981, "coord_origin": "TOPLEFT"}, "text": "TEDS", "orig": "TEDS", "text_direction": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.102, "r_x1": 84.971, "r_y1": 665.102, "r_x2": 84.971, "r_y2": 656.514, "r_x3": 50.112, "r_y3": 656.514, "coord_origin": "TOPLEFT"}, "text": "Detector", "orig": "Detector", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 89.515, "r_y0": 665.245, "r_x1": 123.677, "r_y1": 665.245, "r_x2": 123.677, "r_y2": 656.693, "r_x3": 89.515, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "provides", "orig": "provides", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.991, "r_y0": 665.245, "r_x1": 286.366, "r_y1": 665.245, "r_x2": 286.366, "r_y2": 656.693, "r_x3": 127.991, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "bounding boxes that can be improved", "orig": "bounding boxes that can be improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.2, "r_x1": 286.365, "r_y1": 677.2, "r_x2": 286.365, "r_y2": 668.648, "r_x3": 50.112, "r_y3": 668.648, "coord_origin": "TOPLEFT"}, "text": "with post-processing during inference. We make use of the", "orig": "with post-processing during inference. 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Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.298, "r": 545.115, "b": 227.49099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9713197946548462, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 322.142, "r_y1": 84.029, "r_x2": 322.142, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 325.454, "r_y0": 83.88599999999997, "r_x1": 404.567, "r_y1": 83.88599999999997, "r_x2": 404.567, "r_y2": 75.298, "r_x3": 325.454, "r_y3": 75.298, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.104, "r_y0": 84.029, "r_x1": 545.11, "r_y1": 84.029, "r_x2": 545.11, "r_y2": 75.47699999999998, "r_x3": 408.104, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "accuracy for cells with a class la-", "orig": "accuracy for cells with a class la-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "bel of 'content' only using the PASCAL VOC mAP metric", "orig": "bel of 'content' only using the PASCAL VOC mAP metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 320.478, "r_y1": 107.93899999999996, "r_x2": 320.478, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 324.374, "r_y0": 107.93899999999996, "r_x1": 470.226, "r_y1": 107.93899999999996, "r_x2": 470.226, "r_y2": 99.38699999999994, "r_x3": 324.374, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "pre-processing and post-processing.", "orig": "pre-processing and post-processing.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.529, "r_y0": 107.93899999999996, "r_x1": 545.115, "r_y1": 107.93899999999996, "r_x2": 545.115, "r_y2": 99.38699999999994, "r_x3": 477.529, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "Note that we do", "orig": "Note that we do", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 119.894, "r_x1": 545.115, "r_y1": 119.894, "r_x2": 545.115, "r_y2": 111.34199999999998, "r_x3": 308.862, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "not have post-processing results for SynthTabNet as images", "orig": "not have post-processing results for SynthTabNet as images", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 131.84900000000005, "r_x1": 545.115, "r_y1": 131.84900000000005, "r_x2": 545.115, "r_y2": 123.29700000000003, "r_x3": 308.862, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "are only provided. To compare the performance of our pro-", "orig": "are only provided. 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Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . 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PP: Post-processing.", "orig": "Net, and FinTabNet. PP: Post-processing.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 367.68, "r": 545.116, "b": 520.082, "coord_origin": "TOPLEFT"}, "confidence": 0.9835010766983032, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 376.636, "r_x1": 378.949, "r_y1": 376.636, "r_x2": 378.949, "r_y2": 367.68, "r_x3": 320.817, "r_y3": 367.68, "coord_origin": "TOPLEFT"}, "text": "Cell Content.", "orig": "Cell Content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.079, "r_y0": 376.62, "r_x1": 395.378, "r_y1": 376.62, "r_x2": 395.378, "r_y2": 368.068, "r_x3": 387.079, "r_y3": 368.068, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 399.552, "r_y0": 376.62, "r_x1": 413.948, "r_y1": 376.62, "r_x2": 413.948, "r_y2": 368.068, "r_x3": 399.552, "r_y3": 368.068, "coord_origin": "TOPLEFT"}, "text": "this", "orig": "this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 418.112, "r_y0": 376.62, "r_x1": 448.827, "r_y1": 376.62, "r_x2": 448.827, "r_y2": 368.068, "r_x3": 418.112, "r_y3": 368.068, "coord_origin": "TOPLEFT"}, "text": "section,", "orig": "section,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.42, "r_y0": 376.62, "r_x1": 545.116, "r_y1": 376.62, "r_x2": 545.116, "r_y2": 368.068, "r_x3": 453.42, "r_y3": 368.068, "coord_origin": "TOPLEFT"}, "text": "we evaluate the entire", "orig": "we evaluate the entire", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.575, "r_x1": 487.193, "r_y1": 388.575, "r_x2": 487.193, "r_y2": 380.023, "r_x3": 308.862, "r_y3": 380.023, "coord_origin": "TOPLEFT"}, "text": "pipeline of recovering a table with content.", "orig": "pipeline of recovering a table with content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.967, "r_y0": 388.575, "r_x1": 545.115, "r_y1": 388.575, "r_x2": 545.115, "r_y2": 380.023, "r_x3": 493.967, "r_y3": 380.023, "coord_origin": "TOPLEFT"}, "text": "Here we put", "orig": "Here we put", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.53, "r_x1": 545.115, "r_y1": 400.53, "r_x2": 545.115, "r_y2": 391.978, "r_x3": 308.862, "r_y3": 391.978, "coord_origin": "TOPLEFT"}, "text": "our approach to test by capitalizing on extracting content", "orig": "our approach to test by capitalizing on extracting content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.486, "r_x1": 545.115, "r_y1": 412.486, "r_x2": 545.115, "r_y2": 403.934, "r_x3": 308.862, "r_y3": 403.934, "coord_origin": "TOPLEFT"}, "text": "from the PDF cells rather than decoding from images. Tab.", "orig": "from the PDF cells rather than decoding from images. Tab.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.441, "r_x1": 545.115, "r_y1": 424.441, "r_x2": 545.115, "r_y2": 415.889, "r_x3": 308.862, "r_y3": 415.889, "coord_origin": "TOPLEFT"}, "text": "4 shows the TEDs score of HTML code representing the", "orig": "4 shows the TEDs score of HTML code representing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.396, "r_x1": 545.115, "r_y1": 436.396, "r_x2": 545.115, "r_y2": 427.844, "r_x3": 308.862, "r_y3": 427.844, "coord_origin": "TOPLEFT"}, "text": "structure of the table along with the content inserted in the", "orig": "structure of the table along with the content inserted in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.351, "r_x1": 545.115, "r_y1": 448.351, "r_x2": 545.115, "r_y2": 439.799, "r_x3": 308.862, "r_y3": 439.799, "coord_origin": "TOPLEFT"}, "text": "data cell and compared with the ground-truth. Our method", "orig": "data cell and compared with the ground-truth. Our method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 460.306, "r_x1": 350.237, "r_y1": 460.306, "r_x2": 350.237, "r_y2": 451.754, "r_x3": 308.862, "r_y3": 451.754, "coord_origin": "TOPLEFT"}, "text": "achieved a", "orig": "achieved a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.176, "r_y0": 460.322, "r_x1": 374.592, "r_y1": 460.322, "r_x2": 374.592, "r_y2": 451.366, "r_x3": 352.176, "r_y3": 451.366, "coord_origin": "TOPLEFT"}, "text": "5.3%", "orig": "5.3%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.533, "r_y0": 460.306, "r_x1": 545.11, "r_y1": 460.306, "r_x2": 545.11, "r_y2": 451.754, "r_x3": 376.533, "r_y3": 451.754, "coord_origin": "TOPLEFT"}, "text": "increase over the state-of-the-art, and com-", "orig": "increase over the state-of-the-art, and com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 472.261, "r_x1": 380.523, "r_y1": 472.261, "r_x2": 380.523, "r_y2": 463.709, "r_x3": 308.862, "r_y3": 463.709, "coord_origin": "TOPLEFT"}, "text": "mercial solutions.", "orig": "mercial solutions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 386.042, "r_y0": 472.261, "r_x1": 545.115, "r_y1": 472.261, "r_x2": 545.115, "r_y2": 463.709, "r_x3": 386.042, "r_y3": 463.709, "coord_origin": "TOPLEFT"}, "text": "We believe our scores would be higher", "orig": "We believe our scores would be higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.217, "r_x1": 314.949, "r_y1": 484.217, "r_x2": 314.949, "r_y2": 475.665, "r_x3": 308.862, "r_y3": 475.665, "coord_origin": "TOPLEFT"}, "text": "if", "orig": "if", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.008, "r_y0": 484.217, "r_x1": 545.115, "r_y1": 484.217, "r_x2": 545.115, "r_y2": 475.665, "r_x3": 318.008, "r_y3": 475.665, "coord_origin": "TOPLEFT"}, "text": "the HTML ground-truth matched the extracted PDF cell", "orig": "the HTML ground-truth matched the extracted PDF cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.172, "r_x1": 340.683, "r_y1": 496.172, "r_x2": 340.683, "r_y2": 487.62, "r_x3": 308.862, "r_y3": 487.62, "coord_origin": "TOPLEFT"}, "text": "content.", "orig": "content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.793, "r_y0": 496.172, "r_x1": 545.115, "r_y1": 496.172, "r_x2": 545.115, "r_y2": 487.62, "r_x3": 345.793, "r_y3": 487.62, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, there are small discrepancies such", "orig": "Unfortunately, there are small discrepancies such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.127, "r_x1": 545.115, "r_y1": 508.127, "r_x2": 545.115, "r_y2": 499.575, "r_x3": 308.862, "r_y3": 499.575, "coord_origin": "TOPLEFT"}, "text": "as spacings around words or special characters with various", "orig": "as spacings around words or special characters with various", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.082, "r_x1": 405.698, "r_y1": 520.082, "r_x2": 405.698, "r_y2": 511.53, "r_x3": 308.862, "r_y3": 511.53, "coord_origin": "TOPLEFT"}, "text": "unicode representations.", "orig": "unicode representations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835693359375, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775567650794983, "cells": [{"index": 215, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.011, "r_y0": 561.054, "r_x1": 384.023, "r_y1": 561.054, "r_x2": 384.023, "r_y2": 552.502, "r_x3": 358.011, "r_y3": 552.502, "coord_origin": "TOPLEFT"}, "text": "Model", "orig": "Model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 216, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.034, "r_y0": 555.076, "r_x1": 473.94, "r_y1": 555.076, "r_x2": 473.94, "r_y2": 546.524, "r_x3": 449.034, "r_y3": 546.524, "coord_origin": "TOPLEFT"}, "text": "TEDS", "orig": "TEDS", 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"confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.384, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 281.384, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "As it is shown, our model is able to predict bounding boxes", "orig": "As it is shown, our model is able to predict bounding boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 122.341, "r_y1": 593.514, "r_x2": 122.341, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "for all table cells,", "orig": "for all table cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.107, "r_y0": 593.514, "r_x1": 228.004, "r_y1": 593.514, "r_x2": 228.004, "r_y2": 584.962, "r_x3": 126.107, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "even for the empty ones.", "orig": "even for the empty ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.151, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 234.151, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "our post-processing techniques can extract the cell content", "orig": "our post-processing techniques can extract the cell content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "by matching the predicted bounding boxes to the PDF cells", "orig": "by matching the predicted bounding boxes to the PDF cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 230.873, "r_y1": 629.38, "r_x2": 230.873, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "based on their overlap and spatial proximity.", "orig": "based on their overlap and spatial proximity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.855, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 235.855, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "The left part", "orig": "The left part", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 77.121, "r_y1": 641.335, "r_x2": 77.121, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "of Fig.", "orig": "of Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.544, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 81.544, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "5 demonstrates also the adaptability of our method", "orig": "5 demonstrates also the adaptability of our method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 57.863, "r_y1": 653.29, "r_x2": 57.863, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.436, "r_y0": 653.29, "r_x1": 119.691, "r_y1": 653.29, "r_x2": 119.691, "r_y2": 644.738, "r_x3": 62.436, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "any language,", "orig": "any language,", "text_direction": 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"TOPLEFT"}, "text": "text, although the training set contains only English content.", "orig": "text, although the training set contains only English content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "We provide more visualizations including the intermediate", "orig": "We provide more visualizations including the intermediate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 195.217, "r_y1": 689.156, "r_x2": 195.217, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.431, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 199.431, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Overall these illustra-", "orig": "Overall these illustra-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tions justify the versatility of our method across a diverse", "orig": "tions justify the versatility of our method across a diverse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.669, "r_x1": 545.115, "r_y1": 533.669, "r_x2": 545.115, "r_y2": 525.117, "r_x3": 308.862, "r_y3": 525.117, "coord_origin": "TOPLEFT"}, "text": "transformer based approach to predict table structures and", "orig": "transformer based approach to predict table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.624, "r_x1": 545.115, "r_y1": 545.624, "r_x2": 545.115, "r_y2": 537.072, "r_x3": 308.862, "r_y3": 537.072, "coord_origin": "TOPLEFT"}, "text": "bounding boxes of cells from an image. This approach en-", "orig": "bounding boxes of cells from an image. 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"coord_origin": "TOPLEFT"}, "text": "scenarios when dealing with various types of PDF docu-", "orig": "scenarios when dealing with various types of PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 215, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.4, "r_x1": 335.153, "r_y1": 605.4, "r_x2": 335.153, "r_y2": 596.848, "r_x3": 308.862, "r_y3": 596.848, "coord_origin": "TOPLEFT"}, "text": "ments,", "orig": "ments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 216, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.656, "r_y0": 605.4, "r_x1": 400.468, "r_y1": 605.4, "r_x2": 400.468, "r_y2": 596.848, "r_x3": 339.656, "r_y3": 596.848, "coord_origin": "TOPLEFT"}, "text": "and languages.", "orig": "and languages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 217, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 220, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.234, "r_y0": 617.355, "r_x1": 545.115, "r_y1": 617.355, "r_x2": 545.115, "r_y2": 608.803, "r_x3": 500.234, "r_y3": 608.803, "coord_origin": "TOPLEFT"}, "text": "Finally, we", "orig": "Finally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 221, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.31, "r_x1": 545.115, "r_y1": 629.31, "r_x2": 545.115, "r_y2": 620.758, "r_x3": 308.862, "r_y3": 620.758, "coord_origin": "TOPLEFT"}, "text": "introduce 'SynthTabNet' a challenging synthetically gen-", "orig": "introduce 'SynthTabNet' a challenging synthetically gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 222, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.265, "r_x1": 545.115, "r_y1": 641.265, "r_x2": 545.115, "r_y2": 632.713, "r_x3": 308.862, "r_y3": 632.713, "coord_origin": "TOPLEFT"}, "text": "erated dataset that reinforces missing characteristics from", "orig": "erated dataset that reinforces missing characteristics from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 223, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.22, "r_x1": 365.858, "r_y1": 653.22, "r_x2": 365.858, "r_y2": 644.668, "r_x3": 308.862, "r_y3": 644.668, "coord_origin": "TOPLEFT"}, "text": "other datasets.", "orig": "other datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 224, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 682.847, "r_x1": 364.406, "r_y1": 682.847, "r_x2": 364.406, "r_y2": 672.099, "r_x3": 308.862, "r_y3": 672.099, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 225, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 313.345, "r_y0": 701.901, "r_x1": 323.8, "r_y1": 701.901, "r_x2": 323.8, "r_y2": 694.204, "r_x3": 313.345, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 226, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.785, "r_y0": 701.901, "r_x1": 545.109, "r_y1": 701.901, "r_x2": 545.109, "r_y2": 694.204, "r_x3": 328.785, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 227, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.781, "r_y0": 712.86, "r_x1": 545.113, "r_y1": 712.86, "r_x2": 545.113, "r_y2": 705.163, "r_x3": 328.781, "r_y3": 705.163, "coord_origin": "TOPLEFT"}, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "orig": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 122.341, "r_y1": 593.514, "r_x2": 122.341, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "for all table cells,", "orig": "for all table cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.107, "r_y0": 593.514, "r_x1": 228.004, "r_y1": 593.514, "r_x2": 228.004, "r_y2": 584.962, "r_x3": 126.107, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "even for the empty ones.", "orig": "even for the empty ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.151, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 234.151, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "our post-processing techniques can extract the cell content", "orig": "our post-processing techniques can extract the cell content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "by matching the predicted bounding boxes to the PDF cells", "orig": "by matching the predicted bounding boxes to the PDF cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 230.873, "r_y1": 629.38, "r_x2": 230.873, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "based on their overlap and spatial proximity.", "orig": "based on their overlap and spatial proximity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.855, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 235.855, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "The left part", "orig": "The left part", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 77.121, "r_y1": 641.335, "r_x2": 77.121, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "of Fig.", "orig": "of Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.544, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 81.544, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "5 demonstrates also the adaptability of our method", "orig": "5 demonstrates also the adaptability of our method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 57.863, "r_y1": 653.29, "r_x2": 57.863, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.436, "r_y0": 653.29, "r_x1": 119.691, 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255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "text, although the training set contains only English content.", "orig": "text, although the training set contains only English content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "We provide more visualizations including the intermediate", "orig": "We provide more visualizations including the intermediate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 195.217, "r_y1": 689.156, "r_x2": 195.217, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.431, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 199.431, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Overall these illustra-", "orig": "Overall these illustra-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tions justify the versatility of our method across a diverse", "orig": "tions justify the versatility of our method across a diverse", 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Future Work & Conclusion", "orig": "6. 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"r_y3": 333.101, "coord_origin": "TOPLEFT"}, "text": "demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is", "orig": "demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 353.608, "r_x1": 545.114, "r_y1": 353.608, "r_x2": 545.114, "r_y2": 345.056, "r_x3": 50.112, "r_y3": 345.056, "coord_origin": "TOPLEFT"}, "text": "robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from", "orig": "robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 365.564, "r_x1": 139.795, "r_y1": 365.564, "r_x2": 139.795, "r_y2": 357.012, "r_x3": 50.112, "r_y3": 357.012, "coord_origin": "TOPLEFT"}, "text": "the FinTabNet dataset.", "orig": "the FinTabNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). 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"b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 122.341, "r_y1": 593.514, "r_x2": 122.341, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "for all table cells,", "orig": "for all table cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.107, "r_y0": 593.514, "r_x1": 228.004, "r_y1": 593.514, "r_x2": 228.004, "r_y2": 584.962, "r_x3": 126.107, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "even for the empty ones.", "orig": "even for the empty ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.151, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 234.151, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", 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"r_y2": 644.738, "r_x3": 147.765, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "can successfully", "orig": "can successfully", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.426, "r_y0": 653.29, "r_x1": 246.385, "r_y1": 653.29, "r_x2": 246.385, "r_y2": 644.738, "r_x3": 219.426, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "extract", "orig": "extract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.958, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 250.958, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "Japanese", "orig": "Japanese", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, 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"coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.431, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 199.431, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Overall these illustra-", "orig": "Overall these illustra-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tions justify the versatility of our method across a diverse", "orig": "tions justify the versatility of our method across a diverse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 226.888, "r_y1": 713.066, "r_x2": 226.888, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "range of table appearances and content type.", "orig": "range of table appearances and content type.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.709, "r": 460.848, "b": 501.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 501.457, "r_x1": 460.848, "r_y1": 501.457, "r_x2": 460.848, "r_y2": 490.709, "r_x3": 308.862, "r_y3": 490.709, "coord_origin": "TOPLEFT"}, "text": "6. Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 513.162, "r": 545.115, "b": 653.22, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.669, "r_x1": 545.115, "r_y1": 533.669, "r_x2": 545.115, "r_y2": 525.117, "r_x3": 308.862, "r_y3": 525.117, "coord_origin": "TOPLEFT"}, "text": "transformer based approach to predict table structures and", "orig": "transformer based approach to predict table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.624, "r_x1": 545.115, "r_y1": 545.624, "r_x2": 545.115, "r_y2": 537.072, "r_x3": 308.862, "r_y3": 537.072, "coord_origin": "TOPLEFT"}, "text": "bounding boxes of cells from an image. This approach en-", "orig": "bounding boxes of cells from an image. 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 220, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.234, "r_y0": 617.355, "r_x1": 545.115, "r_y1": 617.355, "r_x2": 545.115, "r_y2": 608.803, "r_x3": 500.234, "r_y3": 608.803, "coord_origin": "TOPLEFT"}, "text": "Finally, we", "orig": "Finally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 221, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.31, "r_x1": 545.115, "r_y1": 629.31, "r_x2": 545.115, "r_y2": 620.758, "r_x3": 308.862, "r_y3": 620.758, "coord_origin": "TOPLEFT"}, "text": "introduce 'SynthTabNet' a challenging synthetically gen-", "orig": "introduce 'SynthTabNet' a challenging synthetically gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 222, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.265, "r_x1": 545.115, "r_y1": 641.265, "r_x2": 545.115, "r_y2": 632.713, "r_x3": 308.862, "r_y3": 632.713, "coord_origin": "TOPLEFT"}, "text": "erated dataset that reinforces missing characteristics from", "orig": "erated dataset that reinforces missing characteristics from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 223, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.22, "r_x1": 365.858, "r_y1": 653.22, "r_x2": 365.858, "r_y2": 644.668, "r_x3": 308.862, "r_y3": 644.668, "coord_origin": "TOPLEFT"}, "text": "other datasets.", "orig": "other datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.099, "r": 364.406, "b": 682.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9442470073699951, "cells": [{"index": 224, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 682.847, "r_x1": 364.406, "r_y1": 682.847, "r_x2": 364.406, "r_y2": 672.099, "r_x3": 308.862, "r_y3": 672.099, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 694.204, "r": 545.113, "b": 712.86, "coord_origin": "TOPLEFT"}, "confidence": 0.8318753838539124, "cells": [{"index": 225, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 313.345, "r_y0": 701.901, "r_x1": 323.8, "r_y1": 701.901, "r_x2": 323.8, "r_y2": 694.204, "r_x3": 313.345, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 226, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.785, "r_y0": 701.901, "r_x1": 545.109, "r_y1": 701.901, "r_x2": 545.109, "r_y2": 694.204, "r_x3": 328.785, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 227, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.781, "r_y0": 712.86, "r_x1": 545.113, "r_y1": 712.86, "r_x2": 545.113, "r_y2": 705.163, "r_x3": 328.781, "r_y3": 705.163, "coord_origin": "TOPLEFT"}, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "orig": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8709858059883118, "cells": [{"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.5836486816406, "t": 98.65415954589844, "r": 554.8258666992188, "b": 180.6267547607422, "coord_origin": "TOPLEFT"}, "confidence": 0.7699173092842102, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97503662109375, "t": 103.71266174316406, "r": 301.6335754394531, "b": 187.5789337158203, "coord_origin": "TOPLEFT"}, "confidence": 0.7873926162719727, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.812, "t": 208.54100000000005, "r": 385.934, "b": 216.02300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.5986505746841431, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 216.02300000000002, "r_x1": 385.934, "r_y1": 216.02300000000002, "r_x2": 385.934, "r_y2": 208.54100000000005, "r_x3": 53.812, "r_y3": 208.54100000000005, "coord_origin": "TOPLEFT"}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.812, "t": 94.58900000000006, "r": 284.346, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46452972292900085, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 102.07100000000003, "r_x1": 284.346, "r_y1": 102.07100000000003, "r_x2": 284.346, "r_y2": 94.58900000000006, "r_x3": 53.812, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Japanese language (previously unseen by TableFormer):", "orig": "Japanese language (previously unseen by TableFormer):", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.831, "t": 94.58900000000006, "r": 431.091, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4629225730895996, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 304.831, "r_y0": 102.07100000000003, "r_x1": 431.091, "r_y1": 102.07100000000003, "r_x2": 431.091, "r_y2": 94.58900000000006, "r_x3": 304.831, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Example table from FinTabNet:", "orig": "Example table from FinTabNet:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example table from FinTabNet:"}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 53.286, "t": 78.995, "r": 499.556, "b": 86.47699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.541211724281311, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.286, "r_y0": 86.47699999999998, "r_x1": 499.556, "r_y1": 86.47699999999998, "r_x2": 499.556, "r_y2": 78.995, "r_x3": 53.286, "r_y3": 78.995, "coord_origin": "TOPLEFT"}, "text": "a. 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"r_y3": 333.101, "coord_origin": "TOPLEFT"}, "text": "demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is", "orig": "demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 353.608, "r_x1": 545.114, "r_y1": 353.608, "r_x2": 545.114, "r_y2": 345.056, "r_x3": 50.112, "r_y3": 345.056, "coord_origin": "TOPLEFT"}, "text": "robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from", "orig": "robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 365.564, "r_x1": 139.795, "r_y1": 365.564, "r_x2": 139.795, "r_y2": 357.012, "r_x3": 50.112, "r_y3": 357.012, "coord_origin": "TOPLEFT"}, "text": "the FinTabNet dataset.", "orig": "the FinTabNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). 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"b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 122.341, "r_y1": 593.514, "r_x2": 122.341, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "for all table cells,", "orig": "for all table cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.107, "r_y0": 593.514, "r_x1": 228.004, "r_y1": 593.514, "r_x2": 228.004, "r_y2": 584.962, "r_x3": 126.107, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "even for the empty ones.", "orig": "even for the empty ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.151, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 234.151, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", 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"coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.431, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 199.431, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Overall these illustra-", "orig": "Overall these illustra-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tions justify the versatility of our method across a diverse", "orig": "tions justify the versatility of our method across a diverse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 226.888, "r_y1": 713.066, "r_x2": 226.888, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "range of table appearances and content type.", "orig": "range of table appearances and content type.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.709, "r": 460.848, "b": 501.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 501.457, "r_x1": 460.848, "r_y1": 501.457, "r_x2": 460.848, "r_y2": 490.709, "r_x3": 308.862, "r_y3": 490.709, "coord_origin": "TOPLEFT"}, "text": "6. Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 513.162, "r": 545.115, "b": 653.22, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.669, "r_x1": 545.115, "r_y1": 533.669, "r_x2": 545.115, "r_y2": 525.117, "r_x3": 308.862, "r_y3": 525.117, "coord_origin": "TOPLEFT"}, "text": "transformer based approach to predict table structures and", "orig": "transformer based approach to predict table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.624, "r_x1": 545.115, "r_y1": 545.624, "r_x2": 545.115, "r_y2": 537.072, "r_x3": 308.862, "r_y3": 537.072, "coord_origin": "TOPLEFT"}, "text": "bounding boxes of cells from an image. This approach en-", "orig": "bounding boxes of cells from an image. 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 220, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.234, "r_y0": 617.355, "r_x1": 545.115, "r_y1": 617.355, "r_x2": 545.115, "r_y2": 608.803, "r_x3": 500.234, "r_y3": 608.803, "coord_origin": "TOPLEFT"}, "text": "Finally, we", "orig": "Finally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 221, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.31, "r_x1": 545.115, "r_y1": 629.31, "r_x2": 545.115, "r_y2": 620.758, "r_x3": 308.862, "r_y3": 620.758, "coord_origin": "TOPLEFT"}, "text": "introduce 'SynthTabNet' a challenging synthetically gen-", "orig": "introduce 'SynthTabNet' a challenging synthetically gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 222, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.265, "r_x1": 545.115, "r_y1": 641.265, "r_x2": 545.115, "r_y2": 632.713, "r_x3": 308.862, "r_y3": 632.713, "coord_origin": "TOPLEFT"}, "text": "erated dataset that reinforces missing characteristics from", "orig": "erated dataset that reinforces missing characteristics from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 223, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.22, "r_x1": 365.858, "r_y1": 653.22, "r_x2": 365.858, "r_y2": 644.668, "r_x3": 308.862, "r_y3": 644.668, "coord_origin": "TOPLEFT"}, "text": "other datasets.", "orig": "other datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.099, "r": 364.406, "b": 682.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9442470073699951, "cells": [{"index": 224, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 682.847, "r_x1": 364.406, "r_y1": 682.847, "r_x2": 364.406, "r_y2": 672.099, "r_x3": 308.862, "r_y3": 672.099, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 694.204, "r": 545.113, "b": 712.86, "coord_origin": "TOPLEFT"}, "confidence": 0.8318753838539124, "cells": [{"index": 225, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 313.345, "r_y0": 701.901, "r_x1": 323.8, "r_y1": 701.901, "r_x2": 323.8, "r_y2": 694.204, "r_x3": 313.345, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 226, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.785, "r_y0": 701.901, "r_x1": 545.109, "r_y1": 701.901, "r_x2": 545.109, "r_y2": 694.204, "r_x3": 328.785, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 227, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.781, "r_y0": 712.86, "r_x1": 545.113, "r_y1": 712.86, "r_x2": 545.113, "r_y2": 705.163, "r_x3": 328.781, "r_y3": 705.163, "coord_origin": "TOPLEFT"}, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "orig": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.5836486816406, "t": 98.65415954589844, "r": 554.8258666992188, "b": 180.6267547607422, "coord_origin": "TOPLEFT"}, "confidence": 0.7699173092842102, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97503662109375, "t": 103.71266174316406, "r": 301.6335754394531, "b": 187.5789337158203, "coord_origin": "TOPLEFT"}, "confidence": 0.7873926162719727, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8709858059883118, "cells": [{"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.031, "r_y0": 83.82299999999998, "r_x1": 212.373, "r_y1": 83.82299999999998, "r_x2": 212.373, "r_y2": 76.12599999999998, "r_x3": 70.031, "r_y3": 76.12599999999998, "coord_origin": "TOPLEFT"}, "text": "end object detection with transformers.", "orig": "end object detection with transformers.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 217.313, "r_y0": 83.82299999999998, "r_x1": 286.363, "r_y1": 83.82299999999998, "r_x2": 286.363, "r_y2": 76.12599999999998, "r_x3": 217.313, "r_y3": 76.12599999999998, "coord_origin": "TOPLEFT"}, "text": "In Andrea Vedaldi,", "orig": "In Andrea Vedaldi,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.031, "r_y0": 94.78200000000004, "r_x1": 286.363, "r_y1": 94.78200000000004, "r_x2": 286.363, "r_y2": 87.08500000000004, "r_x3": 70.031, "r_y3": 87.08500000000004, "coord_origin": "TOPLEFT"}, "text": "Horst Bischof, Thomas Brox, and Jan-Michael Frahm, edi-", "orig": "Horst Bischof, Thomas Brox, and Jan-Michael Frahm, edi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.031, "r_y0": 105.74000000000001, "r_x1": 85.722, "r_y1": 105.74000000000001, "r_x2": 85.722, "r_y2": 98.043, "r_x3": 70.031, "r_y3": 98.043, "coord_origin": "TOPLEFT"}, "text": "tors,", "orig": "tors,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.889, "r_y0": 105.61099999999999, "r_x1": 199.933, "r_y1": 105.61099999999999, "r_x2": 199.933, "r_y2": 97.88099999999997, "r_x3": 87.889, "r_y3": 97.88099999999997, "coord_origin": "TOPLEFT"}, "text": "Computer Vision - ECCV 2020", "orig": "Computer Vision - ECCV 2020", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.936, "r_y0": 105.74000000000001, "r_x1": 286.363, "r_y1": 105.74000000000001, "r_x2": 286.363, "r_y2": 98.043, "r_x3": 199.936, "r_y3": 98.043, "coord_origin": "TOPLEFT"}, "text": ", pages 213-229, Cham,", "orig": ", pages 213-229, Cham,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.031, "r_y0": 116.69899999999996, "r_x1": 221.949, "r_y1": 116.69899999999996, "r_x2": 221.949, "r_y2": 109.00199999999995, "r_x3": 70.031, "r_y3": 109.00199999999995, "coord_origin": "TOPLEFT"}, "text": "2020. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1"}], "headers": [{"label": "page_footer", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 292.63, "t": 734.402, "r": 302.593, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.9069584012031555, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.63, "r_y0": 742.954, "r_x1": 302.593, "r_y1": 742.954, "r_x2": 302.593, "r_y2": 734.402, "r_x3": 292.63, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "10", "orig": "10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "10"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.184, "r_y0": 135.48400000000004, "r_x1": 375.043, "r_y1": 135.48400000000004, "r_x2": 375.043, "r_y2": 122.42399999999998, "r_x3": 220.184, "r_y3": 122.42399999999998, "coord_origin": "TOPLEFT"}, "text": "Supplementary Material", "orig": "Supplementary Material", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.382, "r_y0": 244.726, "r_x1": 492.911, "r_y1": 244.726, "r_x2": 492.911, "r_y2": 236.17399999999998, "r_x3": 335.382, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "Prepare styling and content templates:", "orig": "Prepare styling and content templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.669, "r_y0": 244.726, "r_x1": 545.115, "r_y1": 244.726, "r_x2": 545.115, "r_y2": 236.17399999999998, "r_x3": 498.669, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "The styling", "orig": "The styling", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.457, "r_x1": 425.693, "r_y1": 316.457, "r_x2": 425.693, "r_y2": 307.905, "r_x3": 308.862, "r_y3": 307.905, "coord_origin": "TOPLEFT"}, "text": "PubTabNet, FinTabNet, etc.).", "orig": "PubTabNet, FinTabNet, etc.).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.692, "r_y0": 329.171, "r_x1": 435.377, "r_y1": 329.171, "r_x2": 435.377, "r_y2": 320.619, "r_x3": 332.692, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "Generate table structures:", "orig": "Generate table structures:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.342, "r_y0": 329.171, "r_x1": 545.115, "r_y1": 329.171, "r_x2": 545.115, "r_y2": 320.619, "r_x3": 439.342, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "The structure of each syn-", "orig": "The structure of each syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 341.126, "r_x1": 545.115, "r_y1": 341.126, "r_x2": 545.115, "r_y2": 332.574, "r_x3": 308.862, "r_y3": 332.574, "coord_origin": "TOPLEFT"}, "text": "thetic dataset assumes a horizontal table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, "r_y1": 353.081, "r_x2": 545.115, "r_y2": 344.529, "r_x3": 431.462, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "rows and a table body that", "orig": "rows and a table body that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 365.037, "r_x1": 545.115, "r_y1": 365.037, "r_x2": 545.115, "r_y2": 356.485, "r_x3": 308.862, "r_y3": 356.485, "coord_origin": "TOPLEFT"}, "text": "may contain a combination of row spans and column spans.", "orig": "may contain a combination of row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.992, "r_x1": 545.115, "r_y1": 376.992, "r_x2": 545.115, "r_y2": 368.44, "r_x3": 308.862, "r_y3": 368.44, "coord_origin": "TOPLEFT"}, "text": "However, spans are not allowed to cross the header - body", "orig": "However, spans are not allowed to cross the header - body", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.947, "r_x1": 348.334, "r_y1": 388.947, "r_x2": 348.334, "r_y2": 380.395, "r_x3": 308.862, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "boundary.", "orig": "boundary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 353.504, 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rows and columns, number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.857, "r_x1": 545.115, "r_y1": 412.857, "r_x2": 545.115, "r_y2": 404.305, "r_x3": 308.862, "r_y3": 404.305, "coord_origin": "TOPLEFT"}, "text": "header rows, type of spans (header only spans, row only", "orig": "header rows, type of spans (header only spans, row only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.812, "r_x1": 333.49, "r_y1": 424.812, "r_x2": 333.49, "r_y2": 416.26, "r_x3": 308.862, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "spans,", "orig": "spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.082, "r_y0": 424.812, "r_x1": 418.66, "r_y1": 424.812, "r_x2": 418.66, "r_y2": 416.26, "r_x3": 338.082, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "column only spans,", "orig": "column only spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.253, "r_y0": 424.812, "r_x1": 545.115, "r_y1": 424.812, "r_x2": 545.115, "r_y2": 416.26, "r_x3": 423.253, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "both row and column spans),", "orig": "both row and column spans),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.768, "r_x1": 545.115, "r_y1": 436.768, "r_x2": 545.115, "r_y2": 428.216, "r_x3": 308.862, "r_y3": 428.216, "coord_origin": "TOPLEFT"}, "text": "maximum span size and the ratio of the table area covered", "orig": "maximum span size and the ratio of the table area covered", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.723, "r_x1": 345.943, "r_y1": 448.723, "r_x2": 345.943, "r_y2": 440.171, "r_x3": 308.862, "r_y3": 440.171, "coord_origin": "TOPLEFT"}, "text": "by spans.", "orig": "by spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.073, "r_y0": 461.294, "r_x1": 511.864, "r_y1": 461.294, "r_x2": 511.864, "r_y2": 452.706, "r_x3": 488.073, "r_y3": 452.706, "coord_origin": "TOPLEFT"}, "text": "theme", "orig": "theme", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 511.863, "r_y0": 461.437, "r_x1": 545.108, "r_y1": 461.437, "r_x2": 545.108, "r_y2": 452.885, "r_x3": 511.863, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": ", a set of", "orig": ", a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.392, "r_x1": 545.115, "r_y1": 473.392, "r_x2": 545.115, "r_y2": 464.84, "r_x3": 308.862, "r_y3": 464.84, "coord_origin": "TOPLEFT"}, "text": "suitable content templates is chosen first. Then, this content", "orig": "suitable content templates is chosen first. Then, this content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.348, "r_x1": 545.115, "r_y1": 485.348, "r_x2": 545.115, "r_y2": 476.796, "r_x3": 308.862, "r_y3": 476.796, "coord_origin": "TOPLEFT"}, "text": "can be combined with purely random text to produce the", "orig": "can be combined with purely random text to produce the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.303, "r_x1": 379.148, "r_y1": 497.303, "r_x2": 379.148, "r_y2": 488.751, "r_x3": 308.862, "r_y3": 488.751, "coord_origin": "TOPLEFT"}, "text": "synthetic content.", "orig": "synthetic content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.217, "r_y0": 510.017, "r_x1": 434.082, "r_y1": 510.017, "r_x2": 434.082, "r_y2": 501.465, "r_x3": 334.217, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Apply styling templates:", "orig": "Apply styling templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.063, "r_y0": 510.017, "r_x1": 545.115, "r_y1": 510.017, "r_x2": 545.115, "r_y2": 501.465, "r_x3": 439.063, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Depending on the domain", "orig": "Depending on the domain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.972, "r_x1": 406.884, "r_y1": 521.972, "r_x2": 406.884, "r_y2": 513.4200000000001, "r_x3": 308.862, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "of the synthetic dataset,", "orig": "of the synthetic dataset,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.019, "r_y0": 521.972, "r_x1": 545.115, "r_y1": 521.972, "r_x2": 545.115, "r_y2": 513.4200000000001, "r_x3": 411.019, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "a set of styling templates is first", "orig": "a set of styling templates is first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.928, "r_x1": 384.299, "r_y1": 533.928, "r_x2": 384.299, "r_y2": 525.376, "r_x3": 308.862, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "manually selected.", "orig": "manually selected.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.253, "r_y0": 533.928, "r_x1": 545.115, "r_y1": 533.928, "r_x2": 545.115, "r_y2": 525.376, "r_x3": 391.253, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "Then, a style is randomly selected to", "orig": "Then, a style is randomly selected to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.883, "r_x1": 496.159, "r_y1": 545.883, "r_x2": 496.159, "r_y2": 537.331, "r_x3": 308.862, "r_y3": 537.331, "coord_origin": "TOPLEFT"}, "text": "format the appearance of the synthesized table.", "orig": "format the appearance of the synthesized table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.402, "r_y0": 558.597, "r_x1": 450.371, "r_y1": 558.597, "r_x2": 450.371, "r_y2": 550.045, "r_x3": 335.402, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "Render the complete tables:", "orig": "Render the complete tables:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.139, "r_y0": 558.597, "r_x1": 545.115, "r_y1": 558.597, "r_x2": 545.115, "r_y2": 550.045, "r_x3": 456.139, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "The synthetic table is", "orig": "The synthetic table is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 570.552, "r_x1": 334.326, "r_y1": 570.552, "r_x2": 334.326, "r_y2": 562.0, "r_x3": 308.862, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "finally", "orig": "finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.983, "r_y0": 570.552, "r_x1": 545.115, "r_y1": 570.552, "r_x2": 545.115, "r_y2": 562.0, "r_x3": 337.983, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "rendered by a web browser engine to generate the", "orig": "rendered by a web browser engine to generate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 582.5070000000001, "r_x1": 545.115, "r_y1": 582.5070000000001, "r_x2": 545.115, "r_y2": 573.955, "r_x3": 308.862, "r_y3": 573.955, "coord_origin": "TOPLEFT"}, "text": "bounding boxes for each table cell. A batching technique is", "orig": "bounding boxes for each table cell. A batching technique is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 594.463, "r_x1": 545.115, "r_y1": 594.463, "r_x2": 545.115, "r_y2": 585.9110000000001, "r_x3": 308.862, "r_y3": 585.9110000000001, "coord_origin": "TOPLEFT"}, "text": "utilized to optimize the runtime overhead of the rendering", "orig": "utilized to optimize the runtime overhead of the rendering", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 606.418, "r_x1": 341.23, "r_y1": 606.418, "r_x2": 341.23, "r_y2": 597.866, "r_x3": 308.862, "r_y3": 597.866, "coord_origin": "TOPLEFT"}, "text": "process.", "orig": "process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 323.591, "r_y0": 633.039, "r_x1": 376.493, "r_y1": 633.039, "r_x2": 376.493, "r_y2": 622.2909999999999, "r_x3": 323.591, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "Prediction", "orig": "Prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 382.255, "r_y0": 633.039, "r_x1": 461.076, "r_y1": 633.039, "r_x2": 461.076, "r_y2": 622.2909999999999, "r_x3": 382.255, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "post-processing", "orig": "post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.826, "r_y0": 633.039, "r_x1": 481.794, "r_y1": 633.039, "r_x2": 481.794, "r_y2": 622.2909999999999, "r_x3": 466.826, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.556, "r_y0": 633.039, "r_x1": 545.109, "r_y1": 633.039, "r_x2": 545.109, "r_y2": 622.2909999999999, "r_x3": 487.556, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "PDF docu-", "orig": "PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.795, "r_y0": 646.987, "r_x1": 357.341, "r_y1": 646.987, "r_x2": 357.341, "r_y2": 636.239, "r_x3": 326.795, "r_y3": 636.239, "coord_origin": "TOPLEFT"}, "text": "ments", "orig": "ments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 678.197, "r_x1": 545.115, "r_y1": 678.197, "r_x2": 545.115, "r_y2": 669.645, "r_x3": 308.862, "r_y3": 669.645, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes for tables recognized inside PDF docu-", "orig": "the bounding boxes for tables recognized inside PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 690.152, "r_x1": 545.115, "r_y1": 690.152, "r_x2": 545.115, "r_y2": 681.6, "r_x3": 308.862, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "ments, this is not enough when a full reconstruction of the", "orig": "ments, this is not enough when a full reconstruction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 702.107, "r_x1": 545.115, "r_y1": 702.107, "r_x2": 545.115, "r_y2": 693.5550000000001, "r_x3": 308.862, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "original table is required. This happens mainly due the fol-", "orig": "original table is required. This happens mainly due the fol-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 714.063, "r_x1": 371.427, "r_y1": 714.063, "r_x2": 371.427, "r_y2": 705.511, "r_x3": 308.862, "r_y3": 705.511, "coord_origin": "TOPLEFT"}, "text": "lowing reasons:", "orig": "lowing reasons:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 135.48400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.184, "r_y0": 135.48400000000004, "r_x1": 375.043, "r_y1": 135.48400000000004, "r_x2": 375.043, "r_y2": 122.42399999999998, "r_x3": 220.184, "r_y3": 122.42399999999998, "coord_origin": "TOPLEFT"}, "text": "Supplementary Material", "orig": "Supplementary Material", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 200.18899999999996, "r": 286.365, "b": 388.069, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 391.674, "r": 286.365, "b": 627.374, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 630.979, "r": 286.365, "b": 651.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 681.6, "r": 286.365, "b": 714.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 545.115, "b": 207.34199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.50400000000002, "r": 545.115, "b": 232.01099999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 236.17399999999998, "r": 545.115, "b": 316.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": 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table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, 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"r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "original table is required. This happens mainly due the fol-", "orig": "original table is required. This happens mainly due the fol-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 714.063, "r_x1": 371.427, "r_y1": 714.063, "r_x2": 371.427, "r_y2": 705.511, "r_x3": 308.862, "r_y3": 705.511, "coord_origin": "TOPLEFT"}, "text": "lowing reasons:", "orig": "lowing reasons:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 135.48400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.184, "r_y0": 135.48400000000004, "r_x1": 375.043, "r_y1": 135.48400000000004, "r_x2": 375.043, "r_y2": 122.42399999999998, "r_x3": 220.184, "r_y3": 122.42399999999998, "coord_origin": "TOPLEFT"}, "text": "Supplementary Material", "orig": "Supplementary Material", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 200.18899999999996, "r": 286.365, "b": 388.069, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 391.674, "r": 286.365, "b": 627.374, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 630.979, "r": 286.365, "b": 651.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 681.6, "r": 286.365, "b": 714.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 545.115, "b": 207.34199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.50400000000002, "r": 545.115, "b": 232.01099999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 236.17399999999998, "r": 545.115, "b": 316.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.382, "r_y0": 244.726, "r_x1": 492.911, "r_y1": 244.726, "r_x2": 492.911, "r_y2": 236.17399999999998, "r_x3": 335.382, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "Prepare styling and content templates:", "orig": "Prepare styling and content templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.669, "r_y0": 244.726, "r_x1": 545.115, "r_y1": 244.726, "r_x2": 545.115, "r_y2": 236.17399999999998, "r_x3": 498.669, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "The styling", "orig": "The styling", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.457, "r_x1": 425.693, "r_y1": 316.457, "r_x2": 425.693, "r_y2": 307.905, "r_x3": 308.862, "r_y3": 307.905, "coord_origin": "TOPLEFT"}, "text": "PubTabNet, FinTabNet, etc.).", "orig": "PubTabNet, FinTabNet, etc.).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.619, "r": 545.115, "b": 448.723, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.692, "r_y0": 329.171, "r_x1": 435.377, "r_y1": 329.171, "r_x2": 435.377, "r_y2": 320.619, "r_x3": 332.692, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "Generate table structures:", "orig": "Generate table structures:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.342, "r_y0": 329.171, "r_x1": 545.115, "r_y1": 329.171, "r_x2": 545.115, "r_y2": 320.619, "r_x3": 439.342, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "The structure of each syn-", "orig": "The structure of each syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 341.126, "r_x1": 545.115, "r_y1": 341.126, "r_x2": 545.115, "r_y2": 332.574, "r_x3": 308.862, "r_y3": 332.574, "coord_origin": "TOPLEFT"}, "text": "thetic dataset assumes a horizontal table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, "r_y1": 353.081, "r_x2": 545.115, "r_y2": 344.529, "r_x3": 431.462, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "rows and a table body that", "orig": "rows and a table body that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 365.037, "r_x1": 545.115, "r_y1": 365.037, "r_x2": 545.115, "r_y2": 356.485, "r_x3": 308.862, "r_y3": 356.485, "coord_origin": "TOPLEFT"}, "text": "may contain a combination of row spans and column spans.", "orig": "may contain a combination of row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.992, "r_x1": 545.115, "r_y1": 376.992, "r_x2": 545.115, "r_y2": 368.44, "r_x3": 308.862, "r_y3": 368.44, "coord_origin": "TOPLEFT"}, "text": "However, spans are not allowed to cross the header - body", "orig": "However, spans are not allowed to cross the header - body", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.947, "r_x1": 348.334, "r_y1": 388.947, "r_x2": 348.334, "r_y2": 380.395, "r_x3": 308.862, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "boundary.", "orig": "boundary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 353.504, "r_y0": 388.947, "r_x1": 545.115, "r_y1": 388.947, "r_x2": 545.115, "r_y2": 380.395, "r_x3": 353.504, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "The table structure is described by the parame-", "orig": "The table structure is described by the parame-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.902, "r_x1": 326.018, "r_y1": 400.902, "r_x2": 326.018, "r_y2": 392.35, "r_x3": 308.862, "r_y3": 392.35, "coord_origin": "TOPLEFT"}, "text": "ters:", "orig": "ters:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.909, "r_y0": 400.902, "r_x1": 545.115, "r_y1": 400.902, "r_x2": 545.115, "r_y2": 392.35, "r_x3": 330.909, "r_y3": 392.35, "coord_origin": "TOPLEFT"}, "text": "Total number of table rows and columns, number of", "orig": "Total number of table rows and columns, number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.857, "r_x1": 545.115, "r_y1": 412.857, "r_x2": 545.115, "r_y2": 404.305, "r_x3": 308.862, "r_y3": 404.305, "coord_origin": "TOPLEFT"}, "text": "header rows, type of spans (header only spans, row only", "orig": "header rows, type of spans (header only spans, row only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.812, "r_x1": 333.49, "r_y1": 424.812, "r_x2": 333.49, "r_y2": 416.26, "r_x3": 308.862, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "spans,", "orig": "spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.082, "r_y0": 424.812, "r_x1": 418.66, "r_y1": 424.812, "r_x2": 418.66, "r_y2": 416.26, "r_x3": 338.082, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "column only spans,", "orig": "column only spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.253, "r_y0": 424.812, "r_x1": 545.115, "r_y1": 424.812, "r_x2": 545.115, "r_y2": 416.26, "r_x3": 423.253, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "both row and column spans),", "orig": "both row and column spans),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.768, "r_x1": 545.115, "r_y1": 436.768, "r_x2": 545.115, "r_y2": 428.216, "r_x3": 308.862, "r_y3": 428.216, "coord_origin": "TOPLEFT"}, "text": "maximum span size and the ratio of the table area covered", "orig": "maximum span size and the ratio of the table area covered", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.723, "r_x1": 345.943, "r_y1": 448.723, "r_x2": 345.943, "r_y2": 440.171, "r_x3": 308.862, "r_y3": 440.171, "coord_origin": "TOPLEFT"}, "text": "by spans.", "orig": "by spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.706, "r": 545.115, "b": 497.303, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.073, "r_y0": 461.294, "r_x1": 511.864, "r_y1": 461.294, "r_x2": 511.864, "r_y2": 452.706, "r_x3": 488.073, "r_y3": 452.706, "coord_origin": "TOPLEFT"}, "text": "theme", "orig": "theme", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 511.863, "r_y0": 461.437, "r_x1": 545.108, "r_y1": 461.437, "r_x2": 545.108, "r_y2": 452.885, "r_x3": 511.863, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": ", a set of", "orig": ", a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.392, "r_x1": 545.115, "r_y1": 473.392, "r_x2": 545.115, "r_y2": 464.84, "r_x3": 308.862, "r_y3": 464.84, "coord_origin": "TOPLEFT"}, "text": "suitable content templates is chosen first. Then, this content", "orig": "suitable content templates is chosen first. Then, this content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.348, "r_x1": 545.115, "r_y1": 485.348, "r_x2": 545.115, "r_y2": 476.796, "r_x3": 308.862, "r_y3": 476.796, "coord_origin": "TOPLEFT"}, "text": "can be combined with purely random text to produce the", "orig": "can be combined with purely random text to produce the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.303, "r_x1": 379.148, "r_y1": 497.303, "r_x2": 379.148, "r_y2": 488.751, "r_x3": 308.862, "r_y3": 488.751, "coord_origin": "TOPLEFT"}, "text": "synthetic content.", "orig": "synthetic content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.465, "r": 545.115, "b": 545.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.217, "r_y0": 510.017, "r_x1": 434.082, "r_y1": 510.017, "r_x2": 434.082, "r_y2": 501.465, "r_x3": 334.217, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Apply styling templates:", "orig": "Apply styling templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.063, "r_y0": 510.017, "r_x1": 545.115, "r_y1": 510.017, "r_x2": 545.115, "r_y2": 501.465, "r_x3": 439.063, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Depending on the domain", "orig": "Depending on the domain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.972, "r_x1": 406.884, "r_y1": 521.972, "r_x2": 406.884, "r_y2": 513.4200000000001, "r_x3": 308.862, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "of the synthetic dataset,", "orig": "of the synthetic dataset,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.019, "r_y0": 521.972, "r_x1": 545.115, "r_y1": 521.972, "r_x2": 545.115, "r_y2": 513.4200000000001, "r_x3": 411.019, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "a set of styling templates is first", "orig": "a set of styling templates is first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.928, "r_x1": 384.299, "r_y1": 533.928, "r_x2": 384.299, "r_y2": 525.376, "r_x3": 308.862, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "manually selected.", "orig": "manually selected.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.253, "r_y0": 533.928, "r_x1": 545.115, "r_y1": 533.928, "r_x2": 545.115, "r_y2": 525.376, "r_x3": 391.253, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "Then, a style is randomly selected to", "orig": "Then, a style is randomly selected to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.883, "r_x1": 496.159, "r_y1": 545.883, "r_x2": 496.159, "r_y2": 537.331, "r_x3": 308.862, "r_y3": 537.331, "coord_origin": "TOPLEFT"}, "text": "format the appearance of the synthesized table.", "orig": "format the appearance of the synthesized table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 550.045, "r": 545.115, "b": 606.418, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.402, "r_y0": 558.597, "r_x1": 450.371, "r_y1": 558.597, "r_x2": 450.371, "r_y2": 550.045, "r_x3": 335.402, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "Render the complete tables:", "orig": "Render the complete tables:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.139, "r_y0": 558.597, "r_x1": 545.115, "r_y1": 558.597, "r_x2": 545.115, "r_y2": 550.045, "r_x3": 456.139, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "The synthetic table is", "orig": "The synthetic table is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 570.552, "r_x1": 334.326, "r_y1": 570.552, "r_x2": 334.326, "r_y2": 562.0, "r_x3": 308.862, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "finally", "orig": "finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.983, "r_y0": 570.552, "r_x1": 545.115, "r_y1": 570.552, "r_x2": 545.115, "r_y2": 562.0, "r_x3": 337.983, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "rendered by a web browser engine to generate the", "orig": "rendered by a web browser engine to generate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 582.5070000000001, "r_x1": 545.115, "r_y1": 582.5070000000001, "r_x2": 545.115, "r_y2": 573.955, "r_x3": 308.862, "r_y3": 573.955, "coord_origin": "TOPLEFT"}, "text": "bounding boxes for each table cell. A batching technique is", "orig": "bounding boxes for each table cell. A batching technique is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 594.463, "r_x1": 545.115, "r_y1": 594.463, "r_x2": 545.115, "r_y2": 585.9110000000001, "r_x3": 308.862, "r_y3": 585.9110000000001, "coord_origin": "TOPLEFT"}, "text": "utilized to optimize the runtime overhead of the rendering", "orig": "utilized to optimize the runtime overhead of the rendering", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 606.418, "r_x1": 341.23, "r_y1": 606.418, "r_x2": 341.23, "r_y2": 597.866, "r_x3": 308.862, "r_y3": 597.866, "coord_origin": "TOPLEFT"}, "text": "process.", "orig": "process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2909999999999, "r": 545.109, "b": 646.987, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 323.591, "r_y0": 633.039, "r_x1": 376.493, "r_y1": 633.039, "r_x2": 376.493, "r_y2": 622.2909999999999, "r_x3": 323.591, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "Prediction", "orig": "Prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 382.255, "r_y0": 633.039, "r_x1": 461.076, "r_y1": 633.039, "r_x2": 461.076, "r_y2": 622.2909999999999, "r_x3": 382.255, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "post-processing", "orig": "post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.826, "r_y0": 633.039, "r_x1": 481.794, "r_y1": 633.039, "r_x2": 481.794, "r_y2": 622.2909999999999, "r_x3": 466.826, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.556, "r_y0": 633.039, "r_x1": 545.109, "r_y1": 633.039, "r_x2": 545.109, "r_y2": 622.2909999999999, "r_x3": 487.556, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "PDF docu-", "orig": "PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.795, "r_y0": 646.987, "r_x1": 357.341, "r_y1": 646.987, "r_x2": 357.341, "r_y2": 636.239, "r_x3": 326.795, "r_y3": 636.239, "coord_origin": "TOPLEFT"}, "text": "ments", "orig": "ments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 657.69, "r": 545.115, "b": 714.063, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 678.197, "r_x1": 545.115, "r_y1": 678.197, "r_x2": 545.115, "r_y2": 669.645, "r_x3": 308.862, "r_y3": 669.645, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes for tables recognized inside PDF docu-", "orig": "the bounding boxes for tables recognized inside PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 690.152, "r_x1": 545.115, "r_y1": 690.152, "r_x2": 545.115, "r_y2": 681.6, "r_x3": 308.862, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "ments, this is not enough when a full reconstruction of the", "orig": "ments, this is not enough when a full reconstruction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 702.107, "r_x1": 545.115, "r_y1": 702.107, "r_x2": 545.115, "r_y2": 693.5550000000001, "r_x3": 308.862, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "original table is required. This happens mainly due the fol-", "orig": "original table is required. This happens mainly due the fol-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 714.063, "r_x1": 371.427, "r_y1": 714.063, "r_x2": 371.427, "r_y2": 705.511, "r_x3": 308.862, "r_y3": 705.511, "coord_origin": "TOPLEFT"}, "text": "lowing reasons:", "orig": "lowing reasons:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 135.48400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.184, "r_y0": 135.48400000000004, "r_x1": 375.043, "r_y1": 135.48400000000004, "r_x2": 375.043, "r_y2": 122.42399999999998, "r_x3": 220.184, "r_y3": 122.42399999999998, "coord_origin": "TOPLEFT"}, "text": "Supplementary Material", "orig": "Supplementary Material", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 200.18899999999996, "r": 286.365, "b": 388.069, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 391.674, "r": 286.365, "b": 627.374, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 630.979, "r": 286.365, "b": 651.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 681.6, "r": 286.365, "b": 714.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 545.115, "b": 207.34199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.50400000000002, "r": 545.115, "b": 232.01099999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 236.17399999999998, "r": 545.115, "b": 316.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.382, "r_y0": 244.726, "r_x1": 492.911, "r_y1": 244.726, "r_x2": 492.911, "r_y2": 236.17399999999998, "r_x3": 335.382, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "Prepare styling and content templates:", "orig": "Prepare styling and content templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.669, "r_y0": 244.726, "r_x1": 545.115, "r_y1": 244.726, "r_x2": 545.115, "r_y2": 236.17399999999998, "r_x3": 498.669, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "The styling", "orig": "The styling", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.457, "r_x1": 425.693, "r_y1": 316.457, "r_x2": 425.693, "r_y2": 307.905, "r_x3": 308.862, "r_y3": 307.905, "coord_origin": "TOPLEFT"}, "text": "PubTabNet, FinTabNet, etc.).", "orig": "PubTabNet, FinTabNet, etc.).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.619, "r": 545.115, "b": 448.723, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.692, "r_y0": 329.171, "r_x1": 435.377, "r_y1": 329.171, "r_x2": 435.377, "r_y2": 320.619, "r_x3": 332.692, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "Generate table structures:", "orig": "Generate table structures:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.342, "r_y0": 329.171, "r_x1": 545.115, "r_y1": 329.171, "r_x2": 545.115, "r_y2": 320.619, "r_x3": 439.342, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "The structure of each syn-", "orig": "The structure of each syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 341.126, "r_x1": 545.115, "r_y1": 341.126, "r_x2": 545.115, "r_y2": 332.574, "r_x3": 308.862, "r_y3": 332.574, "coord_origin": "TOPLEFT"}, "text": "thetic dataset assumes a horizontal table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, "r_y1": 353.081, "r_x2": 545.115, "r_y2": 344.529, "r_x3": 431.462, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "rows and a table body that", "orig": "rows and a table body that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 365.037, "r_x1": 545.115, "r_y1": 365.037, "r_x2": 545.115, "r_y2": 356.485, "r_x3": 308.862, "r_y3": 356.485, "coord_origin": "TOPLEFT"}, "text": "may contain a combination of row spans and column spans.", "orig": "may contain a combination of row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.992, "r_x1": 545.115, "r_y1": 376.992, "r_x2": 545.115, "r_y2": 368.44, "r_x3": 308.862, "r_y3": 368.44, "coord_origin": "TOPLEFT"}, "text": "However, spans are not allowed to cross the header - body", "orig": "However, spans are not allowed to cross the header - body", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.947, "r_x1": 348.334, "r_y1": 388.947, "r_x2": 348.334, "r_y2": 380.395, "r_x3": 308.862, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "boundary.", "orig": "boundary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 353.504, "r_y0": 388.947, "r_x1": 545.115, "r_y1": 388.947, "r_x2": 545.115, "r_y2": 380.395, "r_x3": 353.504, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "The table structure is described by the parame-", "orig": "The table structure is described by the parame-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.902, "r_x1": 326.018, "r_y1": 400.902, "r_x2": 326.018, "r_y2": 392.35, "r_x3": 308.862, "r_y3": 392.35, "coord_origin": "TOPLEFT"}, "text": "ters:", "orig": "ters:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.909, "r_y0": 400.902, "r_x1": 545.115, "r_y1": 400.902, "r_x2": 545.115, "r_y2": 392.35, "r_x3": 330.909, "r_y3": 392.35, "coord_origin": "TOPLEFT"}, "text": "Total number of table rows and columns, number of", "orig": "Total number of table rows and columns, number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.857, "r_x1": 545.115, "r_y1": 412.857, "r_x2": 545.115, "r_y2": 404.305, "r_x3": 308.862, "r_y3": 404.305, "coord_origin": "TOPLEFT"}, "text": "header rows, type of spans (header only spans, row only", "orig": "header rows, type of spans (header only spans, row only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.812, "r_x1": 333.49, "r_y1": 424.812, "r_x2": 333.49, "r_y2": 416.26, "r_x3": 308.862, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "spans,", "orig": "spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.082, "r_y0": 424.812, "r_x1": 418.66, "r_y1": 424.812, "r_x2": 418.66, "r_y2": 416.26, "r_x3": 338.082, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "column only spans,", "orig": "column only spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.253, "r_y0": 424.812, "r_x1": 545.115, "r_y1": 424.812, "r_x2": 545.115, "r_y2": 416.26, "r_x3": 423.253, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "both row and column spans),", "orig": "both row and column spans),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.768, "r_x1": 545.115, "r_y1": 436.768, "r_x2": 545.115, "r_y2": 428.216, "r_x3": 308.862, "r_y3": 428.216, "coord_origin": "TOPLEFT"}, "text": "maximum span size and the ratio of the table area covered", "orig": "maximum span size and the ratio of the table area covered", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.723, "r_x1": 345.943, "r_y1": 448.723, "r_x2": 345.943, "r_y2": 440.171, "r_x3": 308.862, "r_y3": 440.171, "coord_origin": "TOPLEFT"}, "text": "by spans.", "orig": "by spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.706, "r": 545.115, "b": 497.303, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.073, "r_y0": 461.294, "r_x1": 511.864, "r_y1": 461.294, "r_x2": 511.864, "r_y2": 452.706, "r_x3": 488.073, "r_y3": 452.706, "coord_origin": "TOPLEFT"}, "text": "theme", "orig": "theme", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 511.863, "r_y0": 461.437, "r_x1": 545.108, "r_y1": 461.437, "r_x2": 545.108, "r_y2": 452.885, "r_x3": 511.863, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": ", a set of", "orig": ", a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.392, "r_x1": 545.115, "r_y1": 473.392, "r_x2": 545.115, "r_y2": 464.84, "r_x3": 308.862, "r_y3": 464.84, "coord_origin": "TOPLEFT"}, "text": "suitable content templates is chosen first. Then, this content", "orig": "suitable content templates is chosen first. Then, this content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.348, "r_x1": 545.115, "r_y1": 485.348, "r_x2": 545.115, "r_y2": 476.796, "r_x3": 308.862, "r_y3": 476.796, "coord_origin": "TOPLEFT"}, "text": "can be combined with purely random text to produce the", "orig": "can be combined with purely random text to produce the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.303, "r_x1": 379.148, "r_y1": 497.303, "r_x2": 379.148, "r_y2": 488.751, "r_x3": 308.862, "r_y3": 488.751, "coord_origin": "TOPLEFT"}, "text": "synthetic content.", "orig": "synthetic content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.465, "r": 545.115, "b": 545.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.217, "r_y0": 510.017, "r_x1": 434.082, "r_y1": 510.017, "r_x2": 434.082, "r_y2": 501.465, "r_x3": 334.217, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Apply styling templates:", "orig": "Apply styling templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.063, "r_y0": 510.017, "r_x1": 545.115, "r_y1": 510.017, "r_x2": 545.115, "r_y2": 501.465, "r_x3": 439.063, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Depending on the domain", "orig": "Depending on the domain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.972, "r_x1": 406.884, "r_y1": 521.972, "r_x2": 406.884, "r_y2": 513.4200000000001, "r_x3": 308.862, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "of the synthetic dataset,", "orig": "of the synthetic dataset,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.019, "r_y0": 521.972, "r_x1": 545.115, "r_y1": 521.972, "r_x2": 545.115, "r_y2": 513.4200000000001, "r_x3": 411.019, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "a set of styling templates is first", "orig": "a set of styling templates is first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.928, "r_x1": 384.299, "r_y1": 533.928, "r_x2": 384.299, "r_y2": 525.376, "r_x3": 308.862, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "manually selected.", "orig": "manually selected.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.253, "r_y0": 533.928, "r_x1": 545.115, "r_y1": 533.928, "r_x2": 545.115, "r_y2": 525.376, "r_x3": 391.253, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "Then, a style is randomly selected to", "orig": "Then, a style is randomly selected to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.883, "r_x1": 496.159, "r_y1": 545.883, "r_x2": 496.159, "r_y2": 537.331, "r_x3": 308.862, "r_y3": 537.331, "coord_origin": "TOPLEFT"}, "text": "format the appearance of the synthesized table.", "orig": "format the appearance of the synthesized table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 550.045, "r": 545.115, "b": 606.418, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.402, "r_y0": 558.597, "r_x1": 450.371, "r_y1": 558.597, "r_x2": 450.371, "r_y2": 550.045, "r_x3": 335.402, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "Render the complete tables:", "orig": "Render the complete tables:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.139, "r_y0": 558.597, "r_x1": 545.115, "r_y1": 558.597, "r_x2": 545.115, "r_y2": 550.045, "r_x3": 456.139, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "The synthetic table is", "orig": "The synthetic table is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 570.552, "r_x1": 334.326, "r_y1": 570.552, "r_x2": 334.326, "r_y2": 562.0, "r_x3": 308.862, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "finally", "orig": "finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.983, "r_y0": 570.552, "r_x1": 545.115, "r_y1": 570.552, "r_x2": 545.115, "r_y2": 562.0, "r_x3": 337.983, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "rendered by a web browser engine to generate the", "orig": "rendered by a web browser engine to generate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 582.5070000000001, "r_x1": 545.115, "r_y1": 582.5070000000001, "r_x2": 545.115, "r_y2": 573.955, "r_x3": 308.862, "r_y3": 573.955, "coord_origin": "TOPLEFT"}, "text": "bounding boxes for each table cell. A batching technique is", "orig": "bounding boxes for each table cell. A batching technique is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 594.463, "r_x1": 545.115, "r_y1": 594.463, "r_x2": 545.115, "r_y2": 585.9110000000001, "r_x3": 308.862, "r_y3": 585.9110000000001, "coord_origin": "TOPLEFT"}, "text": "utilized to optimize the runtime overhead of the rendering", "orig": "utilized to optimize the runtime overhead of the rendering", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 606.418, "r_x1": 341.23, "r_y1": 606.418, "r_x2": 341.23, "r_y2": 597.866, "r_x3": 308.862, "r_y3": 597.866, "coord_origin": "TOPLEFT"}, "text": "process.", "orig": "process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. 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"r_y1": 323.889, "r_x2": 286.365, "r_y2": 315.337, "r_x3": 128.348, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "PDF documents can be seen as a se-", "orig": "PDF documents can be seen as a se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.845, "r_x1": 286.365, "r_y1": 335.845, "r_x2": 286.365, "r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 516.431, "r_x1": 69.539, "r_y1": 516.431, "r_x2": 69.539, "r_y2": 507.879, "r_x3": 62.067, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.248, "r_y0": 516.431, "r_x1": 286.365, "r_y1": 516.431, "r_x2": 286.365, "r_y2": 507.879, "r_x3": 75.248, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "Use a carefully selected IOU threshold to designate", "orig": "Use a carefully selected IOU threshold to designate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 528.387, "r_x1": 226.071, "r_y1": 528.387, "r_x2": 226.071, "r_y2": 519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. The alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 600.7470000000001, "r_x1": 278.704, "r_y1": 600.7470000000001, "r_x2": 278.704, "r_y2": 592.1949999999999, "r_x3": 50.112, "r_y3": 592.1949999999999, "coord_origin": "TOPLEFT"}, "text": "of the column can be identified by the following formula:", "orig": "of the column can be identified by the following formula:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.028, "r_y0": 632.81, "r_x1": 157.952, "r_y1": 632.81, "r_x2": 157.952, "r_y2": 624.104, "r_x3": 112.028, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "alignment", "orig": "alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.715, 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metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.8530000000001, "r_x1": 442.221, "r_y1": 567.8530000000001, "r_x2": 442.221, "r_y2": 559.3009999999999, "r_x3": 308.862, "r_y3": 559.3009999999999, "coord_origin": "TOPLEFT"}, "text": "created to match with the orphan.", "orig": "created to match with the orphan.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 580.159, "r_x1": 332.712, "r_y1": 580.159, "r_x2": 332.712, "r_y2": 571.607, "r_x3": 320.817, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "9a.", "orig": "9a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.943, "r_y0": 580.159, "r_x1": 545.115, "r_y1": 580.159, "r_x2": 545.115, "r_y2": 571.607, "r_x3": 337.943, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "Compute the top and bottom boundary of the hori-", "orig": "Compute the top and bottom boundary of the hori-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 592.114, "r_x1": 471.641, "r_y1": 592.114, "r_x2": 471.641, "r_y2": 583.562, "r_x3": 308.862, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "zontal band for each grid row (min/max", "orig": "zontal band for each grid row (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.834, "r_y0": 591.95, "r_x1": 479.719, "r_y1": 591.95, "r_x2": 479.719, "r_y2": 583.244, "r_x3": 474.834, "r_y3": 583.244, "coord_origin": "TOPLEFT"}, "text": "y", "orig": "y", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.269, "r_y0": 592.114, "r_x1": 545.117, "r_y1": 592.114, "r_x2": 545.117, "r_y2": 583.562, "r_x3": 483.269, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "coordinates per", "orig": "coordinates per", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 604.069, "r_x1": 329.913, "r_y1": 604.069, "r_x2": 329.913, "r_y2": 595.517, "r_x3": 308.862, "r_y3": 595.517, "coord_origin": "TOPLEFT"}, "text": "row).", "orig": "row).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 616.374, "r_x1": 332.872, "r_y1": 616.374, "r_x2": 332.872, "r_y2": 607.822, "r_x3": 320.817, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "9b.", "orig": "9b.", 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"text": "However, it is possible to mitigate those limitations by", "orig": "However, it is possible to mitigate those limitations by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 299.979, "r_x1": 286.365, "r_y1": 299.979, "r_x2": 286.365, "r_y2": 291.427, "r_x3": 50.112, "r_y3": 291.427, "coord_origin": "TOPLEFT"}, "text": "combining the TableFormer predictions with the informa-", "orig": "combining the TableFormer predictions with the informa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.934, "r_x1": 286.365, "r_y1": 311.934, "r_x2": 286.365, "r_y2": 303.382, "r_x3": 50.112, "r_y3": 303.382, "coord_origin": "TOPLEFT"}, "text": "tion already present inside a programmatic PDF document.", "orig": "tion already present inside a programmatic PDF 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"r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.338, "r": 286.365, "b": 419.846, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.564, "r": 286.365, "b": 456.026, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.744, "r": 286.365, "b": 504.161, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.879, "r": 286.365, "b": 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519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 532.104, "r": 286.365, "b": 564.567, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.285, "r": 286.365, "b": 600.7470000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. The alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 600.7470000000001, "r_x1": 278.704, "r_y1": 600.7470000000001, "r_x2": 278.704, "r_y2": 592.1949999999999, "r_x3": 50.112, "r_y3": 592.1949999999999, "coord_origin": "TOPLEFT"}, "text": "of the column can be identified by the following formula:", "orig": "of the column can be identified by the following formula:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "formula", "bbox": {"l": 110.705, "t": 624.104, "r": 286.362, "b": 654.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.028, "r_y0": 632.81, "r_x1": 157.952, "r_y1": 632.81, "r_x2": 157.952, "r_y2": 624.104, "r_x3": 112.028, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "alignment", "orig": "alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.715, "r_y0": 632.81, "r_x1": 203.496, "r_y1": 632.81, "r_x2": 203.496, "r_y2": 624.104, "r_x3": 160.715, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "= arg min", "orig": "= arg min", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.585, "r_y0": 640.154, "r_x1": 189.145, "r_y1": 640.154, "r_x2": 189.145, "r_y2": 634.06, "r_x3": 185.585, "r_y3": 634.06, "coord_origin": "TOPLEFT"}, "text": "c", "orig": "c", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.499, "r_y0": 632.668, "r_x1": 208.48, "r_y1": 632.668, "r_x2": 208.48, "r_y2": 624.104, "r_x3": 203.499, "r_y3": 624.104, 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271.326, "coord_origin": "TOPLEFT"}, "text": "-coordinates and cell sizes.", "orig": "-coordinates and cell sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 283.632, "r": 545.115, "b": 387.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 292.184, "r_x1": 328.289, "r_y1": 292.184, "r_x2": 328.289, "r_y2": 283.632, "r_x3": 320.817, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "7.", "orig": "7.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.144, "r_y0": 292.184, "r_x1": 545.115, "r_y1": 292.184, "r_x2": 545.115, "r_y2": 283.632, "r_x3": 332.144, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "Generate a new set of pair-wise matches between the", "orig": "Generate a new set of pair-wise matches between the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.139, "r_x1": 476.562, "r_y1": 304.139, "r_x2": 476.562, "r_y2": 295.587, "r_x3": 308.862, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "corrected bounding boxes and PDF cells.", "orig": "corrected bounding boxes and PDF cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 482.082, "r_y0": 304.139, "r_x1": 545.115, "r_y1": 304.139, "r_x2": 545.115, "r_y2": 295.587, "r_x3": 482.082, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "This time use a", "orig": "This time use a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.094, "r_x1": 545.115, "r_y1": 316.094, "r_x2": 545.115, "r_y2": 307.542, "r_x3": 308.862, "r_y3": 307.542, "coord_origin": "TOPLEFT"}, "text": "modified version of the IOU metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.862, "t": 391.579, "r": 545.115, "b": 459.906, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.862, "t": 463.66, "r": 545.115, "b": 567.8530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.8530000000001, "r_x1": 442.221, "r_y1": 567.8530000000001, "r_x2": 442.221, "r_y2": 559.3009999999999, "r_x3": 308.862, "r_y3": 559.3009999999999, "coord_origin": "TOPLEFT"}, "text": "created to match with the orphan.", "orig": "created to match with the orphan.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.862, "t": 571.607, "r": 545.117, "b": 604.069, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 580.159, 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Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 211.20000000000005, "r": 286.365, "b": 231.707, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 219.75199999999995, "r_x1": 65.056, "r_y1": 219.75199999999995, "r_x2": 65.056, "r_y2": 211.20000000000005, "r_x3": 61.569, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 219.75199999999995, "r_x1": 286.365, "r_y1": 219.75199999999995, "r_x2": 286.365, "r_y2": 211.20000000000005, "r_x3": 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0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 252.89200000000005, "r_x1": 65.056, "r_y1": 252.89200000000005, "r_x2": 65.056, "r_y2": 244.34000000000003, "r_x3": 61.569, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 252.89200000000005, "r_x1": 286.365, "r_y1": 252.89200000000005, "r_x2": 286.365, "r_y2": 244.34000000000003, "r_x3": 70.037, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "There are occasional inaccuracies in the predictions of", "orig": "There are occasional inaccuracies in the predictions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 264.847, "r_x1": 150.415, "r_y1": 264.847, "r_x2": 150.415, "r_y2": 256.29499999999996, "r_x3": 70.037, "r_y3": 256.29499999999996, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes.", "orig": "the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.472, "r": 286.365, "b": 395.621, "coord_origin": "TOPLEFT"}, "confidence": 0.9767085909843445, "cells": [{"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 288.024, "r_x1": 286.365, "r_y1": 288.024, "r_x2": 286.365, "r_y2": 279.472, "r_x3": 62.067, "r_y3": 279.472, "coord_origin": "TOPLEFT"}, "text": "However, it is possible to mitigate those limitations by", "orig": "However, it is possible to mitigate those limitations by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 299.979, "r_x1": 286.365, "r_y1": 299.979, "r_x2": 286.365, "r_y2": 291.427, "r_x3": 50.112, "r_y3": 291.427, "coord_origin": "TOPLEFT"}, "text": "combining the TableFormer predictions with the informa-", "orig": "combining the TableFormer predictions with the informa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.934, "r_x1": 286.365, "r_y1": 311.934, "r_x2": 286.365, "r_y2": 303.382, "r_x3": 50.112, "r_y3": 303.382, "coord_origin": "TOPLEFT"}, "text": "tion already present inside a programmatic PDF document.", "orig": "tion already present inside a programmatic PDF document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.889, "r_x1": 123.387, "r_y1": 323.889, "r_x2": 123.387, "r_y2": 315.337, "r_x3": 50.112, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "More specifically,", "orig": "More specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.348, "r_y0": 323.889, "r_x1": 286.365, "r_y1": 323.889, "r_x2": 286.365, "r_y2": 315.337, "r_x3": 128.348, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "PDF documents can be seen as a se-", "orig": "PDF documents can be seen as a se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.845, "r_x1": 286.365, "r_y1": 335.845, "r_x2": 286.365, "r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.338, "r": 286.365, "b": 419.846, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.564, "r": 286.365, "b": 456.026, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.744, "r": 286.365, "b": 504.161, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.879, "r": 286.365, "b": 528.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 516.431, "r_x1": 69.539, "r_y1": 516.431, "r_x2": 69.539, "r_y2": 507.879, "r_x3": 62.067, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.248, "r_y0": 516.431, "r_x1": 286.365, "r_y1": 516.431, "r_x2": 286.365, "r_y2": 507.879, "r_x3": 75.248, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "Use a carefully selected IOU threshold to designate", "orig": "Use a carefully selected IOU threshold to designate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 528.387, "r_x1": 226.071, "r_y1": 528.387, "r_x2": 226.071, "r_y2": 519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 532.104, "r": 286.365, "b": 564.567, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.285, "r": 286.365, "b": 600.7470000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. The alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 600.7470000000001, "r_x1": 278.704, "r_y1": 600.7470000000001, "r_x2": 278.704, "r_y2": 592.1949999999999, "r_x3": 50.112, "r_y3": 592.1949999999999, "coord_origin": "TOPLEFT"}, "text": "of the column can be identified by the following formula:", "orig": "of the column can be identified by the following formula:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.705, "t": 624.104, "r": 286.362, "b": 654.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.028, "r_y0": 632.81, "r_x1": 157.952, "r_y1": 632.81, "r_x2": 157.952, "r_y2": 624.104, "r_x3": 112.028, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "alignment", "orig": "alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.715, "r_y0": 632.81, "r_x1": 203.496, "r_y1": 632.81, "r_x2": 203.496, "r_y2": 624.104, "r_x3": 160.715, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "= arg min", "orig": "= arg min", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.729, "r_y0": 633.734, "r_x1": 220.289, "r_y1": 633.734, "r_x2": 220.289, "r_y2": 627.64, "r_x3": 216.729, "r_y3": 627.64, "coord_origin": "TOPLEFT"}, "text": "c", "orig": "c", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.787, "r_y0": 632.668, "r_x1": 225.768, "r_y1": 632.668, "r_x2": 225.768, "r_y2": 624.104, "r_x3": 220.787, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "}", "orig": "}", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 110.705, "r_y0": 654.075, "r_x1": 118.953, "r_y1": 654.075, "r_x2": 118.953, "r_y2": 645.369, "r_x3": 110.705, "r_y3": 645.369, "coord_origin": "TOPLEFT"}, "text": "D", "orig": "D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, 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"r_x3": 243.529, "r_y3": 671.551, "coord_origin": "TOPLEFT"}, "text": "c", "orig": "c", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.473, "r_y0": 676.886, "r_x1": 258.118, "r_y1": 676.886, "r_x2": 258.118, "r_y2": 668.3340000000001, "r_x3": 251.473, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.003, "r_y0": 676.886, "r_x1": 286.362, "r_y1": 676.886, "r_x2": 286.362, "r_y2": 668.3340000000001, "r_x3": 262.003, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "the x-", "orig": "the x-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 688.841, "r_x1": 205.887, "r_y1": 688.841, "r_x2": 205.887, "r_y2": 680.289, "r_x3": 50.112, "r_y3": 680.289, "coord_origin": "TOPLEFT"}, "text": "coordinate for the corresponding point.", "orig": "coordinate for the corresponding point.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510206222534, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 701.111, "r_x1": 69.539, "r_y1": 701.111, "r_x2": 69.539, "r_y2": 692.559, "r_x3": 62.067, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.274, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 76.274, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Use the alignment computed in step 4, to compute", "orig": "Use the alignment computed in step 4, to compute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 94.605, "r_y1": 713.066, "r_x2": 94.605, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "the median", "orig": "the median", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.599, "r_y0": 712.902, "r_x1": 103.293, "r_y1": 712.902, "r_x2": 103.293, "r_y2": 704.196, "r_x3": 97.599, "r_y3": 704.196, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 103.292, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 103.292, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "-coordinate for all table columns and the me-", "orig": "-coordinate for all table columns and the me-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 211.20000000000005, "r": 545.115, "b": 255.61800000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8942298889160156, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 219.75199999999995, "r_x1": 435.915, "r_y1": 219.75199999999995, "r_x2": 435.915, "r_y2": 211.20000000000005, "r_x3": 308.862, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "dian cell size for all table cells.", "orig": "dian cell size for all table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.697, "r_y0": 219.75199999999995, "r_x1": 545.115, "r_y1": 219.75199999999995, "r_x2": 545.115, "r_y2": 211.20000000000005, "r_x3": 440.697, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "The usage of median dur-", "orig": "The usage of median dur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 231.70799999999997, "r_x1": 545.115, "r_y1": 231.70799999999997, "r_x2": 545.115, "r_y2": 223.15599999999995, "r_x3": 308.862, "r_y3": 223.15599999999995, "coord_origin": "TOPLEFT"}, "text": "ing the computations, helps to eliminate outliers caused by", "orig": "ing the computations, helps to eliminate outliers caused by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 243.663, "r_x1": 545.115, "r_y1": 243.663, "r_x2": 545.115, "r_y2": 235.111, "r_x3": 308.862, "r_y3": 235.111, "coord_origin": "TOPLEFT"}, "text": "occasional column spans which are usually wider than the", "orig": "occasional column spans which are usually wider than the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 255.61800000000005, "r_x1": 339.577, "r_y1": 255.61800000000005, "r_x2": 339.577, "r_y2": 247.06600000000003, "r_x3": 308.862, "r_y3": 247.06600000000003, "coord_origin": "TOPLEFT"}, "text": "normal.", "orig": "normal.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.371, "r": 545.115, "b": 279.87800000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9423392415046692, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 267.923, "r_x1": 328.289, "r_y1": 267.923, "r_x2": 328.289, "r_y2": 259.371, "r_x3": 320.817, "r_y3": 259.371, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.884, "r_y0": 267.923, "r_x1": 545.115, "r_y1": 267.923, "r_x2": 545.115, "r_y2": 259.371, "r_x3": 334.884, "r_y3": 259.371, "coord_origin": "TOPLEFT"}, "text": "Snap all cells with bad IOU to their corresponding", "orig": "Snap all cells with bad IOU to their corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 279.87800000000004, "r_x1": 338.192, "r_y1": 279.87800000000004, "r_x2": 338.192, "r_y2": 271.326, "r_x3": 308.862, "r_y3": 271.326, "coord_origin": "TOPLEFT"}, "text": "median", "orig": "median", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.682, "r_y0": 279.71400000000006, "r_x1": 346.376, "r_y1": 279.71400000000006, "r_x2": 346.376, "r_y2": 271.00800000000004, "r_x3": 340.682, "r_y3": 271.00800000000004, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.376, "r_y0": 279.87800000000004, "r_x1": 453.723, "r_y1": 279.87800000000004, "r_x2": 453.723, "r_y2": 271.326, "r_x3": 346.376, "r_y3": 271.326, "coord_origin": "TOPLEFT"}, "text": "-coordinates and cell sizes.", "orig": "-coordinates and cell sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 283.632, "r": 545.115, "b": 387.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 292.184, "r_x1": 328.289, "r_y1": 292.184, "r_x2": 328.289, "r_y2": 283.632, "r_x3": 320.817, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "7.", "orig": "7.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.144, "r_y0": 292.184, "r_x1": 545.115, "r_y1": 292.184, "r_x2": 545.115, "r_y2": 283.632, "r_x3": 332.144, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "Generate a new set of pair-wise matches between the", "orig": "Generate a new set of pair-wise matches between the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.139, "r_x1": 476.562, "r_y1": 304.139, "r_x2": 476.562, "r_y2": 295.587, "r_x3": 308.862, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "corrected bounding boxes and PDF cells.", "orig": "corrected bounding boxes and PDF cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 482.082, "r_y0": 304.139, "r_x1": 545.115, "r_y1": 304.139, "r_x2": 545.115, "r_y2": 295.587, "r_x3": 482.082, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "This time use a", "orig": "This time use a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.094, "r_x1": 545.115, "r_y1": 316.094, "r_x2": 545.115, "r_y2": 307.542, "r_x3": 308.862, "r_y3": 307.542, "coord_origin": "TOPLEFT"}, "text": "modified version of the IOU metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.862, "t": 391.579, "r": 545.115, "b": 459.906, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.862, "t": 463.66, "r": 545.115, "b": 567.8530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.8530000000001, "r_x1": 442.221, "r_y1": 567.8530000000001, "r_x2": 442.221, "r_y2": 559.3009999999999, "r_x3": 308.862, "r_y3": 559.3009999999999, "coord_origin": "TOPLEFT"}, "text": "created to match with the orphan.", "orig": "created to match with the orphan.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.862, "t": 571.607, "r": 545.117, "b": 604.069, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 580.159, "r_x1": 332.712, "r_y1": 580.159, "r_x2": 332.712, "r_y2": 571.607, "r_x3": 320.817, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "9a.", "orig": "9a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.943, "r_y0": 580.159, "r_x1": 545.115, "r_y1": 580.159, "r_x2": 545.115, "r_y2": 571.607, "r_x3": 337.943, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "Compute the top and bottom boundary of the hori-", "orig": "Compute the top and bottom boundary of the hori-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 592.114, "r_x1": 471.641, "r_y1": 592.114, "r_x2": 471.641, "r_y2": 583.562, "r_x3": 308.862, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "zontal band for each grid row (min/max", "orig": "zontal band for each grid row (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.834, "r_y0": 591.95, "r_x1": 479.719, "r_y1": 591.95, "r_x2": 479.719, "r_y2": 583.244, "r_x3": 474.834, "r_y3": 583.244, "coord_origin": "TOPLEFT"}, "text": "y", "orig": "y", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.269, "r_y0": 592.114, "r_x1": 545.117, "r_y1": 592.114, "r_x2": 545.117, "r_y2": 583.562, "r_x3": 483.269, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "coordinates per", "orig": "coordinates per", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 604.069, "r_x1": 329.913, "r_y1": 604.069, "r_x2": 329.913, "r_y2": 595.517, "r_x3": 308.862, "r_y3": 595.517, "coord_origin": "TOPLEFT"}, "text": "row).", "orig": "row).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.862, "t": 607.822, "r": 545.115, "b": 628.33, "coord_origin": "TOPLEFT"}, "confidence": 0.7716891169548035, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 616.374, "r_x1": 332.872, "r_y1": 616.374, "r_x2": 332.872, "r_y2": 607.822, "r_x3": 320.817, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "9b.", "orig": "9b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.925, "r_y0": 616.374, "r_x1": 374.226, "r_y1": 616.374, "r_x2": 374.226, "r_y2": 607.822, "r_x3": 339.925, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "Intersect", "orig": "Intersect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.042, "r_y0": 616.374, "r_x1": 545.115, "r_y1": 616.374, "r_x2": 545.115, "r_y2": 607.822, "r_x3": 378.042, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "the orphan's bounding box with the row", "orig": "the orphan's bounding box with the row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 628.33, "r_x1": 495.292, "r_y1": 628.33, "r_x2": 495.292, "r_y2": 619.778, "r_x3": 308.862, "r_y3": 619.778, "coord_origin": "TOPLEFT"}, "text": "bands, and map the cell to the closest grid row.", "orig": "bands, and map the cell to the closest grid row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.862, "t": 632.083, "r": 545.115, "b": 664.545, "coord_origin": "TOPLEFT"}, "confidence": 0.8584907054901123, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 640.635, "r_x1": 332.712, "r_y1": 640.635, "r_x2": 332.712, "r_y2": 632.083, "r_x3": 320.817, "r_y3": 632.083, "coord_origin": "TOPLEFT"}, "text": "9c.", "orig": "9c.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.534, "r_y0": 640.635, "r_x1": 545.115, "r_y1": 640.635, "r_x2": 545.115, "r_y2": 632.083, "r_x3": 337.534, "r_y3": 632.083, "coord_origin": "TOPLEFT"}, "text": "Compute the left and right boundary of the vertical", "orig": "Compute the left and right boundary of the vertical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.59, "r_x1": 455.282, "r_y1": 652.59, "r_x2": 455.282, "r_y2": 644.038, "r_x3": 308.862, "r_y3": 644.038, "coord_origin": "TOPLEFT"}, "text": "band for each grid column (min/max", "orig": "band for each grid column (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 457.777, "r_y0": 652.4259999999999, "r_x1": 463.471, "r_y1": 652.4259999999999, "r_x2": 463.471, "r_y2": 643.72, "r_x3": 457.777, "r_y3": 643.72, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.971, "r_y0": 652.59, "r_x1": 545.114, "r_y1": 652.59, "r_x2": 545.114, "r_y2": 644.038, "r_x3": 465.971, "r_y3": 644.038, "coord_origin": "TOPLEFT"}, "text": "coordinates per col-", "orig": "coordinates per col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.545, "r_x1": 332.384, "r_y1": 664.545, "r_x2": 332.384, "r_y2": 655.9929999999999, "r_x3": 308.862, "r_y3": 655.9929999999999, "coord_origin": "TOPLEFT"}, "text": "umn).", "orig": "umn).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.862, "t": 668.299, "r": 545.115, "b": 688.806, "coord_origin": "TOPLEFT"}, "confidence": 0.7590745091438293, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 676.851, "r_x1": 545.115, "r_y1": 676.851, "r_x2": 545.115, "r_y2": 668.299, "r_x3": 320.817, "r_y3": 668.299, "coord_origin": "TOPLEFT"}, "text": "9d. Intersect the orphan's bounding box with the column", "orig": "9d. Intersect the orphan's bounding box with the column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 688.806, "r_x1": 510.585, "r_y1": 688.806, "r_x2": 510.585, "r_y2": 680.254, "r_x3": 308.862, "r_y3": 680.254, "coord_origin": "TOPLEFT"}, "text": "bands, and map the cell to the closest grid column.", "orig": "bands, and map the cell to the closest grid column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.862, "t": 692.559, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 320.817, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "9e. If the table cell under the identified row and column", "orig": "9e. If the table cell under the identified row and column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "is not empty, extend its content with the content of the or-", "orig": "is not empty, extend its content with the content of the or-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.9126598238945007, "cells": [{"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "12", "orig": "12", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74854278564453, "r": 544.938232421875, "b": 147.59091186523438, "coord_origin": "TOPLEFT"}, "confidence": 0.6033818125724792, "cells": 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"confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.77099999999996, "r": 545.114, "b": 186.27800000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 174.32299999999998, "r_x1": 86.585, "r_y1": 174.32299999999998, "r_x2": 86.585, "r_y2": 165.77099999999996, "r_x3": 50.112, "r_y3": 165.77099999999996, "coord_origin": "TOPLEFT"}, "text": "Figure 7:", "orig": "Figure 7:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.832, "r_y0": 174.32299999999998, "r_x1": 545.114, "r_y1": 174.32299999999998, "r_x2": 545.114, "r_y2": 165.77099999999996, "r_x3": 92.832, "r_y3": 165.77099999999996, "coord_origin": "TOPLEFT"}, "text": "Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "orig": "Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 186.27800000000002, "r_x1": 513.522, "r_y1": 186.27800000000002, "r_x2": 513.522, "r_y2": 177.726, "r_x3": 50.112, "r_y3": 177.726, "coord_origin": "TOPLEFT"}, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "orig": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 211.20000000000005, "r": 286.365, "b": 231.707, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 219.75199999999995, "r_x1": 65.056, "r_y1": 219.75199999999995, "r_x2": 65.056, "r_y2": 211.20000000000005, "r_x3": 61.569, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 219.75199999999995, "r_x1": 286.365, "r_y1": 219.75199999999995, "r_x2": 286.365, "r_y2": 211.20000000000005, "r_x3": 70.037, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer output does not include the table cell con-", "orig": "TableFormer output does not include the table cell con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 231.707, "r_x1": 87.472, "r_y1": 231.707, "r_x2": 87.472, "r_y2": 223.15499999999997, "r_x3": 70.037, "r_y3": 223.15499999999997, "coord_origin": "TOPLEFT"}, "text": "tent.", "orig": "tent.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.34000000000003, "r": 286.365, "b": 264.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9629555344581604, "cells": [{"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 252.89200000000005, "r_x1": 65.056, "r_y1": 252.89200000000005, "r_x2": 65.056, "r_y2": 244.34000000000003, "r_x3": 61.569, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 252.89200000000005, "r_x1": 286.365, "r_y1": 252.89200000000005, "r_x2": 286.365, "r_y2": 244.34000000000003, "r_x3": 70.037, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "There are occasional inaccuracies in the predictions of", "orig": "There are occasional inaccuracies in the predictions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 264.847, "r_x1": 150.415, "r_y1": 264.847, "r_x2": 150.415, "r_y2": 256.29499999999996, "r_x3": 70.037, "r_y3": 256.29499999999996, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes.", "orig": "the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.472, "r": 286.365, "b": 395.621, "coord_origin": "TOPLEFT"}, "confidence": 0.9767085909843445, "cells": [{"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 288.024, "r_x1": 286.365, "r_y1": 288.024, "r_x2": 286.365, "r_y2": 279.472, "r_x3": 62.067, "r_y3": 279.472, "coord_origin": "TOPLEFT"}, "text": "However, it is possible to mitigate those limitations by", "orig": "However, it is possible to mitigate those limitations by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 299.979, "r_x1": 286.365, "r_y1": 299.979, "r_x2": 286.365, "r_y2": 291.427, "r_x3": 50.112, "r_y3": 291.427, "coord_origin": "TOPLEFT"}, "text": "combining the TableFormer predictions with the informa-", "orig": "combining the TableFormer predictions with the informa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.934, "r_x1": 286.365, "r_y1": 311.934, "r_x2": 286.365, "r_y2": 303.382, "r_x3": 50.112, "r_y3": 303.382, "coord_origin": "TOPLEFT"}, "text": "tion already present inside a programmatic PDF document.", "orig": "tion already present inside a programmatic PDF document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.889, "r_x1": 123.387, "r_y1": 323.889, "r_x2": 123.387, "r_y2": 315.337, "r_x3": 50.112, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "More specifically,", "orig": "More specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.348, "r_y0": 323.889, "r_x1": 286.365, "r_y1": 323.889, "r_x2": 286.365, "r_y2": 315.337, "r_x3": 128.348, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "PDF documents can be seen as a se-", "orig": "PDF documents can be seen as a se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.845, "r_x1": 286.365, "r_y1": 335.845, "r_x2": 286.365, "r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.338, "r": 286.365, "b": 419.846, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.564, "r": 286.365, "b": 456.026, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.744, "r": 286.365, "b": 504.161, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.879, "r": 286.365, "b": 528.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 516.431, "r_x1": 69.539, "r_y1": 516.431, "r_x2": 69.539, "r_y2": 507.879, "r_x3": 62.067, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.248, "r_y0": 516.431, "r_x1": 286.365, "r_y1": 516.431, "r_x2": 286.365, "r_y2": 507.879, "r_x3": 75.248, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "Use a carefully selected IOU threshold to designate", "orig": "Use a carefully selected IOU threshold to designate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 528.387, "r_x1": 226.071, "r_y1": 528.387, "r_x2": 226.071, "r_y2": 519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 532.104, "r": 286.365, "b": 564.567, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.285, "r": 286.365, "b": 600.7470000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. The alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 600.7470000000001, "r_x1": 278.704, "r_y1": 600.7470000000001, "r_x2": 278.704, "r_y2": 592.1949999999999, "r_x3": 50.112, "r_y3": 592.1949999999999, "coord_origin": "TOPLEFT"}, "text": "of the column can be identified by the following formula:", "orig": "of the column can be identified by the following formula:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.705, "t": 624.104, "r": 286.362, "b": 654.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.028, "r_y0": 632.81, "r_x1": 157.952, "r_y1": 632.81, "r_x2": 157.952, "r_y2": 624.104, "r_x3": 112.028, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "alignment", "orig": "alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.715, "r_y0": 632.81, "r_x1": 203.496, "r_y1": 632.81, "r_x2": 203.496, "r_y2": 624.104, "r_x3": 160.715, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "= arg min", "orig": "= arg min", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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668.016, "coord_origin": "TOPLEFT"}, "text": "}", "orig": "}", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.563, "r_y0": 676.886, "r_x1": 233.949, "r_y1": 676.886, "r_x2": 233.949, "r_y2": 668.3340000000001, "r_x3": 219.563, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.835, "r_y0": 676.722, "r_x1": 243.529, "r_y1": 676.722, "r_x2": 243.529, "r_y2": 668.016, "r_x3": 237.835, "r_y3": 668.016, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 243.529, "r_y0": 677.645, "r_x1": 247.089, "r_y1": 677.645, "r_x2": 247.089, "r_y2": 671.551, "r_x3": 243.529, "r_y3": 671.551, "coord_origin": "TOPLEFT"}, "text": "c", "orig": "c", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.473, "r_y0": 676.886, "r_x1": 258.118, "r_y1": 676.886, "r_x2": 258.118, "r_y2": 668.3340000000001, "r_x3": 251.473, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.003, "r_y0": 676.886, "r_x1": 286.362, "r_y1": 676.886, "r_x2": 286.362, "r_y2": 668.3340000000001, "r_x3": 262.003, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "the x-", "orig": "the x-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 688.841, "r_x1": 205.887, "r_y1": 688.841, "r_x2": 205.887, "r_y2": 680.289, "r_x3": 50.112, "r_y3": 680.289, "coord_origin": "TOPLEFT"}, "text": "coordinate for the corresponding point.", "orig": "coordinate for the corresponding point.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510206222534, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 701.111, "r_x1": 69.539, "r_y1": 701.111, "r_x2": 69.539, "r_y2": 692.559, "r_x3": 62.067, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.274, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 76.274, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Use the alignment computed in step 4, to compute", "orig": "Use the alignment computed in step 4, to compute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 94.605, "r_y1": 713.066, "r_x2": 94.605, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "the median", "orig": "the median", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.599, "r_y0": 712.902, "r_x1": 103.293, "r_y1": 712.902, "r_x2": 103.293, "r_y2": 704.196, "r_x3": 97.599, "r_y3": 704.196, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 103.292, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 103.292, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "-coordinate for all table columns and the me-", "orig": "-coordinate for all table columns and the me-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 211.20000000000005, "r": 545.115, "b": 255.61800000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8942298889160156, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 219.75199999999995, "r_x1": 435.915, "r_y1": 219.75199999999995, "r_x2": 435.915, "r_y2": 211.20000000000005, "r_x3": 308.862, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "dian cell size for all table cells.", "orig": "dian cell size for all table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.697, "r_y0": 219.75199999999995, "r_x1": 545.115, "r_y1": 219.75199999999995, "r_x2": 545.115, "r_y2": 211.20000000000005, "r_x3": 440.697, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "The usage of median dur-", "orig": "The usage of median dur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 231.70799999999997, "r_x1": 545.115, "r_y1": 231.70799999999997, "r_x2": 545.115, "r_y2": 223.15599999999995, "r_x3": 308.862, "r_y3": 223.15599999999995, "coord_origin": "TOPLEFT"}, "text": "ing the computations, helps to eliminate outliers caused by", "orig": "ing the computations, helps to eliminate outliers caused by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 243.663, "r_x1": 545.115, "r_y1": 243.663, "r_x2": 545.115, "r_y2": 235.111, "r_x3": 308.862, "r_y3": 235.111, "coord_origin": "TOPLEFT"}, "text": "occasional column spans which are usually wider than the", "orig": "occasional column spans which are usually wider than the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 255.61800000000005, "r_x1": 339.577, "r_y1": 255.61800000000005, "r_x2": 339.577, "r_y2": 247.06600000000003, "r_x3": 308.862, "r_y3": 247.06600000000003, "coord_origin": "TOPLEFT"}, "text": "normal.", "orig": "normal.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.371, "r": 545.115, "b": 279.87800000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9423392415046692, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 267.923, "r_x1": 328.289, "r_y1": 267.923, "r_x2": 328.289, "r_y2": 259.371, "r_x3": 320.817, "r_y3": 259.371, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.884, "r_y0": 267.923, "r_x1": 545.115, "r_y1": 267.923, "r_x2": 545.115, "r_y2": 259.371, "r_x3": 334.884, "r_y3": 259.371, "coord_origin": "TOPLEFT"}, "text": "Snap all cells with bad IOU to their corresponding", "orig": "Snap all cells with bad IOU to their corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 279.87800000000004, "r_x1": 338.192, "r_y1": 279.87800000000004, "r_x2": 338.192, "r_y2": 271.326, "r_x3": 308.862, "r_y3": 271.326, "coord_origin": "TOPLEFT"}, "text": "median", "orig": "median", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.682, "r_y0": 279.71400000000006, "r_x1": 346.376, "r_y1": 279.71400000000006, "r_x2": 346.376, "r_y2": 271.00800000000004, "r_x3": 340.682, "r_y3": 271.00800000000004, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.376, "r_y0": 279.87800000000004, "r_x1": 453.723, "r_y1": 279.87800000000004, "r_x2": 453.723, "r_y2": 271.326, "r_x3": 346.376, "r_y3": 271.326, "coord_origin": "TOPLEFT"}, "text": "-coordinates and cell sizes.", "orig": "-coordinates and cell sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 283.632, "r": 545.115, "b": 387.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 292.184, "r_x1": 328.289, "r_y1": 292.184, "r_x2": 328.289, "r_y2": 283.632, "r_x3": 320.817, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "7.", "orig": "7.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.144, "r_y0": 292.184, "r_x1": 545.115, "r_y1": 292.184, "r_x2": 545.115, "r_y2": 283.632, "r_x3": 332.144, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "Generate a new set of pair-wise matches between the", "orig": "Generate a new set of pair-wise matches between the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.139, "r_x1": 476.562, "r_y1": 304.139, "r_x2": 476.562, "r_y2": 295.587, "r_x3": 308.862, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "corrected bounding boxes and PDF cells.", "orig": "corrected bounding boxes and PDF cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 482.082, "r_y0": 304.139, "r_x1": 545.115, "r_y1": 304.139, "r_x2": 545.115, "r_y2": 295.587, "r_x3": 482.082, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "This time use a", "orig": "This time use a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.094, "r_x1": 545.115, "r_y1": 316.094, "r_x2": 545.115, "r_y2": 307.542, "r_x3": 308.862, "r_y3": 307.542, "coord_origin": "TOPLEFT"}, "text": "modified version of the IOU metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.862, "t": 391.579, "r": 545.115, "b": 459.906, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.862, "t": 463.66, "r": 545.115, "b": 567.8530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.8530000000001, "r_x1": 442.221, "r_y1": 567.8530000000001, "r_x2": 442.221, "r_y2": 559.3009999999999, "r_x3": 308.862, "r_y3": 559.3009999999999, "coord_origin": "TOPLEFT"}, "text": "created to match with the orphan.", "orig": "created to match with the orphan.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.862, "t": 571.607, "r": 545.117, "b": 604.069, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 580.159, "r_x1": 332.712, "r_y1": 580.159, "r_x2": 332.712, "r_y2": 571.607, "r_x3": 320.817, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "9a.", "orig": "9a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.943, "r_y0": 580.159, "r_x1": 545.115, "r_y1": 580.159, "r_x2": 545.115, "r_y2": 571.607, "r_x3": 337.943, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "Compute the top and bottom boundary of the hori-", "orig": "Compute the top and bottom boundary of the hori-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 592.114, "r_x1": 471.641, "r_y1": 592.114, "r_x2": 471.641, "r_y2": 583.562, "r_x3": 308.862, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "zontal band for each grid row (min/max", "orig": "zontal band for each grid row (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.834, "r_y0": 591.95, "r_x1": 479.719, "r_y1": 591.95, "r_x2": 479.719, "r_y2": 583.244, "r_x3": 474.834, "r_y3": 583.244, "coord_origin": "TOPLEFT"}, "text": "y", "orig": "y", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.269, "r_y0": 592.114, "r_x1": 545.117, "r_y1": 592.114, "r_x2": 545.117, "r_y2": 583.562, "r_x3": 483.269, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "coordinates per", "orig": "coordinates per", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 604.069, "r_x1": 329.913, "r_y1": 604.069, "r_x2": 329.913, "r_y2": 595.517, "r_x3": 308.862, "r_y3": 595.517, "coord_origin": "TOPLEFT"}, "text": "row).", "orig": "row).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.862, "t": 607.822, "r": 545.115, "b": 628.33, "coord_origin": "TOPLEFT"}, "confidence": 0.7716891169548035, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 616.374, "r_x1": 332.872, "r_y1": 616.374, "r_x2": 332.872, "r_y2": 607.822, "r_x3": 320.817, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "9b.", "orig": "9b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.925, "r_y0": 616.374, "r_x1": 374.226, "r_y1": 616.374, "r_x2": 374.226, "r_y2": 607.822, "r_x3": 339.925, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "Intersect", "orig": "Intersect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.042, "r_y0": 616.374, "r_x1": 545.115, "r_y1": 616.374, "r_x2": 545.115, "r_y2": 607.822, "r_x3": 378.042, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "the orphan's bounding box with the row", "orig": "the orphan's bounding box with the row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 628.33, "r_x1": 495.292, "r_y1": 628.33, "r_x2": 495.292, "r_y2": 619.778, "r_x3": 308.862, "r_y3": 619.778, "coord_origin": "TOPLEFT"}, "text": "bands, and map the cell to the closest grid row.", "orig": "bands, and map the cell to the closest grid row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.862, "t": 632.083, "r": 545.115, "b": 664.545, "coord_origin": "TOPLEFT"}, "confidence": 0.8584907054901123, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 640.635, "r_x1": 332.712, "r_y1": 640.635, "r_x2": 332.712, "r_y2": 632.083, "r_x3": 320.817, "r_y3": 632.083, "coord_origin": "TOPLEFT"}, "text": "9c.", "orig": "9c.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.534, "r_y0": 640.635, "r_x1": 545.115, "r_y1": 640.635, "r_x2": 545.115, "r_y2": 632.083, "r_x3": 337.534, "r_y3": 632.083, "coord_origin": "TOPLEFT"}, "text": "Compute the left and right boundary of the vertical", "orig": "Compute the left and right boundary of the vertical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.59, "r_x1": 455.282, "r_y1": 652.59, "r_x2": 455.282, "r_y2": 644.038, "r_x3": 308.862, "r_y3": 644.038, "coord_origin": "TOPLEFT"}, "text": "band for each grid column (min/max", "orig": "band for each grid column (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 457.777, "r_y0": 652.4259999999999, "r_x1": 463.471, "r_y1": 652.4259999999999, "r_x2": 463.471, "r_y2": 643.72, "r_x3": 457.777, "r_y3": 643.72, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.971, "r_y0": 652.59, "r_x1": 545.114, "r_y1": 652.59, "r_x2": 545.114, "r_y2": 644.038, "r_x3": 465.971, "r_y3": 644.038, "coord_origin": "TOPLEFT"}, "text": "coordinates per col-", "orig": "coordinates per col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.545, "r_x1": 332.384, "r_y1": 664.545, "r_x2": 332.384, "r_y2": 655.9929999999999, "r_x3": 308.862, "r_y3": 655.9929999999999, "coord_origin": "TOPLEFT"}, "text": "umn).", "orig": "umn).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.862, "t": 668.299, "r": 545.115, "b": 688.806, "coord_origin": "TOPLEFT"}, "confidence": 0.7590745091438293, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 676.851, "r_x1": 545.115, "r_y1": 676.851, "r_x2": 545.115, "r_y2": 668.299, "r_x3": 320.817, "r_y3": 668.299, "coord_origin": "TOPLEFT"}, "text": "9d. Intersect the orphan's bounding box with the column", "orig": "9d. Intersect the orphan's bounding box with the column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 688.806, "r_x1": 510.585, "r_y1": 688.806, "r_x2": 510.585, "r_y2": 680.254, "r_x3": 308.862, "r_y3": 680.254, "coord_origin": "TOPLEFT"}, "text": "bands, and map the cell to the closest grid column.", "orig": "bands, and map the cell to the closest grid column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.862, "t": 692.559, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 320.817, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "9e. If the table cell under the identified row and column", "orig": "9e. If the table cell under the identified row and column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "is not empty, extend its content with the content of the or-", "orig": "is not empty, extend its content with the content of the or-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9e. 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Nassar +DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis +Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com +Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com +Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com +Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com +Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com ABSTRACT -Accurate document layout analysis is a key requirement for high- +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. CCS CONCEPTS -• -Permission to make digital or hard copies of part or all of this work for personal or https://doi.org/10.1145/3534678.3539043 -Peter Staar +· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning Computer vision ; ; Object detection ; +Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). +KDD '22, August 14-18, 2022, Washington, DC, USA +© 2022 Copyright held by the owner/author(s). +ACM ISBN 978-1-4503-9385-0/22/08. +https://doi.org/10.1145/3534678.3539043
-Figure 1: +Figure 1: Four examples of complex page layouts across different document categories
KEYWORDS -PDF document conversion, layout segmentation, object-detection, +PDF document conversion, layout segmentation, object-detection, data set, Machine Learning ACMReference Format: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter -1 -Despite the substantial improvements achieved with machine-learning -Akeyproblem in the process of document conversion is to under- -In this paper, we present the DocLayNet dataset. It provides page- -- (1) -- (2) -- (3) -- (4) -This enables experimentation with annotation uncertainty -- (5) -All aspects outlined above are detailed in Section 3. In Section 4, -In Section 5, we will present baseline accuracy numbers for a -2 -While early approaches in document-layout analysis used rule- -Lately, new types of ML models for document-layout analysis -3 -DocLayNet contains 80863 PDF pages. Among these, 7059 carry two -In addition to open intellectual property constraints for the +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 +1 INTRODUCTION +Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. +Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. +In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: +- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. +- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources. +- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. +- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation. +This enables experimentation with annotation uncertainty and quality control analysis. +- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. +All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns. +In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery. +2 RELATED WORK +While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]. +Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish. +3 THE DOCLAYNET DATASET +DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4. +In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents
-Figure 2: Distribution of DocLayNet pages across document +Figure 2: Distribution of DocLayNet pages across document categories.
-to a minimum, since they introduce difficulties in annotation (see -The pages in DocLayNet can be grouped into six distinct cate- -We did not control the document selection with regard to lan- -To ensure that future benchmarks in the document-layout analy- -Table 1 shows the overall frequency and distribution of the labels -In order to accommodate the different types of models currently -Despite being cost-intense and far less scalable than automation, -4 -The annotation campaign was carried out in four phases. In phase +to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild". +The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. +We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. +To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. +Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5. +In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 × 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. +Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4. +4 ANNOTATION CAMPAIGN +The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four, - + +% of Total% of Total% of Totaltriple inter-annotator mAP @ 0.5-0.95 (%)triple inter-annotator mAP @ 0.5-0.95 (%)triple inter-annotator mAP @ 0.5-0.95 (%)triple inter-annotator mAP @ 0.5-0.95 (%)triple inter-annotator mAP @ 0.5-0.95 (%)triple inter-annotator mAP @ 0.5-0.95 (%)triple inter-annotator mAP @ 0.5-0.95 (%) +class labelCountTrainTestValAllFinManSciLawPatTen +Caption225242.041.772.3284-8940-6186-9294-9995-9969-78n/a +Footnote63180.600.310.5883-91n/a10062-8885-94n/a82-97 +Formula250272.251.902.9683-85n/an/a84-8786-96n/an/a +List-item18566017.1913.3415.8287-8874-8390-9297-9781-8575-8893-95 +Page-footer708786.515.586.0093-9488-9095-9610092-9710096-98 +Page-header580225.106.705.0685-8966-7690-9498-10091-9297-9981-86 +Picture459764.212.785.3169-7156-5982-8669-8280-9566-7159-76 +Section-header14288412.6015.7712.8583-8476-8190-9294-9587-9469-7378-86 +Table347333.202.273.6077-8175-8083-8698-9958-8079-8470-85 +Text51037745.8249.2845.0084-8681-8688-9389-9387-9271-7987-95 +Title50710.470.300.5060-7224-6350-6394-10082-9668-7924-56 +Total1107470941123998166653182-8371-7479-8189-9486-9171-7668-85
Figure 3: Corpus Conversion Service annotation user inter-Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
-Figure 3: Corpus Conversion Service annotation user inter- +Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
-Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % +Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
-we distributed the annotation workload and performed continuous -Phase 1: Data selection and preparation. -include publication repositories such as arXiv -Preparation work included uploading and parsing the sourced -Phase 2: Label selection and guideline. -the textual content of an element, which goes beyond visual layout -At first sight, the task of visual document-layout interpretation -Obviously, this inconsistency in annotations is not desirable for -- (1) -- (2) -- (3) -- (4) -- (5) -- (6) -The complete annotation guideline is over 100 pages long and a -Phase 3: Training. +we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. +Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. +Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. +Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on +the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category. +At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. +Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: +- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object. +- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement. +- (3) For every Caption , there must be exactly one corresponding Picture or Table . +- (4) Connected sub-pictures are grouped together in one Picture object. +- (5) Formula numbers are included in a Formula object. +- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line. +The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference. +Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations
-Figure 4: Examples of plausible annotation alternatives for
-were carried out over a timeframe of 12 weeks, after which 8 of the -Phase 4: Production annotation. -Table 2: Prediction performance (mAP@0.5-0.95) of object +05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0 +Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous. +were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar. +Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted +Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. +humanMRCNNMRCNNFRCNNYOLO +R50R101R101v5x6 +Caption84-8968.471.570.177.7 +Footnote83-9170.971.873.777.2 +Formula83-8560.163.463.566.2 +List-item87-8881.280.881.086.2 +Page-footer93-9461.659.358.961.1 +Page-header85-8971.970.072.067.9 +Picture69-7171.772.772.077.1 +Section-header83-8467.669.368.474.6 +Table77-8182.282.982.286.3 +Text84-8684.685.885.488.1 +Title60-7276.780.479.982.7 +All82-8372.473.573.476.8
-to avoid this at any cost in order to have clear, unbiased baseline -5 -The primary goal of DocLayNet is to obtain high-quality ML models +to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. +5 EXPERIMENTS +The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this
-Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask +Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.
-paper and leave the detailed evaluation of more recent methods -In this section, we will present several aspects related to the +paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work. +In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]. Baselines for Object Detection -In Table 2, we present baseline experiments (given in mAP) on Mask -Table 3: Performance of a Mask R-CNN R50 network in +In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document. +Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. +Class-count11654 +Caption68TextTextText +Footnote71TextTextText +Formula60TextTextText +List-item81Text82Text +Page-footer6262-- +Page-header7268-- +Picture72727272 +Section-header68676968 +Table82838282 +Text85848484 +Title77Sec.-h.Sec.-h.Sec.-h. +Overall72737877
Learning Curve -One of the fundamental questions related to any dataset is if it is +One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles. Impact of Class Labels -The choice and number of labels can have a significant effect on -Table 4: Performance of a Mask R-CNN R50 network with +The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of +Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in /tildelow 10% point improvement. +Class-count111155 +SplitDocPageDocPage +Caption6883 +Footnote7184 +Formula6066 +List-item81888288 +Page-footer6289 +Page-header7290 +Picture72827282 +Section-header68836983 +Table82898290 +Text85918490 +Title7781 +All72847887
-lists in PubLayNet (grouped list-items) versus DocLayNet (separate +lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded. Impact of Document Split in Train and Test Set -Many documents in DocLayNet have a unique styling. In order +Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains ˜ 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided. Dataset Comparison -Throughout this paper, we claim that DocLayNet's wider variety of -Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask +Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , +Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. +Testing onTesting onTesting on +Training onlabelsPLNDBDLN +PubLayNet (PLN)Figure964323 +PubLayNet (PLN)Sec-header87-32 +Table952449 +Text96-42 +total933430 +DocBank (DB)Figure777131 +DocBank (DB)Table196522 +DocBank (DB)total486827 +DocLayNet (DLN)Figure675172 +DocLayNet (DLN)Sec-header53-68 +Table874382 +Text77-84 +total594778
-Section-header -For comparison of DocBank with DocLayNet, we trained only +Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text . +For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. Example Predictions -To conclude this section, we illustrate the quality of layout predic- -6 -In this paper, we presented the DocLayNet dataset. It provides the -From the dataset, we have derived on the one hand reference -To date, there is still a significant gap between human and ML +To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence. +6 CONCLUSION +In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. +From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust. +To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap. REFERENCES -- [1] -- [2] -- [3] -- [4] -- [5] -- [6] -- [7] -- [8] -- [9] -- [10] -- [11] -- [12] -- [13] +- [1] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013. +- [2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. 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A survey on image data augmentation for deep learning. 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In phase", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [53.798, 229.76599999999996, 295.649, 237.21900000000005], "page": 4, "span": [0, 58], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user inter-", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [53.467, 149.47699999999998, 294.047, 157.27800000000002], "page": 4, "span": [0, 63], "__ref_s3_data": null}], "text": "we distributed the annotation workload and performed continuous", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [63.761, 105.76499999999999, 226.725, 113.21799999999996], "page": 4, "span": [0, 40], "__ref_s3_data": null}], "text": "Phase 1: Data selection and preparation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 471.689, 481.034, 479.49], "page": 4, "span": [0, 46], "__ref_s3_data": null}], "text": "include publication repositories such as arXiv", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [327.918, 405.936, 558.206, 413.737], "page": 4, "span": [0, 59], "__ref_s3_data": null}], "text": "Preparation work included uploading and parsing the sourced", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [327.918, 274.553, 482.418, 282.006], "page": 4, "span": [0, 39], "__ref_s3_data": null}], "text": "Phase 2: Label selection and guideline.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 86.20900000000006, 320.503, 90.976], "page": 4, "span": [0, 1], "__ref_s3_data": null}], "text": "3", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [53.798, 696.405, 294.045, 704.206], "page": 5, "span": [0, 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"The complete annotation guideline is over 100 pages long and a", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [63.761, 207.10699999999997, 136.774, 214.55999999999995], "page": 5, "span": [0, 18], "__ref_s3_data": null}], "text": "Phase 3: Training.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [317.623, 258.27099999999996, 558.204, 266.072], "page": 5, "span": [0, 67], "__ref_s3_data": null}], "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [327.918, 236.47699999999998, 456.801, 243.93000000000006], "page": 5, "span": [0, 31], "__ref_s3_data": null}], "text": "Phase 4: Production annotation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.502, 696.903, 294.044, 704.356], "page": 6, "span": [0, 56], "__ref_s3_data": null}], "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [53.798, 412.841, 294.047, 420.642], "page": 6, "span": [0, 67], "__ref_s3_data": null}], "text": "to avoid this at any cost in order to have clear, unbiased baseline", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 193.865, 59.405, 202.93200000000002], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.529, 170.515, 294.049, 178.31600000000003], "page": 6, "span": [0, 65], "__ref_s3_data": null}], "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [317.955, 399.757, 558.204, 407.558], "page": 6, "span": [0, 62], "__ref_s3_data": null}], "text": "paper and leave the detailed evaluation of more recent methods", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [327.918, 377.839, 558.198, 385.64], "page": 6, "span": [0, 63], "__ref_s3_data": null}], "text": "In this section, we will present several aspects related to the", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 285.173, 466.853, 294.24], "page": 6, "span": [0, 30], "__ref_s3_data": null}], "text": "Baselines for Object Detection", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.955, 271.744, 558.431, 279.5450000000001], "page": 6, "span": [0, 66], "__ref_s3_data": null}], "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", 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"type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.529, 226.05700000000002, 294.043, 233.85799999999995], "page": 7, "span": [0, 64], "__ref_s3_data": null}], "text": "The choice and number of labels can have a significant effect on", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.659, 696.903, 558.201, 704.356], "page": 7, "span": [0, 53], "__ref_s3_data": null}], "text": "Table 4: Performance of a Mask R-CNN R50 network with", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [317.955, 452.365, 558.202, 460.166], "page": 7, "span": [0, 66], "__ref_s3_data": null}], "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 352.6, 549.861, 361.667], "page": 7, "span": [0, 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"page": 1, "span": [0, 73], "__ref_s3_data": null}], "text": "Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [255.116, 611.919, 357.88, 657.754], "page": 1, "span": [0, 71], "__ref_s3_data": null}], "text": "Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [419.265, 611.919, 522.029, 657.754], "page": 1, "span": [0, 70], "__ref_s3_data": null}], "text": "Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [172.543, 553.534, 275.307, 599.369], "page": 1, "span": [0, 72], "__ref_s3_data": null}], "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [336.693, 553.534, 439.457, 599.369], "page": 1, "span": [0, 68], "__ref_s3_data": null}], "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 534.292, 111.944, 543.359], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "ABSTRACT", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.467, 257.85, 295.56, 528.665], "page": 1, "span": [0, 1595], "__ref_s3_data": null}], "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 230.99800000000005, 134.82, 240.06500000000005], "page": 1, "span": [0, 12], "__ref_s3_data": null}], "text": "CCS CONCEPTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.798, 194.71000000000004, 297.853, 227.91700000000003], "page": 1, "span": [0, 167], "__ref_s3_data": null}], "text": "\u00b7 Informationsystems \u2192 Documentstructure ; \u00b7 Appliedcomputing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning Computer vision ; ; Object detection ;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 119.31899999999996, 295.118, 157.26700000000005], "page": 1, "span": [0, 397], "__ref_s3_data": null}], "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 110.519, 197.863, 116.66899999999998], "page": 1, "span": [0, 48], "__ref_s3_data": null}], "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.317, 101.78499999999997, 186.746, 107.85299999999995], "page": 1, "span": [0, 45], "__ref_s3_data": null}], "text": "\u00a9 2022 Copyright held by the owner/author(s).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.554, 93.81399999999996, 157.031, 99.88200000000006], "page": 1, "span": [0, 33], "__ref_s3_data": null}], "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 85.84400000000005, 166.941, 91.91200000000003], "page": 1, "span": [0, 39], "__ref_s3_data": null}], "text": "https://doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [317.955, 189.529, 379.821, 198.596], "page": 1, "span": [0, 8], "__ref_s3_data": null}], "text": "KEYWORDS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.955, 165.14200000000005, 559.186, 183.90200000000004], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.66, 144.63599999999997, 404.654, 151.26], "page": 1, "span": [0, 20], "__ref_s3_data": null}], "text": "ACMReference Format:", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.955, 84.75, 559.549, 141.49700000000007], "page": 1, "span": [0, 374], "__ref_s3_data": null}], "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 696.135, 156.529, 705.202], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "1 INTRODUCTION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.529, 563.196, 303.017, 680.586], "page": 2, "span": [0, 702], "__ref_s3_data": null}], "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.529, 289.224, 295.564, 560.038], "page": 2, "span": [0, 1578], "__ref_s3_data": null}], "text": "Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.592, 212.51099999999997, 295.564, 286.066], "page": 2, "span": [0, 462], "__ref_s3_data": null}], "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.708, 177.269, 295.562, 207.05099999999993], "page": 2, "span": [0, 149], "__ref_s3_data": null}], "text": "- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.708, 155.35199999999998, 294.263, 174.17399999999998], "page": 2, "span": [0, 109], "__ref_s3_data": null}], "text": "- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.708, 122.47500000000002, 294.684, 152.25700000000006], "page": 2, "span": [0, 180], "__ref_s3_data": null}], "text": "- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.708, 100.55700000000002, 295.564, 119.38], "page": 2, "span": [0, 115], "__ref_s3_data": null}], "text": "- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.672, 83.37099999999998, 216.027, 91.10299999999995], "page": 2, "span": [0, 56], "__ref_s3_data": null}], "text": "1 https://developer.ibm.com/exchanges/data/all/doclaynet", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [342.095, 685.446, 558.432, 704.206], "page": 2, "span": [0, 86], "__ref_s3_data": null}], "text": "This enables experimentation with annotation uncertainty and quality control analysis.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [328.865, 630.652, 559.721, 682.351], "page": 2, "span": [0, 280], "__ref_s3_data": null}], "text": "- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.623, 571.957, 559.19, 623.594], "page": 2, "span": [0, 297], "__ref_s3_data": null}], "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.731, 484.286, 559.582, 568.799], "page": 2, "span": [0, 506], "__ref_s3_data": null}], "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 460.786, 421.744, 469.853], "page": 2, "span": [0, 14], "__ref_s3_data": null}], "text": "2 RELATED WORK", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.525, 327.847, 559.716, 445.237], "page": 2, "span": [0, 655], "__ref_s3_data": null}], "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 240.17499999999995, 559.186, 324.689], "page": 2, "span": [0, 500], "__ref_s3_data": null}], "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 216.67499999999995, 477.457, 225.74199999999996], "page": 2, "span": [0, 23], "__ref_s3_data": null}], "text": "3 THE DOCLAYNET DATASET", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.955, 116.61300000000006, 559.713, 201.12599999999998], "page": 2, "span": [0, 522], "__ref_s3_data": null}], "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 83.73599999999999, 558.204, 113.45500000000004], "page": 2, "span": [0, 186], "__ref_s3_data": null}], "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [53.798, 425.253, 294.274, 509.766], "page": 3, "span": [0, 513], "__ref_s3_data": null}], "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild\".", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.574, 282.787, 295.56, 422.095], "page": 3, "span": [0, 810], "__ref_s3_data": null}], "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.467, 184.15700000000004, 295.562, 279.629], "page": 3, "span": [0, 535], "__ref_s3_data": null}], "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 107.44499999999994, 295.564, 180.99900000000002], "page": 3, "span": [0, 413], "__ref_s3_data": null}], "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 83.94100000000003, 195.79, 91.673], "page": 3, "span": [0, 47], "__ref_s3_data": null}], "text": "2 e.g. AAPL from https://www.annualreports.com/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [317.623, 630.652, 559.192, 704.206], "page": 3, "span": [0, 435], "__ref_s3_data": null}], "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 521.063, 558.438, 627.494], "page": 3, "span": [0, 645], "__ref_s3_data": null}], "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [316.942, 203.25400000000002, 559.722, 517.905], "page": 3, "span": [0, 1854], "__ref_s3_data": null}], "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 175.14499999999998, 470.213, 184.212], "page": 3, "span": [0, 21], "__ref_s3_data": null}], "text": "4 ANNOTATION CAMPAIGN", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.686, 86.04099999999994, 559.714, 159.596], "page": 3, "span": [0, 457], "__ref_s3_data": null}], "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [53.502, 676.903, 558.49, 706.274], "page": 4, "span": [0, 348], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [53.467, 116.60000000000002, 294.047, 157.27800000000002], "page": 4, "span": [0, 231], "__ref_s3_data": null}], "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 83.72299999999996, 295.558, 113.44200000000001], "page": 4, "span": [0, 567], "__ref_s3_data": null}, {"bbox": [53.798, 83.72299999999996, 295.558, 113.44200000000001], "page": 4, "span": [0, 567], "__ref_s3_data": null}], "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 285.388, 559.713, 413.737], "page": 4, "span": [0, 746], "__ref_s3_data": null}], "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.623, 99.08699999999999, 559.718, 282.23], "page": 4, "span": [0, 1159], "__ref_s3_data": null}], "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 83.24400000000003, 369.246, 90.976], "page": 4, "span": [0, 20], "__ref_s3_data": null}], "text": "3 https://arxiv.org/", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [53.798, 685.402, 294.045, 704.206], "page": 5, "span": [0, 135], "__ref_s3_data": null}], "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 542.981, 295.559, 682.288], "page": 5, "span": [0, 812], "__ref_s3_data": null}], "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 455.309, 295.56, 539.823], "page": 5, "span": [0, 465], "__ref_s3_data": null}], "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.708, 402.335, 294.046, 443.057], "page": 5, "span": [0, 202], "__ref_s3_data": null}], "text": "- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.708, 358.543, 295.564, 399.284], "page": 5, "span": [0, 208], "__ref_s3_data": null}], "text": "- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.708, 336.581, 294.047, 355.448], "page": 5, "span": [0, 82], "__ref_s3_data": null}], "text": "- (3) For every Caption , there must be exactly one corresponding Picture or Table .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.708, 314.708, 294.046, 333.531], "page": 5, "span": [0, 70], "__ref_s3_data": null}], "text": "- (4) Connected sub-pictures are grouped together in one Picture object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.708, 303.705, 264.506, 311.613], "page": 5, "span": [0, 53], "__ref_s3_data": null}], "text": "- (5) Formula numbers are included in a Formula object.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [64.708, 270.87200000000007, 294.046, 300.591], "page": 5, "span": [0, 160], "__ref_s3_data": null}], "text": "- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.529, 217.942, 295.563, 258.619], "page": 5, "span": [0, 221], "__ref_s3_data": null}], "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 86.43499999999995, 295.562, 214.784], "page": 5, "span": [0, 792], "__ref_s3_data": null}], "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [400.128, 331.517, 476.305, 333.335], "page": 5, "span": [0, 64], "__ref_s3_data": null}], "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 288.364, 559.806, 317.735], "page": 5, "span": [0, 173], "__ref_s3_data": null}], "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [317.623, 247.312, 558.204, 266.072], "page": 5, "span": [0, 123], "__ref_s3_data": null}], "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.623, 82.928, 559.715, 244.154], "page": 5, "span": [0, 987], "__ref_s3_data": null}], "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.502, 609.232, 295.649, 704.356], "page": 6, "span": [0, 489], "__ref_s3_data": null}], "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [53.529, 215.58000000000004, 295.556, 420.642], "page": 6, "span": [0, 1252], "__ref_s3_data": null}], "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 193.865, 147.485, 202.93200000000002], "page": 6, "span": [0, 13], "__ref_s3_data": null}], "text": "5 EXPERIMENTS", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.484, 82.84400000000005, 295.428, 178.31600000000003], "page": 6, "span": [0, 584], "__ref_s3_data": null}], "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [317.955, 388.798, 558.204, 407.558], "page": 6, "span": [0, 102], "__ref_s3_data": null}], "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.641, 312.086, 558.437, 385.64], "page": 6, "span": [0, 397], "__ref_s3_data": null}], "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 285.173, 466.853, 294.24], "page": 6, "span": [0, 30], "__ref_s3_data": null}], "text": "Baselines for Object Detection", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.749, 85.44299999999998, 558.431, 279.5450000000001], "page": 6, "span": [0, 1146], "__ref_s3_data": null}], "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.502, 664.026, 295.649, 704.356], "page": 7, "span": [0, 205], "__ref_s3_data": null}], "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [53.798, 462.425, 131.056, 471.492], "page": 7, "span": [0, 14], "__ref_s3_data": null}], "text": "Learning Curve", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [52.785, 262.69499999999994, 295.558, 456.798], "page": 7, "span": [0, 1157], "__ref_s3_data": null}], "text": "One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 239.485, 164.329, 248.55200000000002], "page": 7, "span": [0, 22], "__ref_s3_data": null}], "text": "Impact of Class Labels", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.467, 83.59100000000001, 295.557, 233.85799999999995], "page": 7, "span": [0, 910], "__ref_s3_data": null}], "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.659, 664.026, 559.807, 704.356], "page": 7, "span": [0, 183], "__ref_s3_data": null}], "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in /tildelow 10% point improvement.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [317.686, 375.653, 559.585, 460.166], "page": 7, "span": [0, 469], "__ref_s3_data": null}], "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 352.6, 549.861, 361.667], "page": 7, "span": [0, 46], "__ref_s3_data": null}], "text": "Impact of Document Split in Train and Test Set", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.623, 196.70600000000002, 559.714, 346.973], "page": 7, "span": [0, 853], "__ref_s3_data": null}], "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 173.65300000000002, 418.548, 182.72000000000003], "page": 7, "span": [0, 18], "__ref_s3_data": null}], "text": "Dataset Comparison", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.686, 83.46799999999996, 559.188, 168.02600000000007], "page": 7, "span": [0, 521], "__ref_s3_data": null}], "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.502, 642.108, 295.649, 704.356], "page": 8, "span": [0, 298], "__ref_s3_data": null}], "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [53.798, 348.968, 294.047, 400.712], "page": 8, "span": [0, 295], "__ref_s3_data": null}], "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.467, 206.54700000000003, 295.559, 345.855], "page": 8, "span": [0, 793], "__ref_s3_data": null}], "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [53.798, 176.93399999999997, 156.005, 186.00099999999998], "page": 8, "span": [0, 19], "__ref_s3_data": null}], "text": "Example Predictions", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [53.529, 86.793, 295.558, 171.30600000000004], "page": 8, "span": [0, 481], "__ref_s3_data": null}], "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 696.135, 405.73, 705.202], "page": 8, "span": [0, 12], "__ref_s3_data": null}], "text": "6 CONCLUSION", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [317.955, 605.994, 559.714, 690.508], "page": 8, "span": [0, 507], "__ref_s3_data": null}], "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.686, 507.364, 559.717, 602.836], "page": 8, "span": [0, 573], "__ref_s3_data": null}], "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.623, 474.487, 558.435, 504.206], "page": 8, "span": [0, 188], "__ref_s3_data": null}], "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 446.903, 387.37, 455.97], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "REFERENCES", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [321.198, 420.922, 558.201, 442.963], "page": 8, "span": [0, 191], "__ref_s3_data": null}], "text": "- [1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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IEEE Computer Society, Oct 2017.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.955, 86.21000000000004, 558.972, 116.18899999999996], "page": 8, "span": [0, 305], "__ref_s3_data": null}], "text": "- [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [53.798, 286.418, 559.808, 326.747], "page": 9, "span": [0, 386], "__ref_s3_data": null}], "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [69.234, 242.591, 295.224, 264.59899999999993], "page": 9, "span": [0, 195], "__ref_s3_data": null}], "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [317.955, 250.56100000000004, 559.026, 264.59899999999993], "page": 9, "span": [0, 153], "__ref_s3_data": null}], "text": "- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798, 218.64800000000002, 295.122, 240.68899999999996], "page": 9, "span": [0, 190], "__ref_s3_data": null}], "text": "- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798, 202.707, 294.042, 216.779], "page": 9, "span": [0, 132], "__ref_s3_data": null}], "text": "- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798, 178.83000000000004, 295.223, 200.83799999999997], "page": 9, "span": [0, 219], "__ref_s3_data": null}], "text": "- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798, 162.89, 295.12, 176.928], "page": 9, "span": [0, 100], "__ref_s3_data": null}], "text": "- [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798, 123.03899999999999, 294.809, 160.98800000000006], "page": 9, "span": [0, 339], "__ref_s3_data": null}], "text": "- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [53.798, 83.18899999999996, 295.222, 121.13699999999994], "page": 9, "span": [0, 336], "__ref_s3_data": null}], "text": "- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.955, 226.65100000000007, 558.971, 248.659], "page": 9, "span": [0, 188], "__ref_s3_data": null}], "text": "- [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.955, 194.73699999999997, 559.275, 224.74900000000002], "page": 9, "span": [0, 290], "__ref_s3_data": null}], "text": "- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [317.955, 178.79700000000003, 559.378, 192.86799999999994], "page": 9, "span": [0, 138], "__ref_s3_data": null}], "text": "- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [323.408203125, 266.1492919921875, 553.2952270507812, 541.6512603759766], "page": 1, "span": [0, 84], "__ref_s3_data": null}], "text": "Figure 1: Four examples of complex page layouts across different document categories", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [88.33030700683594, 571.4317321777344, 263.7049560546875, 699.1134796142578], "page": 3, "span": [0, 69], "__ref_s3_data": null}], "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [53.05912780761719, 251.135986328125, 295.8506164550781, 481.2087097167969], "page": 4, "span": [0, 281], "__ref_s3_data": null}], "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [315.960205078125, 332.31915283203125, 559.396484375, 706.6611862182617], "page": 5, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [323.48431396484375, 531.9892272949219, 553.5411376953125, 702.1139678955078], "page": 6, "span": [0, 322], "__ref_s3_data": null}], "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [52.963985443115234, 349.8648681640625, 556.931640625, 707.2641143798828], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [98.93103790283203, 497.91851806640625, 512.579833984375, 654.5245208740234], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. 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[], "footnotes": [], "page-dimensions": [{"height": 792.0, "page": 1, "width": 612.0}, {"height": 792.0, "page": 2, "width": 612.0}, {"height": 792.0, "page": 3, "width": 612.0}, {"height": 792.0, "page": 4, "width": 612.0}, {"height": 792.0, "page": 5, "width": 612.0}, {"height": 792.0, "page": 6, "width": 612.0}, {"height": 792.0, "page": 7, "width": 612.0}, {"height": 792.0, "page": 8, "width": 612.0}, {"height": 792.0, "page": 9, "width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/2206.01062.md b/tests/data/groundtruth/docling_v1/2206.01062.md index 99823867..8ce1dcd4 100644 --- a/tests/data/groundtruth/docling_v1/2206.01062.md +++ b/tests/data/groundtruth/docling_v1/2206.01062.md @@ -1,249 +1,336 @@ -## DocLayNet: A Large Human-Annotated Dataset for +## DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis -Birgit Pfitzmann +Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com -Christoph Auer +Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com -Michele Dolfi +Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com -Ahmed S. Nassar +Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com + +Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com ## ABSTRACT -Accurate document layout analysis is a key requirement for high- +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. ## CCS CONCEPTS -• +· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning Computer vision ; ; Object detection ; -Permission to make digital or hard copies of part or all of this work for personal or https://doi.org/10.1145/3534678.3539043 +Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). -Peter Staar +KDD '22, August 14-18, 2022, Washington, DC, USA -Figure 1: +© 2022 Copyright held by the owner/author(s). + +ACM ISBN 978-1-4503-9385-0/22/08. + +https://doi.org/10.1145/3534678.3539043 + +Figure 1: Four examples of complex page layouts across different document categories ## KEYWORDS -PDF document conversion, layout segmentation, object-detection, +PDF document conversion, layout segmentation, object-detection, data set, Machine Learning ## ACMReference Format: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 -## 1 +## 1 INTRODUCTION -Despite the substantial improvements achieved with machine-learning +Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. -Akeyproblem in the process of document conversion is to under- +Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. -In this paper, we present the DocLayNet dataset. It provides page- +In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: -- (1) +- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. -- (2) +- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources. -- (3) +- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. -- (4) +- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation. -This enables experimentation with annotation uncertainty +This enables experimentation with annotation uncertainty and quality control analysis. -- (5) +- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. -All aspects outlined above are detailed in Section 3. In Section 4, +All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns. -In Section 5, we will present baseline accuracy numbers for a +In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery. -## 2 +## 2 RELATED WORK -While early approaches in document-layout analysis used rule- +While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]. -Lately, new types of ML models for document-layout analysis +Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish. -## 3 +## 3 THE DOCLAYNET DATASET -DocLayNet contains 80863 PDF pages. Among these, 7059 carry two +DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4. -In addition to open intellectual property constraints for the +In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents -Figure 2: Distribution of DocLayNet pages across document +Figure 2: Distribution of DocLayNet pages across document categories. -to a minimum, since they introduce difficulties in annotation (see +to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild". -The pages in DocLayNet can be grouped into six distinct cate- +The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. -We did not control the document selection with regard to lan- +We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. -To ensure that future benchmarks in the document-layout analy- +To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. -Table 1 shows the overall frequency and distribution of the labels +Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5. -In order to accommodate the different types of models currently +In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 × 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. -Despite being cost-intense and far less scalable than automation, +Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4. -## 4 +## 4 ANNOTATION CAMPAIGN -The annotation campaign was carried out in four phases. In phase +The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four, -Figure 3: Corpus Conversion Service annotation user inter- +Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. +| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | +|----------------|---------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------| +| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten | +| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a | +| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 | +| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a | +| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 | +| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 | +| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 | +| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 | +| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 | +| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 | +| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 | +| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 | +| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 | - -Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % +Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. -we distributed the annotation workload and performed continuous +we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. -Phase 1: Data selection and preparation. +Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. -include publication repositories such as arXiv +Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. -Preparation work included uploading and parsing the sourced +Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on -Phase 2: Label selection and guideline. +the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category. -the textual content of an element, which goes beyond visual layout +At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. -At first sight, the task of visual document-layout interpretation +Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: -Obviously, this inconsistency in annotations is not desirable for +- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object. -- (1) +- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement. -- (2) +- (3) For every Caption , there must be exactly one corresponding Picture or Table . -- (3) +- (4) Connected sub-pictures are grouped together in one Picture object. -- (4) +- (5) Formula numbers are included in a Formula object. -- (5) +- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line. -- (6) +The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference. -The complete annotation guideline is over 100 pages long and a +Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations -Phase 3: Training. - -Figure 4: Examples of plausible annotation alternatives for -were carried out over a timeframe of 12 weeks, after which 8 of the +05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0 -Phase 4: Production annotation. +Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous. -Table 2: Prediction performance (mAP@0.5-0.95) of object +were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar. -to avoid this at any cost in order to have clear, unbiased baseline +Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted -## 5 +Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. -The primary goal of DocLayNet is to obtain high-quality ML models +| | human | MRCNN | MRCNN | FRCNN | YOLO | +|----------------|---------|---------|---------|---------|--------| +| | | R50 | R101 | R101 | v5x6 | +| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 | +| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 | +| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 | +| List-item | 87-88 | 81.2 | 80.8 | 81.0 | 86.2 | +| Page-footer | 93-94 | 61.6 | 59.3 | 58.9 | 61.1 | +| Page-header | 85-89 | 71.9 | 70.0 | 72.0 | 67.9 | +| Picture | 69-71 | 71.7 | 72.7 | 72.0 | 77.1 | +| Section-header | 83-84 | 67.6 | 69.3 | 68.4 | 74.6 | +| Table | 77-81 | 82.2 | 82.9 | 82.2 | 86.3 | +| Text | 84-86 | 84.6 | 85.8 | 85.4 | 88.1 | +| Title | 60-72 | 76.7 | 80.4 | 79.9 | 82.7 | +| All | 82-83 | 72.4 | 73.5 | 73.4 | 76.8 | -Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask +to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. + +## 5 EXPERIMENTS + +The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this + +Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. -paper and leave the detailed evaluation of more recent methods +paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work. -In this section, we will present several aspects related to the +In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]. ## Baselines for Object Detection -In Table 2, we present baseline experiments (given in mAP) on Mask +In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document. -Table 3: Performance of a Mask R-CNN R50 network in +Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. + +| Class-count | 11 | 6 | 5 | 4 | +|----------------|------|---------|---------|---------| +| Caption | 68 | Text | Text | Text | +| Footnote | 71 | Text | Text | Text | +| Formula | 60 | Text | Text | Text | +| List-item | 81 | Text | 82 | Text | +| Page-footer | 62 | 62 | - | - | +| Page-header | 72 | 68 | - | - | +| Picture | 72 | 72 | 72 | 72 | +| Section-header | 68 | 67 | 69 | 68 | +| Table | 82 | 83 | 82 | 82 | +| Text | 85 | 84 | 84 | 84 | +| Title | 77 | Sec.-h. | Sec.-h. | Sec.-h. | +| Overall | 72 | 73 | 78 | 77 | ## Learning Curve -One of the fundamental questions related to any dataset is if it is +One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles. ## Impact of Class Labels -The choice and number of labels can have a significant effect on +The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of -Table 4: Performance of a Mask R-CNN R50 network with +Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in /tildelow 10% point improvement. -lists in PubLayNet (grouped list-items) versus DocLayNet (separate +| Class-count | 11 | 11 | 5 | 5 | +|----------------|------|------|-----|------| +| Split | Doc | Page | Doc | Page | +| Caption | 68 | 83 | | | +| Footnote | 71 | 84 | | | +| Formula | 60 | 66 | | | +| List-item | 81 | 88 | 82 | 88 | +| Page-footer | 62 | 89 | | | +| Page-header | 72 | 90 | | | +| Picture | 72 | 82 | 72 | 82 | +| Section-header | 68 | 83 | 69 | 83 | +| Table | 82 | 89 | 82 | 90 | +| Text | 85 | 91 | 84 | 90 | +| Title | 77 | 81 | | | +| All | 72 | 84 | 78 | 87 | + +lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded. ## Impact of Document Split in Train and Test Set -Many documents in DocLayNet have a unique styling. In order +Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains ˜ 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided. ## Dataset Comparison -Throughout this paper, we claim that DocLayNet's wider variety of +Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , -Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask +Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. -Section-header +| | | Testing on | Testing on | Testing on | +|-----------------|------------|--------------|--------------|--------------| +| Training on | labels | PLN | DB | DLN | +| PubLayNet (PLN) | Figure | 96 | 43 | 23 | +| PubLayNet (PLN) | Sec-header | 87 | - | 32 | +| | Table | 95 | 24 | 49 | +| | Text | 96 | - | 42 | +| | total | 93 | 34 | 30 | +| DocBank (DB) | Figure | 77 | 71 | 31 | +| DocBank (DB) | Table | 19 | 65 | 22 | +| DocBank (DB) | total | 48 | 68 | 27 | +| DocLayNet (DLN) | Figure | 67 | 51 | 72 | +| DocLayNet (DLN) | Sec-header | 53 | - | 68 | +| | Table | 87 | 43 | 82 | +| | Text | 77 | - | 84 | +| | total | 59 | 47 | 78 | -For comparison of DocBank with DocLayNet, we trained only +Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text . + +For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. ## Example Predictions -To conclude this section, we illustrate the quality of layout predic- +To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence. -## 6 +## 6 CONCLUSION -In this paper, we presented the DocLayNet dataset. It provides the +In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. -From the dataset, we have derived on the one hand reference +From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust. -To date, there is still a significant gap between human and ML +To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap. ## REFERENCES -- [1] +- [1] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013. -- [2] +- [2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. 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Journal of Big Data , 6(1):60, 2019. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/2206.01062.pages.json b/tests/data/groundtruth/docling_v1/2206.01062.pages.json index 32d73471..0b06a5cc 100644 --- a/tests/data/groundtruth/docling_v1/2206.01062.pages.json +++ b/tests/data/groundtruth/docling_v1/2206.01062.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.3, "r_y0": 99.49000000000001, "r_x1": 505.062, "r_y1": 99.49000000000001, "r_x2": 505.062, "r_y2": 84.22799999999995, "r_x3": 107.3, "r_y3": 84.22799999999995, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for", "orig": "DocLayNet: A Large Human-Annotated Dataset for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.117, "r_y0": 119.41499999999996, 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With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 293.054, "r_x1": 295.346, "r_y1": 293.054, "r_x2": 295.346, "r_y2": 285.253, "r_x3": 53.798, "r_y3": 285.253, "coord_origin": "TOPLEFT"}, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 304.013, "r_x1": 294.047, "r_y1": 304.013, "r_x2": 294.047, "r_y2": 296.212, "r_x3": 53.798, "r_y3": 296.212, "coord_origin": "TOPLEFT"}, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.972, "r_x1": 294.046, "r_y1": 314.972, "r_x2": 294.046, "r_y2": 307.171, "r_x3": 53.798, "r_y3": 307.171, "coord_origin": "TOPLEFT"}, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, 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not made or distributed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 656.741, "r_x1": 294.045, "r_y1": 656.741, "r_x2": 294.045, "r_y2": 650.673, "r_x3": 53.798, "r_y3": 650.673, "coord_origin": "TOPLEFT"}, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "orig": "for profit or commercial advantage and that copies bear this notice and the full citation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.711, "r_x1": 295.118, "r_y1": 664.711, "r_x2": 295.118, "r_y2": 658.643, "r_x3": 53.798, "r_y3": 658.643, "coord_origin": "TOPLEFT"}, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.681, "r_x1": 187.723, "r_y1": 672.681, "r_x2": 187.723, "r_y2": 666.613, "r_x3": 53.798, "r_y3": 666.613, "coord_origin": "TOPLEFT"}, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 681.481, "r_x1": 197.863, "r_y1": 681.481, "r_x2": 197.863, "r_y2": 675.331, "r_x3": 53.798, "r_y3": 675.331, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.317, "r_y0": 690.215, "r_x1": 186.746, "r_y1": 690.215, "r_x2": 186.746, "r_y2": 684.147, "r_x3": 53.317, "r_y3": 684.147, "coord_origin": "TOPLEFT"}, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.554, "r_y0": 698.186, "r_x1": 157.031, "r_y1": 698.186, "r_x2": 157.031, "r_y2": 692.1179999999999, "r_x3": 53.554, "r_y3": 692.1179999999999, "coord_origin": "TOPLEFT"}, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 706.156, "r_x1": 166.941, "r_y1": 706.156, "r_x2": 166.941, "r_y2": 700.088, "r_x3": 53.798, "r_y3": 700.088, "coord_origin": "TOPLEFT"}, "text": "https://doi.org/10.1145/3534678.3539043", "orig": "https://doi.org/10.1145/3534678.3539043", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.87, "r_y0": 353.853, "r_x1": 330.412, "r_y1": 353.853, "r_x2": 330.412, "r_y2": 352.266, "r_x3": 327.87, "r_y3": 352.266, "coord_origin": "TOPLEFT"}, "text": "13", "orig": "13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 333.231, "r_x1": 351.161, "r_y1": 333.231, "r_x2": 351.161, "r_y2": 331.961, "r_x3": 327.83, "r_y3": 331.961, "coord_origin": "TOPLEFT"}, "text": "USING THE VERTICAL TUBE -", "orig": "USING THE VERTICAL TUBE -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 334.841, "r_x1": 348.305, "r_y1": 334.841, "r_x2": 348.305, "r_y2": 333.571, "r_x3": 327.83, "r_y3": 333.571, "coord_origin": "TOPLEFT"}, "text": "MODELS AY11230/11234", "orig": "MODELS AY11230/11234", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 337.855, "r_x1": 350.743, "r_y1": 337.855, "r_x2": 350.743, "r_y2": 336.744, "r_x3": 327.83, "r_y3": 336.744, "coord_origin": "TOPLEFT"}, "text": "1. 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Make su", "orig": "3. 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The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.632, "r_y0": 278.03499999999997, "r_x1": 353.135, "r_y1": 278.03499999999997, "r_x2": 353.135, "r_y2": 276.765, "r_x3": 352.632, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.633, "r_y0": 292.527, "r_x1": 353.137, "r_y1": 292.527, "r_x2": 353.137, "r_y2": 291.256, "r_x3": 352.633, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 295.747, "r_x1": 327.385, "r_y1": 295.747, "r_x2": 327.385, "r_y2": 294.477, "r_x3": 326.882, "r_y3": 294.477, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.638, "r_y0": 326.341, "r_x1": 353.141, "r_y1": 326.341, "r_x2": 353.141, "r_y2": 325.071, "r_x3": 352.638, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.175, "r_y0": 284.476, "r_x1": 384.678, "r_y1": 284.476, "r_x2": 384.678, "r_y2": 283.205, "r_x3": 384.175, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.176, "r_y0": 287.696, "r_x1": 384.68, "r_y1": 287.696, "r_x2": 384.68, "r_y2": 286.426, "r_x3": 384.176, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.176, "r_y0": 300.578, "r_x1": 384.68, "r_y1": 300.578, "r_x2": 384.68, "r_y2": 299.308, "r_x3": 384.176, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.177, "r_y0": 311.849, "r_x1": 384.681, "r_y1": 311.849, "r_x2": 384.681, "r_y2": 310.579, "r_x3": 384.177, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.177, "r_y0": 329.561, "r_x1": 384.681, "r_y1": 329.561, "r_x2": 384.681, "r_y2": 328.291, "r_x3": 384.177, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, "r_x3": 358.426, "r_y3": 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. 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404.538, "coord_origin": "TOPLEFT"}, "text": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney ", "orig": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 408.577, "r_x1": 369.653, "r_y1": 408.577, "r_x2": 369.653, "r_y2": 406.572, "r_x3": 329.405, "r_y3": 406.572, "coord_origin": "TOPLEFT"}, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 465.415, "r_x1": 379.529, "r_y1": 465.415, "r_x2": 379.529, "r_y2": 460.96, "r_x3": 363.545, "r_y3": 460.96, "coord_origin": "TOPLEFT"}, "text": "Yesterday", "orig": "Yesterday", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 468.914, "r_x1": 391.382, "r_y1": 468.914, "r_x2": 391.382, "r_y2": 466.909, "r_x3": 363.545, "r_y3": 466.909, "coord_origin": "TOPLEFT"}, "text": "Established in Sydney in 1837, and then ", "orig": "Established in Sydney in 1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 470.947, "r_x1": 395.018, "r_y1": 470.947, "r_x2": 395.018, "r_y2": 468.942, "r_x3": 363.545, "r_y3": 468.942, "coord_origin": "TOPLEFT"}, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, 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Australian households. ", "orig": "electricity needs of Australian households. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 479.082, "r_x1": 394.115, "r_y1": 479.082, "r_x2": 394.115, "r_y2": 477.076, "r_x3": 363.545, "r_y3": 477.076, "coord_origin": "TOPLEFT"}, "text": "In 1841, when AGL supplied the gas to light ", "orig": "In 1841, when AGL supplied the gas to light ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 481.115, "r_x1": 393.759, "r_y1": 481.115, "r_x2": 393.759, "r_y2": 479.11, "r_x3": 363.545, "r_y3": 479.11, "coord_origin": "TOPLEFT"}, "text": "the first public street lamp, it was reported ", "orig": "the first public street lamp, it was reported ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 483.149, "r_x1": 390.498, "r_y1": 483.149, "r_x2": 390.498, "r_y2": 481.144, "r_x3": 363.545, "r_y3": 481.144, "coord_origin": "TOPLEFT"}, "text": "in the Sydney Gazette as a 'wonderful ", "orig": "in the Sydney Gazette as a 'wonderful ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 485.182, "r_x1": 395.71, "r_y1": 485.182, "r_x2": 395.71, "r_y2": 483.177, "r_x3": 363.545, "r_y3": 483.177, "coord_origin": "TOPLEFT"}, "text": "achievement of scientific knowledge, assisted ", "orig": "achievement of scientific knowledge, assisted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 487.216, "r_x1": 394.273, "r_y1": 487.216, "r_x2": 394.273, "r_y2": 485.211, "r_x3": 363.545, "r_y3": 485.211, "coord_origin": "TOPLEFT"}, "text": "by mechanical ingenuity.' Within two years, ", "orig": "by mechanical ingenuity.' 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"r_y1": 441.009, "r_x2": 372.166, "r_y2": 431.094, "r_x3": 329.405, "r_y3": 431.094, "coord_origin": "TOPLEFT"}, "text": "175years of ", "orig": "175years of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.994, "r_y0": 441.009, "r_x1": 342.695, "r_y1": 441.009, "r_x2": 342.695, "r_y2": 431.094, "r_x3": 340.994, "r_y3": 431.094, "coord_origin": "TOPLEFT"}, "text": "\u00a0", "orig": "\u00a0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 451.181, "r_x1": 385.754, "r_y1": 451.181, "r_x2": 385.754, "r_y2": 441.267, "r_x3": 329.405, "r_y3": 441.267, "coord_origin": "TOPLEFT"}, "text": "lookingforward.", "orig": "lookingforward.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 497.777, "r_y0": 254.86599999999999, "r_x1": 542.884, "r_y1": 254.86599999999999, "r_x2": 542.884, "r_y2": 251.74699999999996, "r_x3": 497.777, "r_y3": 251.74699999999996, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings", "orig": "signs, signals and road markings", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.307, "r_y0": 254.90700000000004, "r_x1": 492.1, "r_y1": 254.90700000000004, "r_x2": 492.1, "r_y2": 251.788, "r_x3": 490.307, "r_y3": 251.788, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 265.87300000000005, "r_x1": 500.04, "r_y1": 265.87300000000005, "r_x2": 500.04, "r_y2": 264.063, "r_x3": 498.153, "r_y3": 264.063, "coord_origin": "TOPLEFT"}, "text": "In ", "orig": "In ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.056, "r_y0": 265.81600000000003, "r_x1": 524.387, "r_y1": 265.81600000000003, "r_x2": 524.387, "r_y2": 264.034, "r_x3": 500.056, "r_y3": 264.034, "coord_origin": "TOPLEFT"}, "text": "chapter 2, you and your vehicle", "orig": "chapter 2, you and your vehicle", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 524.37, "r_y0": 265.87300000000005, "r_x1": 539.891, "r_y1": 265.87300000000005, "r_x2": 539.891, "r_y2": 264.063, "r_x3": 524.37, "r_y3": 264.063, "coord_origin": "TOPLEFT"}, "text": ", you learned about ", "orig": ", you learned about ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 267.91600000000005, "r_x1": 544.504, "r_y1": 267.91600000000005, "r_x2": 544.504, "r_y2": 266.106, "r_x3": 498.153, "r_y3": 266.106, "coord_origin": "TOPLEFT"}, "text": "some of the controls in your vehicle. This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 95.86500000000001, "r_x1": 156.529, "r_y1": 95.86500000000001, "r_x2": 156.529, "r_y2": 86.798, "r_x3": 70.314, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 130.17399999999998, "r_x1": 295.557, "r_y1": 130.17399999999998, "r_x2": 295.557, "r_y2": 122.37300000000005, "r_x3": 53.529, "r_y3": 122.37300000000005, "coord_origin": "TOPLEFT"}, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 141.13300000000004, "r_x1": 294.046, "r_y1": 141.13300000000004, "r_x2": 294.046, "r_y2": 133.332, "r_x3": 53.798, "r_y3": 133.332, "coord_origin": "TOPLEFT"}, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 152.091, "r_x1": 294.047, "r_y1": 152.091, "r_x2": 294.047, "r_y2": 144.28999999999996, "r_x3": 53.798, "r_y3": 144.28999999999996, "coord_origin": "TOPLEFT"}, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 625.734, "r_x1": 167.917, "r_y1": 625.734, "r_x2": 167.917, "r_y2": 617.826, "r_x3": 78.207, "r_y3": 617.826, "coord_origin": "TOPLEFT"}, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.335, "r_y0": 625.69, "r_x1": 170.493, "r_y1": 625.69, "r_x2": 170.493, "r_y2": 617.889, "r_x3": 168.335, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.981, "r_y0": 625.69, "r_x1": 294.263, "r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 647.6510000000001, "r_x1": 143.517, "r_y1": 647.6510000000001, "r_x2": 143.517, "r_y2": 639.7429999999999, "r_x3": 78.207, "r_y3": 639.7429999999999, "coord_origin": "TOPLEFT"}, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.02, "r_y0": 647.607, "r_x1": 146.178, "r_y1": 647.607, "r_x2": 146.178, "r_y2": 639.806, "r_x3": 144.02, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 117.55700000000002, "r_x1": 487.253, "r_y1": 117.55700000000002, "r_x2": 487.253, "r_y2": 109.649, "r_x3": 342.364, "r_y3": 109.649, "coord_origin": "TOPLEFT"}, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.759, "r_y0": 117.51300000000003, "r_x1": 489.867, "r_y1": 117.51300000000003, "r_x2": 489.867, "r_y2": 109.71199999999999, "r_x3": 487.759, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 542.158, "r_y0": 642.554, "r_x1": 559.099, "r_y1": 642.554, "r_x2": 559.099, "r_y2": 634.646, "r_x3": 542.158, "r_y3": 634.646, "coord_origin": "TOPLEFT"}, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 653.513, "r_x1": 331.863, "r_y1": 653.513, "r_x2": 331.863, "r_y2": 645.605, "r_x3": 317.955, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.863, "r_y0": 653.469, "r_x1": 333.84, "r_y1": 653.469, "r_x2": 333.84, "r_y2": 645.668, "r_x3": 331.863, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.064, "r_y0": 653.513, "r_x1": 366.054, "r_y1": 653.513, "r_x2": 366.054, "r_y2": 645.605, "r_x3": 336.064, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "Footnote", "orig": "Footnote", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.053, "r_y0": 653.469, "r_x1": 368.03, "r_y1": 653.469, "r_x2": 368.03, "r_y2": 645.668, "r_x3": 366.053, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.254, "r_y0": 653.513, "r_x1": 436.195, "r_y1": 653.513, "r_x2": 436.195, "r_y2": 645.605, "r_x3": 370.254, 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property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.3050000000001, "r_x1": 558.199, "r_y1": 697.3050000000001, "r_x2": 558.199, "r_y2": 689.504, "r_x3": 317.955, "r_y3": 689.504, "coord_origin": "TOPLEFT"}, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.264, "r_x1": 558.204, "r_y1": 708.264, "r_x2": 558.204, "r_y2": 700.463, "r_x3": 317.955, "r_y3": 700.463, "coord_origin": "TOPLEFT"}, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 246.244, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 59.405, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.71199999999999, "r": 338.378, "b": 117.51300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 111.41399999999999, "r": 303.017, "b": 119.21500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 327.918, "t": 168.40599999999995, "r": 559.19, "b": 176.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 223.20100000000002, "r": 558.198, "b": 231.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 63.761, "t": 231.962, "r": 295.564, "b": 239.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 323.562, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.713, "b": 354.564, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 467.311, "r": 558.198, "b": 475.112, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 505.934, "r": 295.564, "b": 513.735, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 323.562, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 585.012, "r": 74.221, "b": 592.813, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 558.202, "b": 598.675, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.889, "r": 74.221, "b": 625.69, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.806, "r": 74.221, "b": 647.607, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.683, "r": 74.221, "b": 680.484, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 327.918, "t": 678.545, "r": 558.198, "b": 686.346, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 56.22, "b": 705.664, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 246.244, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 59.405, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This enables experimentation with annotation uncertainty"}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.71199999999999, "r": 338.378, "b": 117.51300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5)"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 111.41399999999999, "r": 303.017, "b": 119.21500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning"}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 327.918, "t": 168.40599999999995, "r": 559.19, "b": 176.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,"}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 223.20100000000002, "r": 558.198, "b": 231.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a"}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 63.761, "t": 231.962, "r": 295.564, "b": 239.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Akeyproblem in the process of document conversion is to under-"}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 323.562, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.713, "b": 354.564, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While early approaches in document-layout analysis used rule-"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 467.311, "r": 558.198, "b": 475.112, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis"}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 505.934, "r": 295.564, "b": 513.735, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides page-"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 323.562, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 585.012, "r": 74.221, "b": 592.813, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1)"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 558.202, "b": 598.675, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two"}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.889, "r": 74.221, "b": 625.69, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2)"}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.806, "r": 74.221, "b": 647.607, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, 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0.9749757051467896, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In addition to open intellectual property constraints for the"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 56.22, "b": 705.664, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 59.405, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This enables experimentation with annotation uncertainty"}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.71199999999999, "r": 338.378, "b": 117.51300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5)"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 111.41399999999999, "r": 303.017, "b": 119.21500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning"}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 327.918, "t": 168.40599999999995, "r": 559.19, "b": 176.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,"}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 223.20100000000002, "r": 558.198, "b": 231.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a"}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 63.761, "t": 231.962, "r": 295.564, "b": 239.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Akeyproblem in the process of document conversion is to under-"}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 323.562, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.713, "b": 354.564, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While early approaches in document-layout analysis used rule-"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 467.311, "r": 558.198, "b": 475.112, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis"}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 505.934, "r": 295.564, "b": 513.735, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. 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Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two"}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.889, "r": 74.221, "b": 625.69, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2)"}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.806, "r": 74.221, "b": 647.607, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, 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As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 311.953, "r_x1": 98.881, "r_y1": 311.953, "r_x2": 98.881, "r_y2": 304.152, "r_x3": 53.798, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.071, "r_y0": 310.088, "r_x1": 104.774, "r_y1": 310.088, "r_x2": 104.774, "r_y2": 306.08, "r_x3": 99.071, "r_y3": 306.08, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.464, "r_y0": 311.953, "r_x1": 294.262, "r_y1": 311.953, "r_x2": 294.262, "r_y2": 304.152, "r_x3": 107.464, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "10 pages) with technical content, dense in complex", "orig": "10 pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 322.912, "r_x1": 294.047, "r_y1": 322.912, "r_x2": 294.047, "r_y2": 315.111, "r_x3": 53.798, "r_y3": 315.111, "coord_origin": "TOPLEFT"}, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 388.665, "r_x1": 105.905, "r_y1": 388.665, "r_x2": 105.905, "r_y2": 380.864, "r_x3": 53.798, "r_y3": 380.864, "coord_origin": "TOPLEFT"}, "text": "gories, namely", "orig": "gories, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.754, "r_y0": 388.709, "r_x1": 167.497, "r_y1": 388.709, "r_x2": 167.497, "r_y2": 380.801, "r_x3": 107.754, "r_y3": 380.801, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.496, "r_y0": 388.665, "r_x1": 169.429, "r_y1": 388.665, "r_x2": 169.429, "r_y2": 380.864, "r_x3": 167.496, "r_y3": 380.864, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.281, "r_y0": 388.709, "r_x1": 264.553, "r_y1": 388.709, "r_x2": 264.553, "r_y2": 380.801, "r_x3": 171.281, "r_y3": 380.801, "coord_origin": "TOPLEFT"}, "text": "Manuals Scientific Articles", "orig": "Manuals Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.455, "r_y0": 388.665, "r_x1": 203.388, 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"r_y1": 399.668, "r_x2": 94.9, "r_y2": 391.76, "r_x3": 53.798, "r_y3": 391.76, "coord_origin": "TOPLEFT"}, "text": "Regulations", "orig": "Regulations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 94.9, "r_y0": 399.624, "r_x1": 96.863, "r_y1": 399.624, "r_x2": 96.863, "r_y2": 391.823, "r_x3": 94.9, "r_y3": 391.823, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.109, "r_y0": 399.668, "r_x1": 124.723, "r_y1": 399.668, "r_x2": 124.723, "r_y2": 391.76, "r_x3": 99.109, "r_y3": 391.76, "coord_origin": "TOPLEFT"}, "text": "Patents", "orig": "Patents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.179, "r_y0": 399.624, "r_x1": 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For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. In phase four,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 87.79399999999998, "r": 558.201, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 327.918, "t": 164.50599999999997, "r": 558.438, "b": 172.30700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 236.88599999999997, "r": 294.044, "b": 244.33900000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 244.33900000000006, "r_x1": 294.044, "r_y1": 244.33900000000006, "r_x2": 294.044, "r_y2": 236.88599999999997, "r_x3": 53.798, "r_y3": 236.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 327.918, "t": 274.095, "r": 559.185, "b": 281.896, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 282.234, "r": 294.046, "b": 290.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 63.761, "t": 369.905, "r": 295.559, "b": 377.706, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 63.761, "t": 512.371, "r": 295.559, "b": 520.172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 323.562, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 63.761, "t": 611.001, "r": 295.564, "b": 618.802, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 558.201, "b": 640.205, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 56.346, "b": 705.094, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 87.79399999999998, "r": 558.201, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels"}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 327.918, "t": 164.50599999999997, "r": 558.438, "b": 172.30700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In order to accommodate the different types of models currently"}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 236.88599999999997, "r": 294.044, "b": 244.33900000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 244.33900000000006, "r_x1": 294.044, "r_y1": 244.33900000000006, "r_x2": 294.044, "r_y2": 236.88599999999997, "r_x3": 53.798, "r_y3": 236.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document"}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 327.918, "t": 274.095, "r": 559.185, "b": 281.896, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation,"}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 282.234, "r": 294.046, "b": 290.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 63.761, "t": 369.905, "r": 295.559, "b": 377.706, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct cate-"}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 63.761, "t": 512.371, "r": 295.559, "b": 520.172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We did not control the document selection with regard to lan-"}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 323.562, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 63.761, "t": 611.001, "r": 295.564, "b": 618.802, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analy-"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 558.201, "b": 640.205, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 56.346, "b": 705.094, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 87.79399999999998, "r": 558.201, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels"}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 327.918, "t": 164.50599999999997, "r": 558.438, "b": 172.30700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In order to accommodate the different types of models currently"}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 236.88599999999997, "r": 294.044, "b": 244.33900000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 244.33900000000006, "r_x1": 294.044, "r_y1": 244.33900000000006, "r_x2": 294.044, "r_y2": 236.88599999999997, "r_x3": 53.798, "r_y3": 236.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document"}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 327.918, "t": 274.095, "r": 559.185, "b": 281.896, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation,"}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 282.234, "r": 294.046, "b": 290.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 63.761, "t": 369.905, "r": 295.559, "b": 377.706, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct cate-"}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 63.761, "t": 512.371, "r": 295.559, "b": 520.172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We did not control the document selection with regard to lan-"}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 323.562, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 63.761, "t": 611.001, "r": 295.564, "b": 618.802, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analy-"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 558.201, "b": 640.205, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 56.346, "b": 705.094, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. 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"coord_origin": "TOPLEFT"}, "text": "71-76", "orig": "71-76", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.47, "r_y0": 291.718, "r_x1": 507.178, "r_y1": 291.718, "r_x2": 507.178, "r_y2": 283.917, "r_x3": 487.47, "r_y3": 283.917, "coord_origin": "TOPLEFT"}, "text": "68-85", "orig": "68-85", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 573.193, "r_x1": 295.649, "r_y1": 573.193, "r_x2": 295.649, "r_y2": 565.74, "r_x3": 53.798, "r_y3": 565.74, "coord_origin": "TOPLEFT"}, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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317.955, "r_y3": 575.523, "coord_origin": "TOPLEFT"}, "text": "Critical factors that were considered for the choice of these class", "orig": "Critical factors that were considered for the choice of these class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 594.283, "r_x1": 558.431, "r_y1": 594.283, "r_x2": 558.431, "r_y2": 586.482, "r_x3": 317.955, "r_y3": 586.482, "coord_origin": "TOPLEFT"}, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 605.242, "r_x1": 558.369, "r_y1": 605.242, "r_x2": 558.369, "r_y2": 597.441, "r_x3": 317.955, "r_y3": 597.441, "coord_origin": "TOPLEFT"}, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 660.037, "r_x1": 436.906, "r_y1": 660.037, "r_x2": 436.906, "r_y2": 652.236, "r_x3": 317.955, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.138, "r_y0": 660.081, "r_x1": 469.691, "r_y1": 660.081, "r_x2": 469.691, "r_y2": 652.173, "r_x3": 439.138, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.429, "r_y0": 660.037, "r_x1": 493.973, "r_y1": 660.037, "r_x2": 493.973, "r_y2": 652.236, "r_x3": 472.429, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 496.207, "r_y0": 660.081, "r_x1": 558.2, "r_y1": 660.081, "r_x2": 558.2, "r_y2": 652.173, "r_x3": 496.207, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 670.996, "r_x1": 558.206, "r_y1": 670.996, "r_x2": 558.206, "r_y2": 663.1949999999999, "r_x3": 317.955, "r_y3": 663.1949999999999, "coord_origin": "TOPLEFT"}, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.954, "r_x1": 447.652, "r_y1": 681.954, "r_x2": 447.652, "r_y2": 674.153, "r_x3": 317.955, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.86, "r_y0": 681.998, "r_x1": 474.314, "r_y1": 681.998, "r_x2": 474.314, "r_y2": 674.09, "r_x3": 449.86, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.172, "r_y0": 681.954, "r_x1": 490.397, "r_y1": 681.954, "r_x2": 490.397, "r_y2": 674.153, "r_x3": 477.172, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.606, "r_y0": 681.998, "r_x1": 528.299, "r_y1": 681.998, "r_x2": 528.299, "r_y2": 674.09, "r_x3": 492.606, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.299, "r_y0": 681.954, "r_x1": 530.232, "r_y1": 681.954, "r_x2": 530.232, "r_y2": 674.153, "r_x3": 528.299, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.438, "r_y0": 681.954, "r_x1": 558.201, "r_y1": 681.954, "r_x2": 558.201, "r_y2": 674.153, "r_x3": 532.438, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "as seen", "orig": "as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 692.913, "r_x1": 558.204, "r_y1": 692.913, "r_x2": 558.204, "r_y2": 685.112, "r_x3": 317.955, "r_y3": 685.112, "coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 246.244, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.502, "t": 85.726, "r": 558.49, "b": 93.17899999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 312.51, "r": 481.034, "b": 320.311, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 378.263, "r": 558.206, "b": 386.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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Along with the frequency of each class label, we present the relative occurrence (as %"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 312.51, "r": 481.034, "b": 320.311, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "include publication repositories such as arXiv"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 378.263, "r": 558.206, "b": 386.064, "coord_origin": "TOPLEFT"}, "confidence": 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"coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 2: Label selection and guideline."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 554.781, "r": 295.649, "b": 562.234, "coord_origin": "TOPLEFT"}, "confidence": 0.810490071773529, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user inter-"}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 642.523, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "we distributed the annotation workload and performed continuous"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 678.782, "r": 226.725, "b": 686.235, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": 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"coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 2: Label selection and guideline."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 554.781, "r": 295.649, "b": 562.234, "coord_origin": "TOPLEFT"}, "confidence": 0.810490071773529, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user inter-"}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 642.523, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "we distributed the annotation workload and performed continuous"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 678.782, "r": 226.725, "b": 686.235, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": 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USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. 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list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 389.621, "r_x1": 91.594, "r_y1": 389.621, "r_x2": 91.594, "r_y2": 381.82, "r_x3": 78.207, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.835, "r_y0": 389.665, "r_x1": 106.881, "r_y1": 389.665, "r_x2": 106.881, "r_y2": 381.757, "r_x3": 93.835, "r_y3": 381.757, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 109.629, "r_y0": 389.621, "r_x1": 133.722, "r_y1": 389.621, "r_x2": 133.722, "r_y2": 381.82, "r_x3": 109.629, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 400.58, "r_x1": 74.221, "r_y1": 400.58, "r_x2": 74.221, "r_y2": 392.779, "r_x3": 64.708, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 400.58, "r_x1": 84.351, "r_y1": 400.58, "r_x2": 84.351, "r_y2": 392.779, "r_x3": 78.207, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 86.584, "r_y0": 400.624, "r_x1": 118.013, "r_y1": 400.624, "r_x2": 118.013, "r_y2": 392.716, "r_x3": 86.584, "r_y3": 392.716, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.269, "r_y0": 400.58, "r_x1": 295.557, "r_y1": 400.58, "r_x2": 295.557, "r_y2": 392.779, "r_x3": 120.269, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 433.457, "r_x1": 192.009, "r_y1": 433.457, "r_x2": 192.009, "r_y2": 425.656, "r_x3": 78.207, "r_y3": 425.656, "coord_origin": "TOPLEFT"}, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 444.416, "r_x1": 74.221, "r_y1": 444.416, "r_x2": 74.221, "r_y2": 436.615, "r_x3": 64.708, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 444.416, "r_x1": 112.616, "r_y1": 444.416, "r_x2": 112.616, "r_y2": 436.615, "r_x3": 78.207, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 114.861, "r_y0": 444.46, "r_x1": 142.612, "r_y1": 444.46, "r_x2": 142.612, "r_y2": 436.552, "r_x3": 114.861, "r_y3": 436.552, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.612, "r_y0": 444.416, "r_x1": 144.557, "r_y1": 444.416, "r_x2": 144.557, "r_y2": 436.615, "r_x3": 142.612, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.794, "r_y0": 444.416, "r_x1": 294.047, "r_y1": 444.416, "r_x2": 294.047, "r_y2": 436.615, "r_x3": 146.794, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "there must be exactly one corresponding", "orig": "there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 455.419, "r_x1": 102.793, "r_y1": 455.419, "r_x2": 102.793, "r_y2": 447.511, "r_x3": 78.207, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 105.245, "r_y0": 455.375, "r_x1": 113.1, "r_y1": 455.375, "r_x2": 113.1, "r_y2": 447.574, "r_x3": 105.245, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.341, 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1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 488.251, "r_x1": 74.221, "r_y1": 488.251, "r_x2": 74.221, "r_y2": 480.45, "r_x3": 64.708, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.202, "r_y0": 488.251, "r_x1": 206.135, "r_y1": 488.251, "r_x2": 206.135, "r_y2": 480.45, "r_x3": 78.202, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.38, "r_y0": 488.295, "r_x1": 238.122, "r_y1": 488.295, "r_x2": 238.122, "r_y2": 480.387, "r_x3": 208.38, "r_y3": 480.387, "coord_origin": "TOPLEFT"}, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.413, "r_y0": 488.251, "r_x1": 264.506, "r_y1": 488.251, "r_x2": 264.506, "r_y2": 480.45, "r_x3": 240.413, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 499.21, "r_x1": 294.046, "r_y1": 499.21, "r_x2": 294.046, "r_y2": 491.409, "r_x3": 78.207, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 510.169, "r_x1": 200.348, "r_y1": 510.169, "r_x2": 200.348, "r_y2": 502.368, "r_x3": 78.207, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.667, "r_y0": 510.213, "r_x1": 256.575, "r_y1": 510.213, "r_x2": 256.575, "r_y2": 502.305, "r_x3": 203.667, "r_y3": 502.305, "coord_origin": "TOPLEFT"}, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 256.574, "r_y0": 510.169, "r_x1": 258.586, "r_y1": 510.169, "r_x2": 258.586, "r_y2": 502.368, "r_x3": 256.574, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.906, "r_y0": 510.169, "r_x1": 294.044, "r_y1": 510.169, "r_x2": 294.044, "r_y2": 502.368, "r_x3": 261.906, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "unless it", "orig": "unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 521.1279999999999, "r_x1": 208.13, "r_y1": 521.1279999999999, "r_x2": 208.13, "r_y2": 513.327, "r_x3": 78.207, "r_y3": 513.327, "coord_origin": "TOPLEFT"}, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.14, "r_x1": 295.563, "r_y1": 552.14, "r_x2": 295.563, "r_y2": 544.3389999999999, "r_x3": 53.798, "r_y3": 544.3389999999999, "coord_origin": "TOPLEFT"}, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 181.83299999999997, "r_x1": 416.179, "r_y1": 181.83299999999997, "r_x2": 416.179, "r_y2": 180.015, "r_x3": 340.002, "r_y3": 180.015, "coord_origin": "TOPLEFT"}, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.383, "r_y0": 92.00300000000004, "r_x1": 417.395, "r_y1": 92.00300000000004, "r_x2": 417.395, "r_y2": 86.39099999999996, "r_x3": 339.383, "r_y3": 86.39099999999996, "coord_origin": "TOPLEFT"}, "text": "Compliant with guidelines", "orig": "Compliant with guidelines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.428, "r_y0": 92.00300000000004, "r_x1": 545.834, "r_y1": 92.00300000000004, "r_x2": 545.834, "r_y2": 86.39099999999996, "r_x3": 451.428, "r_y3": 86.39099999999996, "coord_origin": "TOPLEFT"}, "text": "Plausible but invalid alternative", "orig": "Plausible but invalid alternative", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.337, "r_y0": 371.669, "r_x1": 513.216, "r_y1": 371.669, "r_x2": 513.216, "r_y2": 366.057, "r_x3": 350.337, "r_y3": 366.057, "coord_origin": "TOPLEFT"}, "text": "Borderline case: Two guideline-compliant alternatives", "orig": "Borderline case: Two guideline-compliant alternatives", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 247.11400000000003, "r_x1": 416.179, 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"from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 322.194, "r_y0": 259.21900000000005, "r_x1": 326.015, "r_y1": 259.21900000000005, "r_x2": 326.015, "r_y2": 254.54200000000003, "r_x3": 322.194, "r_y3": 254.54200000000003, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 322.194, "r_y0": 372.762, "r_x1": 326.015, "r_y1": 372.762, "r_x2": 326.015, "r_y2": 368.085, "r_x3": 322.194, "r_y3": 368.085, "coord_origin": "TOPLEFT"}, "text": "D", "orig": "D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 481.718, "r_x1": 558.389, "r_y1": 481.718, "r_x2": 558.389, "r_y2": 474.265, "r_x3": 317.955, "r_y3": 474.265, "coord_origin": "TOPLEFT"}, "text": "Figure 4: Examples of plausible annotation alternatives for", "orig": "Figure 4: Examples of plausible annotation alternatives for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 492.677, "r_x1": 559.806, "r_y1": 492.677, "r_x2": 559.806, "r_y2": 485.224, "r_x3": 317.955, "r_y3": 485.224, "coord_origin": "TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 53.798, "t": 87.79399999999998, "r": 294.045, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 63.761, "t": 109.71199999999999, "r": 294.043, "b": 117.51300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": 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"confidence": 0.9544308185577393, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.623, "t": 525.928, "r": 558.204, "b": 533.729, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.529, "t": 533.381, "r": 294.043, "b": 541.182, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 327.918, "t": 548.0699999999999, "r": 456.801, "b": 555.523, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 63.761, "t": 577.44, "r": 136.774, "b": 584.893, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 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"DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.798, "t": 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Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 401.077, "r_x1": 140.362, "r_y1": 401.077, "r_x2": 140.362, "r_y2": 393.276, "r_x3": 53.798, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.99, "r_y0": 401.121, "r_x1": 175.969, "r_y1": 401.121, "r_x2": 175.969, "r_y2": 393.213, "r_x3": 142.99, "r_y3": 393.213, "coord_origin": "TOPLEFT"}, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.951, "r_y0": 401.077, "r_x1": 294.041, "r_y1": 401.077, "r_x2": 294.041, "r_y2": 393.276, "r_x3": 178.951, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 412.036, "r_x1": 295.425, "r_y1": 412.036, "r_x2": 295.425, "r_y2": 404.235, "r_x3": 53.798, "r_y3": 404.235, "coord_origin": "TOPLEFT"}, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 422.995, "r_x1": 294.043, "r_y1": 422.995, "r_x2": 294.043, "r_y2": 415.194, "r_x3": 53.529, "r_y3": 415.194, "coord_origin": "TOPLEFT"}, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 433.954, "r_x1": 294.048, "r_y1": 433.954, "r_x2": 294.048, "r_y2": 426.153, "r_x3": 53.798, "r_y3": 426.153, "coord_origin": "TOPLEFT"}, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 444.913, "r_x1": 256.806, "r_y1": 444.913, "r_x2": 256.806, "r_y2": 437.112, "r_x3": 53.798, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": 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"from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.877, "r_y0": 455.872, "r_x1": 80.889, "r_y1": 455.872, "r_x2": 80.889, "r_y2": 448.071, "r_x3": 78.877, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.139, "r_y0": 455.872, "r_x1": 294.049, "r_y1": 455.872, "r_x2": 294.049, "r_y2": 448.071, "r_x3": 83.139, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": "For the latter, we instructed annotation staff to minimise", "orig": "For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 466.831, "r_x1": 294.046, "r_y1": 466.831, "r_x2": 294.046, "r_y2": 459.03, "r_x3": 53.798, "r_y3": 459.03, "coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 385.937, "r_y0": 252.75400000000002, "r_x1": 392.569, "r_y1": 252.75400000000002, "r_x2": 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"TOPLEFT"}, "text": "ing fractions of the DocLayNet dataset. The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, 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"r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", 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PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 372.512, "r_x1": 558.2, "r_y1": 372.512, "r_x2": 558.2, "r_y2": 364.711, "r_x3": 317.686, "r_y3": 364.711, "coord_origin": "TOPLEFT"}, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 383.47, "r_x1": 558.369, "r_y1": 383.47, "r_x2": 558.369, "r_y2": 375.669, "r_x3": 317.955, "r_y3": 375.669, "coord_origin": "TOPLEFT"}, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 394.429, "r_x1": 558.2, "r_y1": 394.429, "r_x2": 558.2, "r_y2": 386.628, "r_x3": 317.955, "r_y3": 386.628, "coord_origin": "TOPLEFT"}, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.488, "r_x1": 531.077, "r_y1": 708.488, "r_x2": 531.077, "r_y2": 700.687, "r_x3": 317.955, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 533.162, "r_y0": 708.532, "r_x1": 557.256, "r_y1": 708.532, "r_x2": 557.256, "r_y2": 700.624, "r_x3": 533.162, "r_y3": 700.624, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 557.255, "r_y0": 708.488, "r_x1": 559.188, "r_y1": 708.488, "r_x2": 559.188, "r_y2": 700.687, "r_x3": 557.255, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": 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"Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 331.834, "r": 558.202, "b": 339.635, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 335.202, "r": 294.042, "b": 343.003, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, 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[]}, {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 445.027, "r": 558.369, "b": 452.828, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.529, "t": 558.142, "r": 294.043, "b": 565.943, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 558.206, "b": 631.775, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 352.97747802734375, "t": 150.791259765625, "r": 522.9158935546875, "b": 306.2658386230469, "coord_origin": "TOPLEFT"}, "confidence": 0.9879696369171143, "cells": [], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 80.35525512695312, "t": 150.93629455566406, "r": 267.0082092285156, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [], "children": []}]}, 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"figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 294.044, "b": 95.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 558.201, "b": 95.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with"}, {"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 320.508, "r": 131.056, "b": 329.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9555686116218567, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 329.575, "r_x1": 131.056, "r_y1": 329.575, "r_x2": 131.056, "r_y2": 320.508, "r_x3": 53.798, "r_y3": 320.508, "coord_origin": "TOPLEFT"}, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 331.834, "r": 558.202, "b": 339.635, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate"}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 335.202, "r": 294.042, "b": 343.003, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 445.027, "r": 558.369, "b": 452.828, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order"}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.529, "t": 558.142, "r": 294.043, "b": 565.943, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The choice and number of labels can have a significant effect on"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 558.206, "b": 631.775, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of"}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 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"b": 95.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 558.201, "b": 95.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 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"children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 331.834, "r": 558.202, "b": 339.635, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate"}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 335.202, "r": 294.042, "b": 343.003, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is"}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 430.333, "r": 549.861, "b": 439.4, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 445.027, "r": 558.369, "b": 452.828, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order"}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.529, "t": 558.142, "r": 294.043, "b": 565.943, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The choice and number of labels can have a significant effect on"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", 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60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": 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DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.322, "r_y1": 95.09699999999998, "r_x2": 294.322, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 295.649, "r_y1": 106.05600000000004, "r_x2": 295.649, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "orig": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "cLayNet data-sets. By evaluating on common label classes of", "orig": "cLayNet data-sets. By evaluating on common label classes of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "each dataset, we observe that the DocLayNet-trained model", "orig": "each dataset, we observe that the DocLayNet-trained model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 294.044, "r_y1": 138.933, "r_x2": 294.044, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "has much less pronounced variations in performance across", "orig": "has much less pronounced variations in performance across", 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"orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.448, "r_y0": 443.032, "r_x1": 123.305, "r_y1": 443.032, "r_x2": 123.305, "r_y2": 435.124, "r_x3": 108.448, "r_y3": 435.124, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.305, "r_y0": 442.988, "r_x1": 125.278, "r_y1": 442.988, "r_x2": 125.278, "r_y2": 435.187, "r_x3": 123.305, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 453.946, "r_x1": 294.276, "r_y1": 453.946, "r_x2": 294.276, "r_y2": 446.145, "r_x3": 63.761, "r_y3": 446.145, "coord_origin": "TOPLEFT"}, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 464.905, "r_x1": 62.989, "r_y1": 464.905, "r_x2": 62.989, "r_y2": 457.104, "r_x3": 53.798, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.853, "r_y0": 464.949, "r_x1": 88.947, "r_y1": 464.949, "r_x2": 88.947, "r_y2": 457.041, "r_x3": 64.853, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.02, "r_y0": 464.905, "r_x1": 104.245, "r_y1": 464.905, "r_x2": 104.245, "r_y2": 457.104, "r_x3": 91.02, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.108, "r_y0": 464.949, "r_x1": 124.781, "r_y1": 464.949, "r_x2": 124.781, "r_y2": 457.041, "r_x3": 106.108, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.855, "r_y0": 464.905, "r_x1": 277.632, "r_y1": 464.905, "r_x2": 277.632, "r_y2": 457.104, "r_x3": 126.855, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 295.595, "r_x1": 558.435, "r_y1": 295.595, "r_x2": 558.435, "r_y2": 287.794, "r_x3": 327.918, "r_y3": 287.794, "coord_origin": "TOPLEFT"}, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 306.554, "r_x1": 558.2, "r_y1": 306.554, "r_x2": 558.2, "r_y2": 298.753, "r_x3": 317.955, "r_y3": 298.753, "coord_origin": "TOPLEFT"}, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 371.078, "r_x1": 365.596, "r_y1": 371.078, "r_x2": 365.596, "r_y2": 364.928, "r_x3": 333.391, "r_y3": 364.928, "coord_origin": "TOPLEFT"}, "text": "Recognition", "orig": "Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.596, "r_y0": 371.045, "r_x1": 367.13, "r_y1": 371.045, "r_x2": 367.13, "r_y2": 364.977, "r_x3": 365.596, "r_y3": 364.977, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.874, "r_y0": 371.045, "r_x1": 434.295, "r_y1": 371.045, "r_x2": 434.295, "r_y2": 364.977, "r_x3": 368.874, "r_y3": 364.977, "coord_origin": "TOPLEFT"}, "text": "pages 1449-1453, 2013.", "orig": "pages 1449-1453, 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"Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 558.204, "b": 109.293, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. 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"coord_origin": "TOPLEFT"}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 558.204, "b": 109.293, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. 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While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 512.232, "r_x1": 295.556, "r_y1": 512.232, "r_x2": 295.556, "r_y2": 504.431, "r_x3": 53.798, "r_y3": 504.431, "coord_origin": "TOPLEFT"}, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 523.191, "r_x1": 294.216, "r_y1": 523.191, "r_x2": 294.216, "r_y2": 515.39, "r_x3": 53.798, "r_y3": 515.39, "coord_origin": "TOPLEFT"}, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 534.15, "r_x1": 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not made or distributed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 656.741, "r_x1": 294.045, "r_y1": 656.741, "r_x2": 294.045, "r_y2": 650.673, "r_x3": 53.798, "r_y3": 650.673, "coord_origin": "TOPLEFT"}, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "orig": "for profit or commercial advantage and that copies bear this notice and the full citation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.711, "r_x1": 295.118, "r_y1": 664.711, "r_x2": 295.118, "r_y2": 658.643, "r_x3": 53.798, "r_y3": 658.643, "coord_origin": "TOPLEFT"}, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.681, "r_x1": 187.723, "r_y1": 672.681, "r_x2": 187.723, "r_y2": 666.613, "r_x3": 53.798, "r_y3": 666.613, "coord_origin": "TOPLEFT"}, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 681.481, "r_x1": 197.863, "r_y1": 681.481, "r_x2": 197.863, "r_y2": 675.331, "r_x3": 53.798, "r_y3": 675.331, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.317, "r_y0": 690.215, "r_x1": 186.746, "r_y1": 690.215, "r_x2": 186.746, "r_y2": 684.147, "r_x3": 53.317, "r_y3": 684.147, "coord_origin": "TOPLEFT"}, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.554, "r_y0": 698.186, "r_x1": 157.031, "r_y1": 698.186, "r_x2": 157.031, "r_y2": 692.1179999999999, "r_x3": 53.554, "r_y3": 692.1179999999999, "coord_origin": "TOPLEFT"}, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 706.156, "r_x1": 166.941, "r_y1": 706.156, "r_x2": 166.941, "r_y2": 700.088, "r_x3": 53.798, "r_y3": 700.088, "coord_origin": "TOPLEFT"}, "text": "https://doi.org/10.1145/3534678.3539043", "orig": "https://doi.org/10.1145/3534678.3539043", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.87, "r_y0": 353.853, "r_x1": 330.412, "r_y1": 353.853, "r_x2": 330.412, "r_y2": 352.266, "r_x3": 327.87, "r_y3": 352.266, "coord_origin": "TOPLEFT"}, "text": "13", "orig": "13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 333.231, "r_x1": 351.161, "r_y1": 333.231, "r_x2": 351.161, "r_y2": 331.961, "r_x3": 327.83, "r_y3": 331.961, "coord_origin": "TOPLEFT"}, "text": "USING THE VERTICAL TUBE -", "orig": "USING THE VERTICAL TUBE -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 334.841, "r_x1": 348.305, "r_y1": 334.841, "r_x2": 348.305, "r_y2": 333.571, "r_x3": 327.83, "r_y3": 333.571, "coord_origin": "TOPLEFT"}, "text": "MODELS AY11230/11234", "orig": "MODELS AY11230/11234", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 337.855, "r_x1": 350.743, "r_y1": 337.855, "r_x2": 350.743, "r_y2": 336.744, "r_x3": 327.83, "r_y3": 336.744, "coord_origin": "TOPLEFT"}, "text": "1. 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Make su", "orig": "3. 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In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.632, "r_y0": 278.03499999999997, "r_x1": 353.135, "r_y1": 278.03499999999997, "r_x2": 353.135, "r_y2": 276.765, "r_x3": 352.632, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.633, "r_y0": 292.527, "r_x1": 353.137, "r_y1": 292.527, "r_x2": 353.137, "r_y2": 291.256, "r_x3": 352.633, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 295.747, "r_x1": 327.385, "r_y1": 295.747, "r_x2": 327.385, "r_y2": 294.477, "r_x3": 326.882, "r_y3": 294.477, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.638, "r_y0": 326.341, "r_x1": 353.141, "r_y1": 326.341, "r_x2": 353.141, "r_y2": 325.071, "r_x3": 352.638, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.175, "r_y0": 284.476, "r_x1": 384.678, "r_y1": 284.476, "r_x2": 384.678, "r_y2": 283.205, "r_x3": 384.175, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.176, "r_y0": 287.696, "r_x1": 384.68, "r_y1": 287.696, "r_x2": 384.68, "r_y2": 286.426, "r_x3": 384.176, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.176, "r_y0": 300.578, "r_x1": 384.68, "r_y1": 300.578, "r_x2": 384.68, "r_y2": 299.308, "r_x3": 384.176, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.177, "r_y0": 311.849, "r_x1": 384.681, "r_y1": 311.849, "r_x2": 384.681, "r_y2": 310.579, "r_x3": 384.177, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.177, "r_y0": 329.561, "r_x1": 384.681, "r_y1": 329.561, "r_x2": 384.681, "r_y2": 328.291, "r_x3": 384.177, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. 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"Yesterday", "orig": "Yesterday", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 323, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 468.914, "r_x1": 391.382, "r_y1": 468.914, "r_x2": 391.382, "r_y2": 466.909, "r_x3": 363.545, "r_y3": 466.909, "coord_origin": "TOPLEFT"}, "text": "Established in Sydney in 1837, and then ", "orig": "Established in Sydney in 1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 324, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 470.947, "r_x1": 395.018, "r_y1": 470.947, "r_x2": 395.018, "r_y2": 468.942, "r_x3": 363.545, "r_y3": 468.942, "coord_origin": "TOPLEFT"}, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 325, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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"electricity needs of Australian households. ", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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"r_y1": 441.009, "r_x2": 372.166, "r_y2": 431.094, "r_x3": 329.405, "r_y3": 431.094, "coord_origin": "TOPLEFT"}, "text": "175years of ", "orig": "175years of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 336, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.994, "r_y0": 441.009, "r_x1": 342.695, "r_y1": 441.009, "r_x2": 342.695, "r_y2": 431.094, "r_x3": 340.994, "r_y3": 431.094, "coord_origin": "TOPLEFT"}, "text": "\u00a0", "orig": "\u00a0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 337, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 451.181, "r_x1": 385.754, "r_y1": 451.181, "r_x2": 385.754, "r_y2": 441.267, "r_x3": 329.405, "r_y3": 441.267, "coord_origin": "TOPLEFT"}, "text": "lookingforward.", "orig": "lookingforward.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 338, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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"from_ocr": false}, {"index": 341, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 497.777, "r_y0": 254.86599999999999, "r_x1": 542.884, "r_y1": 254.86599999999999, "r_x2": 542.884, "r_y2": 251.74699999999996, "r_x3": 497.777, "r_y3": 251.74699999999996, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings", "orig": "signs, signals and road markings", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 342, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.307, "r_y0": 254.90700000000004, "r_x1": 492.1, "r_y1": 254.90700000000004, "r_x2": 492.1, "r_y2": 251.788, "r_x3": 490.307, "r_y3": 251.788, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 343, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 265.87300000000005, "r_x1": 500.04, "r_y1": 265.87300000000005, "r_x2": 500.04, "r_y2": 264.063, 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0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 267.91600000000005, "r_x1": 544.504, "r_y1": 267.91600000000005, "r_x2": 544.504, "r_y2": 266.106, "r_x3": 498.153, "r_y3": 266.106, "coord_origin": "TOPLEFT"}, "text": "some of the controls in your vehicle. This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 293.054, "r_x1": 295.346, "r_y1": 293.054, "r_x2": 295.346, "r_y2": 285.253, "r_x3": 53.798, "r_y3": 285.253, "coord_origin": "TOPLEFT"}, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 304.013, "r_x1": 294.047, "r_y1": 304.013, "r_x2": 294.047, "r_y2": 296.212, "r_x3": 53.798, "r_y3": 296.212, "coord_origin": "TOPLEFT"}, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.972, "r_x1": 294.046, "r_y1": 314.972, "r_x2": 294.046, "r_y2": 307.171, "r_x3": 53.798, "r_y3": 307.171, "coord_origin": "TOPLEFT"}, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, 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or hard copies of part or all of this work for personal or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 648.771, "r_x1": 294.044, "r_y1": 648.771, "r_x2": 294.044, "r_y2": 642.703, "r_x3": 53.798, "r_y3": 642.703, "coord_origin": "TOPLEFT"}, "text": "classroom use is granted without fee provided that copies are not made or distributed", "orig": "classroom use is granted without fee provided that copies are not made or distributed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 656.741, "r_x1": 294.045, "r_y1": 656.741, "r_x2": 294.045, "r_y2": 650.673, "r_x3": 53.798, "r_y3": 650.673, "coord_origin": "TOPLEFT"}, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "orig": "for profit or commercial 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Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.681, "r_x1": 187.723, "r_y1": 672.681, "r_x2": 187.723, "r_y2": 666.613, "r_x3": 53.798, "r_y3": 666.613, "coord_origin": "TOPLEFT"}, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 134, "label": "text", "bbox": {"l": 53.798, "t": 675.331, "r": 197.863, "b": 681.481, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 681.481, "r_x1": 197.863, "r_y1": 681.481, "r_x2": 197.863, "r_y2": 675.331, "r_x3": 53.798, "r_y3": 675.331, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 135, "label": "text", "bbox": {"l": 53.317, "t": 684.147, "r": 186.746, "b": 690.215, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.317, "r_y0": 690.215, "r_x1": 186.746, "r_y1": 690.215, "r_x2": 186.746, "r_y2": 684.147, "r_x3": 53.317, "r_y3": 684.147, "coord_origin": "TOPLEFT"}, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 136, "label": "text", "bbox": {"l": 53.554, "t": 692.1179999999999, "r": 157.031, "b": 698.186, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.554, "r_y0": 698.186, "r_x1": 157.031, "r_y1": 698.186, "r_x2": 157.031, "r_y2": 692.1179999999999, "r_x3": 53.554, "r_y3": 692.1179999999999, "coord_origin": "TOPLEFT"}, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 53.798, "t": 700.088, "r": 166.941, "b": 706.156, "coord_origin": "TOPLEFT"}, "confidence": 0.509588360786438, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 706.156, "r_x1": 166.941, "r_y1": 706.156, "r_x2": 166.941, "r_y2": 700.088, "r_x3": 53.798, "r_y3": 700.088, "coord_origin": "TOPLEFT"}, "text": "https://doi.org/10.1145/3534678.3539043", "orig": "https://doi.org/10.1145/3534678.3539043", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "picture", "bbox": {"l": 323.408203125, "t": 250.34873962402344, "r": 553.2952270507812, "b": 525.8507080078125, "coord_origin": "TOPLEFT"}, "confidence": 0.5910208821296692, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.87, "r_y0": 353.853, "r_x1": 330.412, "r_y1": 353.853, "r_x2": 330.412, "r_y2": 352.266, "r_x3": 327.87, "r_y3": 352.266, "coord_origin": "TOPLEFT"}, "text": "13", "orig": "13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 333.231, "r_x1": 351.161, "r_y1": 333.231, "r_x2": 351.161, "r_y2": 331.961, "r_x3": 327.83, "r_y3": 331.961, "coord_origin": "TOPLEFT"}, "text": "USING THE VERTICAL TUBE -", "orig": "USING THE VERTICAL TUBE -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 334.841, "r_x1": 348.305, "r_y1": 334.841, "r_x2": 348.305, "r_y2": 333.571, "r_x3": 327.83, "r_y3": 333.571, "coord_origin": "TOPLEFT"}, "text": "MODELS AY11230/11234", "orig": "MODELS AY11230/11234", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 337.855, "r_x1": 350.743, "r_y1": 337.855, "r_x2": 350.743, "r_y2": 336.744, "r_x3": 327.83, "r_y3": 336.744, "coord_origin": "TOPLEFT"}, "text": "1. 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There are two objectives. The lower ", "orig": "1. There are two objectives. The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, "r_x3": 358.426, "r_y3": 334.732, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 340.833, "r_x1": 386.395, "r_y1": 340.833, "r_x2": 386.395, "r_y2": 339.563, "r_x3": 358.426, "r_y3": 339.563, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 342.443, "r_x1": 379.572, "r_y1": 342.443, "r_x2": 379.572, "r_y2": 341.173, "r_x3": 358.426, "r_y3": 341.173, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.427, "r_y0": 344.053, "r_x1": 384.511, "r_y1": 344.053, "r_x2": 384.511, "r_y2": 342.783, "r_x3": 358.427, "r_y3": 342.783, "coord_origin": "TOPLEFT"}, "text": "3. 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To indicate that the new criteria had been applied to a given procedure, a ", "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 257, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.311, "r_y0": 387.794, "r_x1": 549.499, "r_y1": 387.794, "r_x2": 549.499, "r_y2": 386.116, "r_x3": 538.311, "r_y3": 386.116, "coord_origin": "TOPLEFT"}, "text": " is placed on ", "orig": " is placed on ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 258, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 390.073, "r_x1": 547.582, "r_y1": 390.073, "r_x2": 547.582, "r_y2": 388.395, "r_x3": 449.101, "r_y3": 388.395, "coord_origin": "TOPLEFT"}, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "orig": "the circling line of minimums. 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"r_y3": 393.572, "coord_origin": "TOPLEFT"}, "text": " on the circling line of ", "orig": " on the circling line of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 261, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 397.529, "r_x1": 455.747, "r_y1": 397.529, "r_x2": 455.747, "r_y2": 395.85, "r_x3": 449.101, "r_y3": 395.85, "coord_origin": "TOPLEFT"}, "text": "minima.", "orig": "minima.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 262, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 417.429, "r_x1": 496.283, "r_y1": 417.429, "r_x2": 496.283, "r_y2": 415.919, "r_x3": 449.955, "r_y3": 415.919, "coord_origin": "TOPLEFT"}, "text": "$SSO\\glyph6WDQGDUGglyph&LUFOLQJglyph $SSURDFKglyph0DQHXYHULQJglyph5DGLXVglyph7 DEOH", "orig": "$SSO\\glyph6WDQGDUGglyph&LUFOLQJglyph $SSURDFKglyph0DQHXYHULQJglyph5DGLXVglyph7 DEOH", "text_direction": "left_to_right", 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469.356, "r_y1": 422.632, "r_x2": 469.356, "r_y2": 420.619, "r_x3": 449.101, "r_y3": 420.619, "coord_origin": "TOPLEFT"}, "text": "AIRPORT SKETCH", "orig": "AIRPORT SKETCH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 270, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 427.126, "r_x1": 525.936, "r_y1": 427.126, "r_x2": 525.936, "r_y2": 425.448, "r_x3": 449.101, "r_y3": 425.448, "coord_origin": "TOPLEFT"}, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 271, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 429.405, "r_x1": 522.034, "r_y1": 429.405, "r_x2": 522.034, "r_y2": 427.727, "r_x3": 449.101, "r_y3": 427.727, "coord_origin": "TOPLEFT"}, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 272, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 431.683, "r_x1": 524.672, "r_y1": 431.683, "r_x2": 524.672, "r_y2": 430.005, "r_x3": 449.101, "r_y3": 430.005, "coord_origin": "TOPLEFT"}, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 273, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 433.962, "r_x1": 527.172, "r_y1": 433.962, "r_x2": 527.172, "r_y2": 432.284, "r_x3": 449.101, "r_y3": 432.284, "coord_origin": "TOPLEFT"}, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 274, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 436.241, "r_x1": 502.395, "r_y1": 436.241, "r_x2": 502.395, "r_y2": 434.563, "r_x3": 449.101, "r_y3": 434.563, "coord_origin": "TOPLEFT"}, "text": "dimensions (length and width) are shown for al active runways.", "orig": "dimensions (length and width) are shown for al active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 275, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 440.798, "r_x1": 512.927, "r_y1": 440.798, "r_x2": 512.927, "r_y2": 439.12, "r_x3": 449.101, "r_y3": 439.12, "coord_origin": "TOPLEFT"}, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 276, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 445.903, "r_x1": 460.023, "r_y1": 445.903, "r_x2": 460.023, "r_y2": 444.393, "r_x3": 449.955, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 277, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 445.903, "r_x1": 473.988, "r_y1": 445.903, "r_x2": 473.988, "r_y2": 444.393, "r_x3": 464.9, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 278, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 447.954, "r_x1": 474.967, "r_y1": 447.954, "r_x2": 474.967, "r_y2": 446.444, "r_x3": 464.9, "r_y3": 446.444, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 279, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 445.903, "r_x1": 489.457, "r_y1": 445.903, "r_x2": 489.457, "r_y2": 444.393, "r_x3": 478.914, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 280, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 445.903, "r_x1": 505.031, "r_y1": 445.903, "r_x2": 505.031, "r_y2": 444.393, "r_x3": 493.064, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 281, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.581, "r_y0": 445.903, "r_x1": 524.302, "r_y1": 445.903, "r_x2": 524.302, "r_y2": 444.393, "r_x3": 509.581, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 282, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 456.645, "r_x1": 458.314, "r_y1": 456.645, "r_x2": 458.314, "r_y2": 455.135, "r_x3": 449.955, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 283, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 458.696, "r_x1": 461.921, "r_y1": 458.696, "r_x2": 461.921, "r_y2": 457.186, "r_x3": 449.955, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 284, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 460.747, "r_x1": 457.08, "r_y1": 460.747, "r_x2": 457.08, "r_y2": 459.237, "r_x3": 449.955, "r_y3": 459.237, "coord_origin": "TOPLEFT"}, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 285, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 456.645, "r_x1": 472.877, "r_y1": 456.645, "r_x2": 472.877, "r_y2": 455.135, "r_x3": 464.9, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 286, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 458.696, "r_x1": 472.498, "r_y1": 458.696, "r_x2": 472.498, "r_y2": 457.186, "r_x3": 464.9, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 287, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 456.645, "r_x1": 484.707, "r_y1": 456.645, "r_x2": 484.707, "r_y2": 455.135, "r_x3": 478.914, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Closed ", "orig": "Closed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 288, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 458.696, "r_x1": 486.608, "r_y1": 458.696, "r_x2": 486.608, "r_y2": 457.186, "r_x3": 478.914, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 289, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 456.645, "r_x1": 504.207, "r_y1": 456.645, "r_x2": 504.207, "r_y2": 455.135, "r_x3": 493.064, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 290, "rgba": {"r": 0, "g": 0, 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Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. 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Circling only approaches wil not show a TDZE.", "orig": "the landing surface. 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Meeting", "orig": "Annual General Meeting", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 313, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 397.215, "r_x1": 342.658, "r_y1": 396.465, "r_x2": 342.658, "r_y2": 395.296, "r_x3": 329.405, "r_y3": 395.21, "coord_origin": "TOPLEFT"}, "text": "27 February 2013 1", "orig": "27 February 2013 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 315, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.472, "r_y0": 397.213, "r_x1": 386.259, "r_y1": 397.213, "r_x2": 386.259, "r_y2": 395.208, "r_x3": 350.472, "r_y3": 395.208, "coord_origin": "TOPLEFT"}, "text": "2013 interim result and interim dividend announced", "orig": "2013 interim result and interim dividend announced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 316, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, 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confirmation", "orig": "1 Indicative dates only, subject to change/Board confirmation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 320, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 406.543, "r_x1": 391.771, "r_y1": 406.543, "r_x2": 391.771, "r_y2": 404.538, "r_x3": 329.405, "r_y3": 404.538, "coord_origin": "TOPLEFT"}, "text": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney ", "orig": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 321, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 408.577, "r_x1": 369.653, "r_y1": 408.577, "r_x2": 369.653, "r_y2": 406.572, "r_x3": 329.405, "r_y3": 406.572, "coord_origin": "TOPLEFT"}, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 322, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 465.415, "r_x1": 379.529, "r_y1": 465.415, "r_x2": 379.529, "r_y2": 460.96, "r_x3": 363.545, "r_y3": 460.96, "coord_origin": "TOPLEFT"}, "text": "Yesterday", "orig": "Yesterday", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 323, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 468.914, "r_x1": 391.382, "r_y1": 468.914, "r_x2": 391.382, "r_y2": 466.909, "r_x3": 363.545, "r_y3": 466.909, "coord_origin": "TOPLEFT"}, "text": "Established in Sydney in 1837, and then ", "orig": "Established in Sydney in 1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 324, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 470.947, "r_x1": 395.018, "r_y1": 470.947, "r_x2": 395.018, "r_y2": 468.942, "r_x3": 363.545, "r_y3": 468.942, "coord_origin": "TOPLEFT"}, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 325, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 472.981, "r_x1": 394.083, "r_y1": 472.981, "r_x2": 394.083, "r_y2": 470.976, "r_x3": 363.545, "r_y3": 470.976, "coord_origin": "TOPLEFT"}, "text": "the AGL business has an established history ", "orig": "the AGL business has an established history ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 326, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 475.014, "r_x1": 390.607, "r_y1": 475.014, "r_x2": 390.607, "r_y2": 473.009, "r_x3": 363.545, "r_y3": 473.009, "coord_origin": "TOPLEFT"}, "text": "and reputation for serving the gas and ", "orig": "and reputation for serving the gas and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 327, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 477.048, "r_x1": 393.496, "r_y1": 477.048, "r_x2": 393.496, "r_y2": 475.043, "r_x3": 363.545, "r_y3": 475.043, "coord_origin": "TOPLEFT"}, "text": "electricity needs of Australian households. ", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. 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Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 159, "label": "text", "bbox": {"l": 326.88, "t": 276.765, "r": 351.888, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 160, "label": "text", "bbox": {"l": 326.88, "t": 278.375, "r": 335.467, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 161, "label": "text", "bbox": {"l": 326.88, "t": 281.595, "r": 354.578, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 162, "label": "text", "bbox": {"l": 326.88, "t": 283.205, "r": 335.175, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 163, "label": "text", "bbox": {"l": 326.88, "t": 284.816, "r": 355.748, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 164, "label": "text", "bbox": {"l": 326.88, "t": 286.426, "r": 355.576, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 165, "label": "text", "bbox": {"l": 326.88, "t": 288.036, "r": 355.17, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 166, "label": "text", "bbox": {"l": 326.882, "t": 289.646, "r": 356.021, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 167, "label": "text", "bbox": {"l": 326.882, "t": 291.256, "r": 351.575, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 168, "label": "text", "bbox": {"l": 326.882, "t": 292.867, "r": 336.207, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 169, "label": "text", "bbox": {"l": 326.882, "t": 296.087, "r": 335.394, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 170, "label": "text", "bbox": {"l": 326.882, "t": 297.697, "r": 353.186, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 171, "label": "text", "bbox": {"l": 326.883, "t": 299.308, "r": 354.432, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 172, "label": "text", "bbox": {"l": 326.883, "t": 300.918, "r": 333.328, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 173, "label": "text", "bbox": {"l": 326.883, "t": 302.528, "r": 355.709, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 174, "label": "text", "bbox": {"l": 326.883, "t": 304.138, "r": 356.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 175, "label": "text", "bbox": {"l": 326.883, "t": 305.748, "r": 354.466, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 176, "label": "text", "bbox": {"l": 326.883, "t": 307.359, "r": 357.261, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 177, "label": "text", "bbox": {"l": 326.883, "t": 308.969, "r": 356.449, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 178, "label": "text", "bbox": {"l": 326.883, "t": 310.579, "r": 343.173, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 179, "label": "text", "bbox": {"l": 326.883, "t": 313.799, "r": 344.134, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 180, "label": "text", "bbox": {"l": 326.883, "t": 315.41, "r": 348.502, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 181, "label": "text", "bbox": {"l": 326.883, "t": 317.02, "r": 354.081, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 182, "label": "text", "bbox": {"l": 326.885, "t": 318.63, "r": 354.842, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 183, "label": "text", "bbox": {"l": 326.885, "t": 320.24, "r": 348.021, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 184, "label": "text", "bbox": {"l": 326.885, "t": 321.851, "r": 352.968, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 185, "label": "text", "bbox": {"l": 326.886, "t": 323.461, "r": 357.529, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 186, "label": "text", "bbox": {"l": 326.886, "t": 325.071, "r": 350.138, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 187, "label": "text", "bbox": {"l": 326.886, "t": 326.681, "r": 353.141, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 188, "label": "text", "bbox": {"l": 326.886, "t": 328.291, "r": 336.892, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 189, "label": "text", "bbox": {"l": 358.42, "t": 263.88300000000004, "r": 366.933, "b": 265.153, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 190, "label": "text", "bbox": {"l": 358.42, "t": 265.49300000000005, "r": 388.997, "b": 266.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 191, "label": "text", "bbox": {"l": 358.42, "t": 267.10299999999995, "r": 384.59, "b": 268.37300000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 192, "label": "text", "bbox": {"l": 358.422, "t": 268.71299999999997, "r": 385.698, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 193, "label": "text", "bbox": {"l": 358.423, "t": 270.32399999999996, "r": 385.612, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 194, "label": "text", "bbox": {"l": 358.423, "t": 271.93399999999997, "r": 385.389, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 195, "label": "text", "bbox": {"l": 358.423, "t": 275.154, "r": 377.35, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 196, "label": "text", "bbox": {"l": 358.423, "t": 276.765, "r": 387.359, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 197, "label": "text", "bbox": {"l": 358.423, "t": 278.375, "r": 387.748, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 198, "label": "text", "bbox": {"l": 358.423, "t": 279.985, "r": 364.169, "b": 281.255, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 199, "label": "text", "bbox": {"l": 358.423, "t": 281.595, "r": 388.266, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 200, "label": "text", "bbox": {"l": 358.423, "t": 283.205, "r": 381.567, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 201, "label": "text", "bbox": {"l": 358.423, "t": 284.816, "r": 387.673, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 202, "label": "text", "bbox": {"l": 358.425, "t": 286.426, "r": 382.771, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 203, "label": "text", "bbox": {"l": 358.425, "t": 288.036, "r": 367.987, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 204, "label": "text", "bbox": {"l": 358.425, "t": 289.646, "r": 387.601, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 205, "label": "text", "bbox": {"l": 358.425, "t": 291.256, "r": 389.075, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 206, "label": "text", "bbox": {"l": 358.425, "t": 292.867, "r": 373.137, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 207, "label": "text", "bbox": {"l": 358.425, "t": 296.087, "r": 381.745, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 208, "label": "text", "bbox": {"l": 358.425, "t": 297.697, "r": 389.01, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 209, "label": "text", "bbox": {"l": 358.425, "t": 299.308, "r": 382.733, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 210, "label": "text", "bbox": {"l": 358.425, "t": 300.918, "r": 388.701, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 211, "label": "text", "bbox": {"l": 358.425, "t": 302.528, "r": 385.147, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 212, "label": "text", "bbox": {"l": 358.425, "t": 304.138, "r": 364.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 213, "label": "text", "bbox": {"l": 358.425, "t": 305.748, "r": 387.708, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 214, "label": "text", "bbox": {"l": 358.425, "t": 307.359, "r": 387.857, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 215, "label": "text", "bbox": {"l": 358.425, "t": 308.969, "r": 385.414, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 216, "label": "text", "bbox": {"l": 358.425, "t": 310.579, "r": 382.561, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 217, "label": "text", "bbox": {"l": 358.426, "t": 312.189, "r": 386.568, "b": 313.459, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 218, "label": "text", "bbox": {"l": 358.426, "t": 313.799, "r": 385.703, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 219, "label": "text", "bbox": {"l": 358.426, "t": 315.41, "r": 366.744, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 220, "label": "text", "bbox": {"l": 358.426, "t": 317.02, "r": 384.99, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 221, "label": "text", "bbox": {"l": 358.426, "t": 318.63, "r": 386.867, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 222, "label": "text", "bbox": {"l": 358.426, "t": 320.24, "r": 385.949, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 223, "label": "text", "bbox": {"l": 358.426, "t": 321.851, "r": 387.137, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 226, "label": "text", "bbox": {"l": 358.426, "t": 326.681, "r": 386.352, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 227, "label": "text", "bbox": {"l": 358.426, "t": 328.291, "r": 383.28, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 231, "label": "text", "bbox": {"l": 358.426, "t": 336.342, "r": 372.014, "b": 337.613, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 232, "label": "text", "bbox": {"l": 358.426, "t": 337.953, "r": 385.634, "b": 339.223, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 233, "label": "text", "bbox": {"l": 358.426, "t": 339.563, "r": 386.395, "b": 340.833, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 340.833, "r_x1": 386.395, "r_y1": 340.833, "r_x2": 386.395, "r_y2": 339.563, "r_x3": 358.426, "r_y3": 339.563, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 234, "label": "text", "bbox": {"l": 358.426, "t": 341.173, "r": 379.572, "b": 342.443, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 342.443, "r_x1": 379.572, "r_y1": 342.443, "r_x2": 379.572, "r_y2": 341.173, "r_x3": 358.426, "r_y3": 341.173, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 235, "label": "text", "bbox": {"l": 358.427, "t": 342.783, "r": 384.511, "b": 344.053, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.427, "r_y0": 344.053, "r_x1": 384.511, "r_y1": 344.053, "r_x2": 384.511, "r_y2": 342.783, "r_x3": 358.427, "r_y3": 342.783, "coord_origin": "TOPLEFT"}, "text": "3. 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The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 299, "label": "text", "bbox": {"l": 449.101, "t": 434.563, "r": 502.395, "b": 436.241, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 274, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 436.241, "r_x1": 502.395, "r_y1": 436.241, "r_x2": 502.395, "r_y2": 434.563, "r_x3": 449.101, "r_y3": 434.563, "coord_origin": "TOPLEFT"}, "text": "dimensions (length and width) are shown for al active runways.", "orig": "dimensions (length and width) are shown for al active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 300, "label": "text", "bbox": {"l": 449.101, "t": 439.12, "r": 512.927, "b": 440.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 275, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 440.798, "r_x1": 512.927, "r_y1": 440.798, "r_x2": 512.927, "r_y2": 439.12, "r_x3": 449.101, "r_y3": 439.12, "coord_origin": "TOPLEFT"}, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 301, "label": "text", "bbox": {"l": 449.955, "t": 444.393, "r": 460.023, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 276, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 445.903, "r_x1": 460.023, "r_y1": 445.903, "r_x2": 460.023, "r_y2": 444.393, "r_x3": 449.955, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 302, "label": "text", "bbox": {"l": 464.9, "t": 444.393, "r": 473.988, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 277, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 445.903, "r_x1": 473.988, "r_y1": 445.903, "r_x2": 473.988, "r_y2": 444.393, "r_x3": 464.9, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 303, "label": "text", "bbox": {"l": 464.9, "t": 446.444, "r": 474.967, "b": 447.954, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 278, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 447.954, "r_x1": 474.967, "r_y1": 447.954, "r_x2": 474.967, "r_y2": 446.444, "r_x3": 464.9, "r_y3": 446.444, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 304, "label": "text", "bbox": {"l": 478.914, "t": 444.393, "r": 489.457, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 279, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 445.903, "r_x1": 489.457, "r_y1": 445.903, "r_x2": 489.457, "r_y2": 444.393, "r_x3": 478.914, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 305, "label": "text", "bbox": {"l": 493.064, "t": 444.393, "r": 505.031, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 280, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 445.903, "r_x1": 505.031, "r_y1": 445.903, "r_x2": 505.031, "r_y2": 444.393, "r_x3": 493.064, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 306, "label": "text", "bbox": {"l": 509.581, "t": 444.393, "r": 524.302, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 281, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.581, "r_y0": 445.903, "r_x1": 524.302, "r_y1": 445.903, "r_x2": 524.302, "r_y2": 444.393, "r_x3": 509.581, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 307, "label": "text", "bbox": {"l": 449.955, "t": 455.135, "r": 458.314, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 282, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 456.645, "r_x1": 458.314, "r_y1": 456.645, "r_x2": 458.314, "r_y2": 455.135, "r_x3": 449.955, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 308, "label": "text", "bbox": {"l": 449.955, "t": 457.186, "r": 461.921, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 283, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 458.696, "r_x1": 461.921, "r_y1": 458.696, "r_x2": 461.921, "r_y2": 457.186, "r_x3": 449.955, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 309, "label": "text", "bbox": {"l": 449.955, "t": 459.237, "r": 457.08, "b": 460.747, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 284, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 460.747, "r_x1": 457.08, "r_y1": 460.747, "r_x2": 457.08, "r_y2": 459.237, "r_x3": 449.955, "r_y3": 459.237, "coord_origin": "TOPLEFT"}, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 310, "label": "text", "bbox": {"l": 464.9, "t": 455.135, "r": 472.877, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 285, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 456.645, "r_x1": 472.877, "r_y1": 456.645, "r_x2": 472.877, "r_y2": 455.135, "r_x3": 464.9, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 311, "label": "text", "bbox": {"l": 464.9, "t": 457.186, "r": 472.498, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 286, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 458.696, "r_x1": 472.498, "r_y1": 458.696, "r_x2": 472.498, "r_y2": 457.186, "r_x3": 464.9, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 312, "label": "text", "bbox": {"l": 478.914, "t": 455.135, "r": 484.707, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 287, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 456.645, "r_x1": 484.707, "r_y1": 456.645, "r_x2": 484.707, "r_y2": 455.135, "r_x3": 478.914, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Closed ", "orig": "Closed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 313, "label": "text", "bbox": {"l": 478.914, "t": 457.186, "r": 486.608, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 288, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 458.696, "r_x1": 486.608, "r_y1": 458.696, "r_x2": 486.608, "r_y2": 457.186, "r_x3": 478.914, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 314, "label": "text", "bbox": {"l": 493.064, "t": 455.135, "r": 504.207, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 289, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 456.645, "r_x1": 504.207, "r_y1": 456.645, "r_x2": 504.207, "r_y2": 455.135, "r_x3": 493.064, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 315, "label": "text", "bbox": {"l": 449.101, "t": 469.689, "r": 548.597, "b": 471.367, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 290, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 471.367, "r_x1": 548.597, "r_y1": 471.367, "r_x2": 548.597, "r_y2": 469.689, "r_x3": 449.101, "r_y3": 469.689, "coord_origin": "TOPLEFT"}, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 316, "label": "text", "bbox": {"l": 449.101, "t": 471.967, "r": 500.082, "b": 473.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 291, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 473.645, "r_x1": 500.082, "r_y1": 473.645, "r_x2": 500.082, "r_y2": 471.967, "r_x3": 449.101, "r_y3": 471.967, "coord_origin": "TOPLEFT"}, "text": "sions, runway slope, arresting gear, and displaced threshold.", "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 317, "label": "text", "bbox": {"l": 449.101, "t": 476.525, "r": 547.826, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 292, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 478.203, "r_x1": 547.826, "r_y1": 478.203, "r_x2": 547.826, "r_y2": 476.525, "r_x3": 449.101, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "orig": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 318, "label": "text", "bbox": {"l": 547.826, "t": 476.525, "r": 548.459, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 293, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 547.826, "r_y0": 478.203, "r_x1": 548.459, "r_y1": 478.203, "r_x2": 548.459, "r_y2": 476.525, "r_x3": 547.826, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 319, "label": "text", "bbox": {"l": 449.101, "t": 478.803, "r": 470.526, "b": 480.481, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 294, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 480.481, "r_x1": 470.526, "r_y1": 480.481, "r_x2": 470.526, "r_y2": 478.803, "r_x3": 449.101, "r_y3": 478.803, "coord_origin": "TOPLEFT"}, "text": "pads may also be shown.", "orig": "pads may also be shown.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 320, "label": "text", "bbox": {"l": 449.101, "t": 483.361, "r": 493.379, "b": 485.039, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 295, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 485.039, "r_x1": 493.379, "r_y1": 485.039, "r_x2": 493.379, "r_y2": 483.361, "r_x3": 449.101, "r_y3": 483.361, "coord_origin": "TOPLEFT"}, "text": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "orig": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 449.101, "t": 487.918, "r": 551.8, "b": 496.432, "coord_origin": "TOPLEFT"}, "confidence": 0.5027860403060913, "cells": [{"index": 296, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 489.596, "r_x1": 549.162, "r_y1": 489.596, "r_x2": 549.162, "r_y2": 487.918, "r_x3": 449.101, "r_y3": 487.918, "coord_origin": "TOPLEFT"}, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 297, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 491.875, "r_x1": 546.909, "r_y1": 491.875, "r_x2": 546.909, "r_y2": 490.197, "r_x3": 449.101, "r_y3": 490.197, "coord_origin": "TOPLEFT"}, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 298, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 494.154, "r_x1": 551.8, "r_y1": 494.154, "r_x2": 551.8, "r_y2": 492.475, "r_x3": 449.101, "r_y3": 492.475, "coord_origin": "TOPLEFT"}, "text": "DLUSRUW\u00b6VglyphXVDEOHglyphUXQZD\\VglyphPHDVXUHGglyphLQglyphIHHWglyphIURPglyphPHDQglyphVHDglyphOHYHOglyphglyph7KHglyph7'=(glyphLVglyphWKHglyphKLJKHVWglyphHOHYDWLRQglyphLQglyphWKHglyph\u00bfUVWglyphglyphglyphglyphglyphglyphglyphIHHWglyphRIglyph", "orig": "DLUSRUW\u00b6VglyphXVDEOHglyphUXQZD\\VglyphPHDVXUHGglyphLQglyphIHHWglyphIURPglyphPHDQglyphVHDglyphOHYHOglyphglyph7KHglyph7'=(glyphLVglyphWKHglyphKLJKHVWglyphHOHYDWLRQglyphLQglyphWKHglyph\u00bfUVWglyphglyphglyphglyphglyphglyphglyphIHHWglyphRIglyph", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 299, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 496.432, "r_x1": 505.851, "r_y1": 496.432, "r_x2": 505.851, "r_y2": 494.754, "r_x3": 449.101, "r_y3": 494.754, "coord_origin": "TOPLEFT"}, "text": "the landing surface. Circling only approaches wil not show a TDZE.", "orig": "the landing surface. 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 364, "label": "text", "bbox": {"l": 498.153, "t": 268.149, "r": 544.013, "b": 269.95899999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 365, "label": "text", "bbox": {"l": 498.153, "t": 270.192, "r": 544.12, "b": 272.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 366, "label": "text", "bbox": {"l": 498.153, "t": 272.235, "r": 515.411, "b": 274.04499999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. 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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"label": "text", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "text", "bbox": {"l": 336.693, "t": 192.63099999999997, "r": 439.457, "b": 238.466, "coord_origin": "TOPLEFT"}, "confidence": 0.8027974367141724, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.528, "r_y0": 203.03300000000002, "r_x1": 414.848, "r_y1": 203.03300000000002, "r_x2": 414.848, "r_y2": 192.63099999999997, "r_x3": 361.528, "r_y3": 192.63099999999997, "coord_origin": "TOPLEFT"}, "text": "Peter Staar", "orig": "Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 360.02, "r_y0": 214.55499999999995, "r_x1": 416.129, "r_y1": 214.55499999999995, "r_x2": 416.129, "r_y2": 205.88699999999994, "r_x3": 360.02, "r_y3": 205.88699999999994, "coord_origin": "TOPLEFT"}, "text": "IBM Research", "orig": "IBM Research", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.693, "r_y0": 226.51, "r_x1": 439.457, "r_y1": 226.51, "r_x2": 439.457, "r_y2": 217.84199999999998, "r_x3": 336.693, "r_y3": 217.84199999999998, "coord_origin": "TOPLEFT"}, "text": "Rueschlikon, Switzerland", "orig": "Rueschlikon, Switzerland", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.207, "r_y0": 238.466, "r_x1": 429.943, "r_y1": 238.466, "r_x2": 429.943, "r_y2": 229.798, "r_x3": 346.207, "r_y3": 229.798, "coord_origin": "TOPLEFT"}, "text": "taa@zurich.ibm.com", "orig": "taa@zurich.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 53.798, "t": 248.64099999999996, "r": 111.944, "b": 257.70799999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8876155018806458, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 257.70799999999997, "r_x1": 111.944, "r_y1": 257.70799999999997, "r_x2": 111.944, "r_y2": 248.64099999999996, "r_x3": 53.798, "r_y3": 248.64099999999996, "coord_origin": "TOPLEFT"}, "text": "ABSTRACT", "orig": "ABSTRACT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ABSTRACT"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.467, "t": 263.33500000000004, "r": 295.56, "b": 534.15, "coord_origin": "TOPLEFT"}, "confidence": 0.9823057055473328, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 271.13599999999997, "r_x1": 295.556, "r_y1": 271.13599999999997, "r_x2": 295.556, "r_y2": 263.33500000000004, "r_x3": 53.484, "r_y3": 263.33500000000004, "coord_origin": "TOPLEFT"}, "text": "Accurate document layout analysis is a key requirement for high-", "orig": "Accurate document layout analysis is a key requirement for high-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 282.095, "r_x1": 294.042, "r_y1": 282.095, "r_x2": 294.042, "r_y2": 274.294, "r_x3": 53.798, "r_y3": 274.294, "coord_origin": "TOPLEFT"}, "text": "quality PDF document conversion. With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 293.054, "r_x1": 295.346, "r_y1": 293.054, "r_x2": 295.346, "r_y2": 285.253, "r_x3": 53.798, "r_y3": 285.253, "coord_origin": "TOPLEFT"}, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 304.013, "r_x1": 294.047, "r_y1": 304.013, "r_x2": 294.047, "r_y2": 296.212, "r_x3": 53.798, "r_y3": 296.212, "coord_origin": "TOPLEFT"}, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.972, "r_x1": 294.046, "r_y1": 314.972, "r_x2": 294.046, "r_y2": 307.171, "r_x3": 53.798, "r_y3": 307.171, "coord_origin": "TOPLEFT"}, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 512.232, "r_x1": 295.556, "r_y1": 512.232, "r_x2": 295.556, "r_y2": 504.431, "r_x3": 53.798, "r_y3": 504.431, "coord_origin": "TOPLEFT"}, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 523.191, "r_x1": 294.216, "r_y1": 523.191, "r_x2": 294.216, "r_y2": 515.39, "r_x3": 53.798, "r_y3": 515.39, "coord_origin": "TOPLEFT"}, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 534.15, "r_x1": 212.055, "r_y1": 534.15, "r_x2": 212.055, "r_y2": 526.3489999999999, "r_x3": 53.798, "r_y3": 526.3489999999999, "coord_origin": "TOPLEFT"}, "text": "general-purpose document-layout analysis.", "orig": "general-purpose document-layout analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.798, "t": 551.935, "r": 134.82, "b": 561.002, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 561.002, "r_x1": 134.82, "r_y1": 561.002, "r_x2": 134.82, "r_y2": 551.935, "r_x3": 53.798, "r_y3": 551.935, "coord_origin": "TOPLEFT"}, "text": "CCS CONCEPTS", "orig": "CCS CONCEPTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.798, "t": 564.083, "r": 297.853, "b": 597.29, "coord_origin": "TOPLEFT"}, "confidence": 0.8458839058876038, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.431, "r_x1": 56.945, "r_y1": 574.431, "r_x2": 56.945, "r_y2": 566.63, "r_x3": 53.798, "r_y3": 566.63, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 58.44, "r_y0": 574.307, "r_x1": 142.446, "r_y1": 574.307, "r_x2": 142.446, "r_y2": 566.854, "r_x3": 58.44, "r_y3": 566.854, "coord_origin": "TOPLEFT"}, "text": "Informationsystems", "orig": "Informationsystems", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.938, "r_y0": 575.3720000000001, "r_x1": 153.151, "r_y1": 575.3720000000001, "r_x2": 153.151, "r_y2": 564.083, "r_x3": 143.938, "r_y3": 564.083, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.646, "r_y0": 574.307, "r_x1": 235.46, "r_y1": 574.307, "r_x2": 235.46, "r_y2": 566.854, "r_x3": 154.646, "r_y3": 566.854, "coord_origin": "TOPLEFT"}, "text": "Documentstructure", "orig": "Documentstructure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.457, "r_y0": 574.431, "r_x1": 242.174, "r_y1": 574.431, "r_x2": 242.174, "r_y2": 566.63, "r_x3": 235.457, "r_y3": 566.63, "coord_origin": "TOPLEFT"}, "text": "; 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Loosen the retention screw, then rotate ", "orig": "2. Loosen the retention screw, then rotate ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.594, "r_y0": 345.073, "r_x1": 351.67, "r_y1": 345.073, "r_x2": 351.67, "r_y2": 343.961, "r_x3": 329.594, "r_y3": 343.961, "coord_origin": "TOPLEFT"}, "text": "the adjustment ring to change the ", "orig": "the adjustment ring to change the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.269, "r_y0": 347.876, "r_x1": 351.18, "r_y1": 348.298, "r_x2": 351.18, "r_y2": 347.186, "r_x3": 334.269, "r_y3": 346.764, "coord_origin": "TOPLEFT"}, "text": " re that both the images in", "orig": " re that both the images in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.093, "r_y0": 346.504, "r_x1": 346.332, "r_y1": 346.504, "r_x2": 346.332, "r_y2": 345.393, "r_x3": 329.093, "r_y3": 345.393, "coord_origin": "TOPLEFT"}, "text": " length of the vertical tube.", "orig": " length of the vertical tube.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 348.298, "r_x1": 334.702, "r_y1": 348.298, "r_x2": 334.702, "r_y2": 347.186, "r_x3": 327.83, "r_y3": 347.186, "coord_origin": "TOPLEFT"}, "text": "3. Make su", "orig": "3. Make su", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.253, "r_y0": 258.679, "r_x1": 362.6, "r_y1": 258.49800000000005, "r_x2": 362.6, "r_y2": 255.64099999999996, "r_x3": 327.253, "r_y3": 255.82100000000003, "coord_origin": "TOPLEFT"}, "text": "OPERATION (cont.)", "orig": "OPERATION (cont.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 265.153, "r_x1": 345.844, "r_y1": 265.153, "r_x2": 345.844, "r_y2": 263.88300000000004, "r_x3": 326.88, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "SELECTING OBJECTIVE ", "orig": "SELECTING OBJECTIVE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 266.76300000000003, "r_x1": 340.542, "r_y1": 266.76300000000003, "r_x2": 340.542, "r_y2": 265.49300000000005, "r_x3": 326.88, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "MAGNIFICATION", "orig": "MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 268.37300000000005, "r_x1": 355.17, "r_y1": 268.37300000000005, "r_x2": 355.17, "r_y2": 267.10299999999995, "r_x3": 326.88, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": "1. There are two objectives. The lower ", "orig": "1. There are two objectives. The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. 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", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 154, "label": "text", "bbox": {"l": 326.88, "t": 268.71299999999997, "r": 356.201, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 155, "label": "text", "bbox": {"l": 326.88, "t": 270.32399999999996, "r": 345.801, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 156, "label": "text", "bbox": {"l": 326.88, "t": 271.93399999999997, "r": 353.377, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 157, "label": "text", "bbox": {"l": 326.88, "t": 273.544, "r": 353.892, "b": 274.81399999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 158, "label": "text", "bbox": {"l": 326.88, "t": 275.154, "r": 356.017, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 159, "label": "text", "bbox": {"l": 326.88, "t": 276.765, "r": 351.888, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 160, "label": "text", "bbox": {"l": 326.88, "t": 278.375, "r": 335.467, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 161, "label": "text", "bbox": {"l": 326.88, "t": 281.595, "r": 354.578, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 162, "label": "text", "bbox": {"l": 326.88, "t": 283.205, "r": 335.175, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 163, "label": "text", "bbox": {"l": 326.88, "t": 284.816, "r": 355.748, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 164, "label": "text", "bbox": {"l": 326.88, "t": 286.426, "r": 355.576, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 165, "label": "text", "bbox": {"l": 326.88, "t": 288.036, "r": 355.17, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 166, "label": "text", "bbox": {"l": 326.882, "t": 289.646, "r": 356.021, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 167, "label": "text", "bbox": {"l": 326.882, "t": 291.256, "r": 351.575, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 168, "label": "text", "bbox": {"l": 326.882, "t": 292.867, "r": 336.207, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 169, "label": "text", "bbox": {"l": 326.882, "t": 296.087, "r": 335.394, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 170, "label": "text", "bbox": {"l": 326.882, "t": 297.697, "r": 353.186, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 171, "label": "text", "bbox": {"l": 326.883, "t": 299.308, "r": 354.432, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 172, "label": "text", "bbox": {"l": 326.883, "t": 300.918, "r": 333.328, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 173, "label": "text", "bbox": {"l": 326.883, "t": 302.528, "r": 355.709, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 174, "label": "text", "bbox": {"l": 326.883, "t": 304.138, "r": 356.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 175, "label": "text", "bbox": {"l": 326.883, "t": 305.748, "r": 354.466, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 176, "label": "text", "bbox": {"l": 326.883, "t": 307.359, "r": 357.261, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 177, "label": "text", "bbox": {"l": 326.883, "t": 308.969, "r": 356.449, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 178, "label": "text", "bbox": {"l": 326.883, "t": 310.579, "r": 343.173, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 179, "label": "text", "bbox": {"l": 326.883, "t": 313.799, "r": 344.134, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 180, "label": "text", "bbox": {"l": 326.883, "t": 315.41, "r": 348.502, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 181, "label": "text", "bbox": {"l": 326.883, "t": 317.02, "r": 354.081, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 182, "label": "text", "bbox": {"l": 326.885, "t": 318.63, "r": 354.842, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 183, "label": "text", "bbox": {"l": 326.885, "t": 320.24, "r": 348.021, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 184, "label": "text", "bbox": {"l": 326.885, "t": 321.851, "r": 352.968, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 185, "label": "text", "bbox": {"l": 326.886, "t": 323.461, "r": 357.529, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 186, "label": "text", "bbox": {"l": 326.886, "t": 325.071, "r": 350.138, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 187, "label": "text", "bbox": {"l": 326.886, "t": 326.681, "r": 353.141, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 188, "label": "text", "bbox": {"l": 326.886, "t": 328.291, "r": 336.892, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 189, "label": "text", "bbox": {"l": 358.42, "t": 263.88300000000004, "r": 366.933, "b": 265.153, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 190, "label": "text", "bbox": {"l": 358.42, "t": 265.49300000000005, "r": 388.997, "b": 266.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 191, "label": "text", "bbox": {"l": 358.42, "t": 267.10299999999995, "r": 384.59, "b": 268.37300000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 192, "label": "text", "bbox": {"l": 358.422, "t": 268.71299999999997, "r": 385.698, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 193, "label": "text", "bbox": {"l": 358.423, "t": 270.32399999999996, "r": 385.612, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 194, "label": "text", "bbox": {"l": 358.423, "t": 271.93399999999997, "r": 385.389, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 195, "label": "text", "bbox": {"l": 358.423, "t": 275.154, "r": 377.35, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 196, "label": "text", "bbox": {"l": 358.423, "t": 276.765, "r": 387.359, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 197, "label": "text", "bbox": {"l": 358.423, "t": 278.375, "r": 387.748, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 198, "label": "text", "bbox": {"l": 358.423, "t": 279.985, "r": 364.169, "b": 281.255, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 199, "label": "text", "bbox": {"l": 358.423, "t": 281.595, "r": 388.266, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 200, "label": "text", "bbox": {"l": 358.423, "t": 283.205, "r": 381.567, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 201, "label": "text", "bbox": {"l": 358.423, "t": 284.816, "r": 387.673, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 202, "label": "text", "bbox": {"l": 358.425, "t": 286.426, "r": 382.771, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 203, "label": "text", "bbox": {"l": 358.425, "t": 288.036, "r": 367.987, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 204, "label": "text", "bbox": {"l": 358.425, "t": 289.646, "r": 387.601, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 205, "label": "text", "bbox": {"l": 358.425, "t": 291.256, "r": 389.075, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 206, "label": "text", "bbox": {"l": 358.425, "t": 292.867, "r": 373.137, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 207, "label": "text", "bbox": {"l": 358.425, "t": 296.087, "r": 381.745, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 208, "label": "text", "bbox": {"l": 358.425, "t": 297.697, "r": 389.01, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 209, "label": "text", "bbox": {"l": 358.425, "t": 299.308, "r": 382.733, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 210, "label": "text", "bbox": {"l": 358.425, "t": 300.918, "r": 388.701, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 211, "label": "text", "bbox": {"l": 358.425, "t": 302.528, "r": 385.147, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 212, "label": "text", "bbox": {"l": 358.425, "t": 304.138, "r": 364.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 213, "label": "text", "bbox": {"l": 358.425, "t": 305.748, "r": 387.708, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 214, "label": "text", "bbox": {"l": 358.425, "t": 307.359, "r": 387.857, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 215, "label": "text", "bbox": {"l": 358.425, "t": 308.969, "r": 385.414, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 216, "label": "text", "bbox": {"l": 358.425, "t": 310.579, "r": 382.561, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 217, "label": "text", "bbox": {"l": 358.426, "t": 312.189, "r": 386.568, "b": 313.459, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 218, "label": "text", "bbox": {"l": 358.426, "t": 313.799, "r": 385.703, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 219, "label": "text", "bbox": {"l": 358.426, "t": 315.41, "r": 366.744, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 220, "label": "text", "bbox": {"l": 358.426, "t": 317.02, "r": 384.99, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 221, "label": "text", "bbox": {"l": 358.426, "t": 318.63, "r": 386.867, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 222, "label": "text", "bbox": {"l": 358.426, "t": 320.24, "r": 385.949, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 223, "label": "text", "bbox": {"l": 358.426, "t": 321.851, "r": 387.137, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 224, "label": "text", "bbox": {"l": 358.426, "t": 323.461, "r": 384.369, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 225, "label": "text", "bbox": {"l": 358.426, "t": 325.071, "r": 386.093, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 226, "label": "text", "bbox": {"l": 358.426, "t": 326.681, "r": 386.352, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 227, "label": "text", "bbox": {"l": 358.426, "t": 328.291, "r": 383.28, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 228, "label": "text", "bbox": {"l": 358.426, "t": 329.902, "r": 382.634, "b": 331.172, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 229, "label": "text", "bbox": {"l": 358.426, "t": 333.122, "r": 375.677, "b": 334.392, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 230, "label": "text", "bbox": {"l": 358.426, "t": 334.732, "r": 386.737, "b": 336.002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, "r_x3": 358.426, "r_y3": 334.732, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 231, "label": "text", "bbox": {"l": 358.426, "t": 336.342, "r": 372.014, "b": 337.613, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 232, "label": "text", "bbox": {"l": 358.426, "t": 337.953, "r": 385.634, "b": 339.223, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 233, "label": "text", "bbox": {"l": 358.426, "t": 339.563, "r": 386.395, "b": 340.833, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 340.833, "r_x1": 386.395, "r_y1": 340.833, "r_x2": 386.395, "r_y2": 339.563, "r_x3": 358.426, "r_y3": 339.563, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 234, "label": "text", "bbox": {"l": 358.426, "t": 341.173, "r": 379.572, "b": 342.443, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 342.443, "r_x1": 379.572, "r_y1": 342.443, "r_x2": 379.572, "r_y2": 341.173, "r_x3": 358.426, "r_y3": 341.173, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 235, "label": "text", "bbox": {"l": 358.427, "t": 342.783, "r": 384.511, "b": 344.053, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.427, "r_y0": 344.053, "r_x1": 384.511, "r_y1": 344.053, "r_x2": 384.511, "r_y2": 342.783, "r_x3": 358.427, "r_y3": 342.783, "coord_origin": "TOPLEFT"}, "text": "3. 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emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 296, "label": "text", "bbox": {"l": 449.101, "t": 427.727, "r": 522.034, "b": 429.405, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 271, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 429.405, "r_x1": 522.034, "r_y1": 429.405, "r_x2": 522.034, "r_y2": 427.727, "r_x3": 449.101, "r_y3": 427.727, "coord_origin": "TOPLEFT"}, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 297, "label": "text", "bbox": {"l": 449.101, "t": 430.005, "r": 524.672, "b": 431.683, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 272, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 431.683, "r_x1": 524.672, "r_y1": 431.683, "r_x2": 524.672, "r_y2": 430.005, "r_x3": 449.101, "r_y3": 430.005, "coord_origin": "TOPLEFT"}, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 298, "label": "text", "bbox": {"l": 449.101, "t": 432.284, "r": 527.172, "b": 433.962, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 273, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 433.962, "r_x1": 527.172, "r_y1": 433.962, "r_x2": 527.172, "r_y2": 432.284, "r_x3": 449.101, "r_y3": 432.284, "coord_origin": "TOPLEFT"}, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 299, "label": "text", "bbox": {"l": 449.101, "t": 434.563, "r": 502.395, "b": 436.241, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 274, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 436.241, "r_x1": 502.395, "r_y1": 436.241, "r_x2": 502.395, "r_y2": 434.563, "r_x3": 449.101, "r_y3": 434.563, "coord_origin": "TOPLEFT"}, "text": "dimensions (length and width) are shown for al active runways.", "orig": "dimensions (length and width) are shown for al active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 300, "label": "text", "bbox": {"l": 449.101, "t": 439.12, "r": 512.927, "b": 440.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 275, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 440.798, "r_x1": 512.927, "r_y1": 440.798, "r_x2": 512.927, "r_y2": 439.12, "r_x3": 449.101, "r_y3": 439.12, "coord_origin": "TOPLEFT"}, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 301, "label": "text", "bbox": {"l": 449.955, "t": 444.393, "r": 460.023, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 276, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 445.903, "r_x1": 460.023, "r_y1": 445.903, "r_x2": 460.023, "r_y2": 444.393, "r_x3": 449.955, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 302, "label": "text", "bbox": {"l": 464.9, "t": 444.393, "r": 473.988, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 277, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 445.903, "r_x1": 473.988, "r_y1": 445.903, "r_x2": 473.988, "r_y2": 444.393, "r_x3": 464.9, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 303, "label": "text", "bbox": {"l": 464.9, "t": 446.444, "r": 474.967, "b": 447.954, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 278, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 447.954, "r_x1": 474.967, "r_y1": 447.954, "r_x2": 474.967, "r_y2": 446.444, "r_x3": 464.9, "r_y3": 446.444, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 304, "label": "text", "bbox": {"l": 478.914, "t": 444.393, "r": 489.457, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 279, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 445.903, "r_x1": 489.457, "r_y1": 445.903, "r_x2": 489.457, "r_y2": 444.393, "r_x3": 478.914, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 305, "label": "text", "bbox": {"l": 493.064, "t": 444.393, "r": 505.031, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 280, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 445.903, "r_x1": 505.031, "r_y1": 445.903, "r_x2": 505.031, "r_y2": 444.393, "r_x3": 493.064, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 306, "label": "text", "bbox": {"l": 509.581, "t": 444.393, "r": 524.302, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 281, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.581, "r_y0": 445.903, "r_x1": 524.302, "r_y1": 445.903, "r_x2": 524.302, "r_y2": 444.393, "r_x3": 509.581, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 307, "label": "text", "bbox": {"l": 449.955, "t": 455.135, "r": 458.314, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 282, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 456.645, "r_x1": 458.314, "r_y1": 456.645, "r_x2": 458.314, "r_y2": 455.135, "r_x3": 449.955, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 308, "label": "text", "bbox": {"l": 449.955, "t": 457.186, "r": 461.921, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 283, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 458.696, "r_x1": 461.921, "r_y1": 458.696, "r_x2": 461.921, "r_y2": 457.186, "r_x3": 449.955, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 309, "label": "text", "bbox": {"l": 449.955, "t": 459.237, "r": 457.08, "b": 460.747, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 284, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 460.747, "r_x1": 457.08, "r_y1": 460.747, "r_x2": 457.08, "r_y2": 459.237, "r_x3": 449.955, "r_y3": 459.237, "coord_origin": "TOPLEFT"}, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 310, "label": "text", "bbox": {"l": 464.9, "t": 455.135, "r": 472.877, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 285, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 456.645, "r_x1": 472.877, "r_y1": 456.645, "r_x2": 472.877, "r_y2": 455.135, "r_x3": 464.9, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 311, "label": "text", "bbox": {"l": 464.9, "t": 457.186, "r": 472.498, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 286, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 458.696, "r_x1": 472.498, "r_y1": 458.696, "r_x2": 472.498, "r_y2": 457.186, "r_x3": 464.9, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 312, "label": "text", "bbox": {"l": 478.914, "t": 455.135, "r": 484.707, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 287, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 456.645, "r_x1": 484.707, "r_y1": 456.645, "r_x2": 484.707, "r_y2": 455.135, "r_x3": 478.914, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Closed ", "orig": "Closed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 313, "label": "text", "bbox": {"l": 478.914, "t": 457.186, "r": 486.608, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 288, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 458.696, "r_x1": 486.608, "r_y1": 458.696, "r_x2": 486.608, "r_y2": 457.186, "r_x3": 478.914, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 314, "label": "text", "bbox": {"l": 493.064, "t": 455.135, "r": 504.207, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 289, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 456.645, "r_x1": 504.207, "r_y1": 456.645, "r_x2": 504.207, "r_y2": 455.135, "r_x3": 493.064, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 315, "label": "text", "bbox": {"l": 449.101, "t": 469.689, "r": 548.597, "b": 471.367, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 290, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 471.367, "r_x1": 548.597, "r_y1": 471.367, "r_x2": 548.597, "r_y2": 469.689, "r_x3": 449.101, "r_y3": 469.689, "coord_origin": "TOPLEFT"}, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 316, "label": "text", "bbox": {"l": 449.101, "t": 471.967, "r": 500.082, "b": 473.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 291, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 473.645, "r_x1": 500.082, "r_y1": 473.645, "r_x2": 500.082, "r_y2": 471.967, "r_x3": 449.101, "r_y3": 471.967, "coord_origin": "TOPLEFT"}, "text": "sions, runway slope, arresting gear, and displaced threshold.", "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 317, "label": "text", "bbox": {"l": 449.101, "t": 476.525, "r": 547.826, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 292, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 478.203, "r_x1": 547.826, "r_y1": 478.203, "r_x2": 547.826, "r_y2": 476.525, "r_x3": 449.101, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "orig": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 318, "label": "text", "bbox": {"l": 547.826, "t": 476.525, "r": 548.459, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 293, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 547.826, "r_y0": 478.203, "r_x1": 548.459, "r_y1": 478.203, "r_x2": 548.459, "r_y2": 476.525, "r_x3": 547.826, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 319, "label": "text", "bbox": {"l": 449.101, "t": 478.803, "r": 470.526, "b": 480.481, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 294, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 480.481, "r_x1": 470.526, "r_y1": 480.481, "r_x2": 470.526, "r_y2": 478.803, "r_x3": 449.101, "r_y3": 478.803, "coord_origin": "TOPLEFT"}, "text": "pads may also be shown.", "orig": "pads may also be shown.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 320, "label": "text", "bbox": {"l": 449.101, "t": 483.361, "r": 493.379, "b": 485.039, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 295, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 485.039, "r_x1": 493.379, "r_y1": 485.039, "r_x2": 493.379, "r_y2": 483.361, "r_x3": 449.101, "r_y3": 483.361, "coord_origin": "TOPLEFT"}, "text": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "orig": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 449.101, "t": 487.918, "r": 551.8, "b": 496.432, "coord_origin": "TOPLEFT"}, "confidence": 0.5027860403060913, "cells": [{"index": 296, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 489.596, "r_x1": 549.162, "r_y1": 489.596, "r_x2": 549.162, "r_y2": 487.918, "r_x3": 449.101, "r_y3": 487.918, "coord_origin": "TOPLEFT"}, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 297, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 491.875, "r_x1": 546.909, "r_y1": 491.875, "r_x2": 546.909, "r_y2": 490.197, "r_x3": 449.101, "r_y3": 490.197, "coord_origin": "TOPLEFT"}, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. 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Circling only approaches wil not show a TDZE.", "orig": "the landing surface. 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", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 364, "label": "text", "bbox": {"l": 498.153, "t": 268.149, "r": 544.013, "b": 269.95899999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 365, "label": "text", "bbox": {"l": 498.153, "t": 270.192, "r": 544.12, "b": 272.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 366, "label": "text", "bbox": {"l": 498.153, "t": 272.235, "r": 515.411, "b": 274.04499999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 367, "label": "text", "bbox": {"l": 498.153, "t": 277.663, "r": 505.646, "b": 280.851, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 368, "label": "text", "bbox": {"l": 498.153, "t": 281.996, "r": 543.93, "b": 283.805, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 369, "label": "text", "bbox": {"l": 498.153, "t": 284.039, "r": 545.678, "b": 285.848, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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", "orig": "that are new to you. 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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"label": "text", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "text", "bbox": {"l": 336.693, "t": 192.63099999999997, "r": 439.457, "b": 238.466, "coord_origin": "TOPLEFT"}, "confidence": 0.8027974367141724, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.528, "r_y0": 203.03300000000002, "r_x1": 414.848, "r_y1": 203.03300000000002, "r_x2": 414.848, "r_y2": 192.63099999999997, "r_x3": 361.528, "r_y3": 192.63099999999997, "coord_origin": "TOPLEFT"}, "text": "Peter Staar", "orig": "Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 360.02, "r_y0": 214.55499999999995, "r_x1": 416.129, "r_y1": 214.55499999999995, "r_x2": 416.129, "r_y2": 205.88699999999994, "r_x3": 360.02, "r_y3": 205.88699999999994, "coord_origin": "TOPLEFT"}, "text": "IBM Research", "orig": "IBM Research", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.693, "r_y0": 226.51, "r_x1": 439.457, "r_y1": 226.51, "r_x2": 439.457, "r_y2": 217.84199999999998, "r_x3": 336.693, "r_y3": 217.84199999999998, "coord_origin": "TOPLEFT"}, "text": "Rueschlikon, Switzerland", "orig": "Rueschlikon, Switzerland", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.207, "r_y0": 238.466, "r_x1": 429.943, "r_y1": 238.466, "r_x2": 429.943, "r_y2": 229.798, "r_x3": 346.207, "r_y3": 229.798, "coord_origin": "TOPLEFT"}, "text": "taa@zurich.ibm.com", "orig": "taa@zurich.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 53.798, "t": 248.64099999999996, "r": 111.944, "b": 257.70799999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8876155018806458, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 257.70799999999997, "r_x1": 111.944, "r_y1": 257.70799999999997, "r_x2": 111.944, "r_y2": 248.64099999999996, "r_x3": 53.798, "r_y3": 248.64099999999996, "coord_origin": "TOPLEFT"}, "text": "ABSTRACT", "orig": "ABSTRACT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ABSTRACT"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.467, "t": 263.33500000000004, "r": 295.56, "b": 534.15, "coord_origin": "TOPLEFT"}, "confidence": 0.9823057055473328, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 271.13599999999997, "r_x1": 295.556, "r_y1": 271.13599999999997, "r_x2": 295.556, "r_y2": 263.33500000000004, "r_x3": 53.484, "r_y3": 263.33500000000004, "coord_origin": "TOPLEFT"}, "text": "Accurate document layout analysis is a key requirement for high-", "orig": "Accurate document layout analysis is a key requirement for high-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 282.095, "r_x1": 294.042, "r_y1": 282.095, "r_x2": 294.042, "r_y2": 274.294, "r_x3": 53.798, "r_y3": 274.294, "coord_origin": "TOPLEFT"}, "text": "quality PDF document conversion. With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 293.054, "r_x1": 295.346, "r_y1": 293.054, "r_x2": 295.346, "r_y2": 285.253, "r_x3": 53.798, "r_y3": 285.253, "coord_origin": "TOPLEFT"}, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 304.013, "r_x1": 294.047, "r_y1": 304.013, "r_x2": 294.047, "r_y2": 296.212, "r_x3": 53.798, "r_y3": 296.212, "coord_origin": "TOPLEFT"}, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.972, "r_x1": 294.046, "r_y1": 314.972, "r_x2": 294.046, "r_y2": 307.171, "r_x3": 53.798, "r_y3": 307.171, "coord_origin": "TOPLEFT"}, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 512.232, "r_x1": 295.556, "r_y1": 512.232, "r_x2": 295.556, "r_y2": 504.431, "r_x3": 53.798, "r_y3": 504.431, "coord_origin": "TOPLEFT"}, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 523.191, "r_x1": 294.216, "r_y1": 523.191, "r_x2": 294.216, "r_y2": 515.39, "r_x3": 53.798, "r_y3": 515.39, "coord_origin": "TOPLEFT"}, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 534.15, "r_x1": 212.055, "r_y1": 534.15, "r_x2": 212.055, "r_y2": 526.3489999999999, "r_x3": 53.798, "r_y3": 526.3489999999999, "coord_origin": "TOPLEFT"}, "text": "general-purpose document-layout analysis.", "orig": "general-purpose document-layout analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.798, "t": 551.935, "r": 134.82, "b": 561.002, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 561.002, "r_x1": 134.82, "r_y1": 561.002, "r_x2": 134.82, "r_y2": 551.935, "r_x3": 53.798, "r_y3": 551.935, "coord_origin": "TOPLEFT"}, "text": "CCS CONCEPTS", "orig": "CCS CONCEPTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.798, "t": 564.083, "r": 297.853, "b": 597.29, "coord_origin": "TOPLEFT"}, "confidence": 0.8458839058876038, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.431, "r_x1": 56.945, "r_y1": 574.431, "r_x2": 56.945, "r_y2": 566.63, "r_x3": 53.798, "r_y3": 566.63, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 58.44, "r_y0": 574.307, "r_x1": 142.446, "r_y1": 574.307, "r_x2": 142.446, "r_y2": 566.854, "r_x3": 58.44, "r_y3": 566.854, "coord_origin": "TOPLEFT"}, "text": "Informationsystems", "orig": "Informationsystems", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.938, "r_y0": 575.3720000000001, "r_x1": 153.151, "r_y1": 575.3720000000001, "r_x2": 153.151, "r_y2": 564.083, "r_x3": 143.938, "r_y3": 564.083, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.646, "r_y0": 574.307, "r_x1": 235.46, "r_y1": 574.307, "r_x2": 235.46, "r_y2": 566.854, "r_x3": 154.646, "r_y3": 566.854, "coord_origin": "TOPLEFT"}, "text": "Documentstructure", "orig": "Documentstructure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.457, "r_y0": 574.431, "r_x1": 242.174, "r_y1": 574.431, "r_x2": 242.174, "r_y2": 566.63, "r_x3": 235.457, "r_y3": 566.63, "coord_origin": "TOPLEFT"}, "text": "; 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Loosen the retention screw, then rotate ", "orig": "2. Loosen the retention screw, then rotate ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.594, "r_y0": 345.073, "r_x1": 351.67, "r_y1": 345.073, "r_x2": 351.67, "r_y2": 343.961, "r_x3": 329.594, "r_y3": 343.961, "coord_origin": "TOPLEFT"}, "text": "the adjustment ring to change the ", "orig": "the adjustment ring to change the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.269, "r_y0": 347.876, "r_x1": 351.18, "r_y1": 348.298, "r_x2": 351.18, "r_y2": 347.186, "r_x3": 334.269, "r_y3": 346.764, "coord_origin": "TOPLEFT"}, "text": " re that both the images in", "orig": " re that both the images in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.093, "r_y0": 346.504, "r_x1": 346.332, "r_y1": 346.504, "r_x2": 346.332, "r_y2": 345.393, "r_x3": 329.093, "r_y3": 345.393, "coord_origin": "TOPLEFT"}, "text": " length of the vertical tube.", "orig": " length of the vertical tube.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 348.298, "r_x1": 334.702, "r_y1": 348.298, "r_x2": 334.702, "r_y2": 347.186, "r_x3": 327.83, "r_y3": 347.186, "coord_origin": "TOPLEFT"}, "text": "3. Make su", "orig": "3. Make su", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.253, "r_y0": 258.679, "r_x1": 362.6, "r_y1": 258.49800000000005, "r_x2": 362.6, "r_y2": 255.64099999999996, "r_x3": 327.253, "r_y3": 255.82100000000003, "coord_origin": "TOPLEFT"}, "text": "OPERATION (cont.)", "orig": "OPERATION (cont.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 265.153, "r_x1": 345.844, "r_y1": 265.153, "r_x2": 345.844, "r_y2": 263.88300000000004, "r_x3": 326.88, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "SELECTING OBJECTIVE ", "orig": "SELECTING OBJECTIVE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 266.76300000000003, "r_x1": 340.542, "r_y1": 266.76300000000003, "r_x2": 340.542, "r_y2": 265.49300000000005, "r_x3": 326.88, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "MAGNIFICATION", "orig": "MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 268.37300000000005, "r_x1": 355.17, "r_y1": 268.37300000000005, "r_x2": 355.17, "r_y2": 267.10299999999995, "r_x3": 326.88, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": "1. There are two objectives. The lower ", "orig": "1. There are two objectives. The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. 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", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 154, "label": "text", "bbox": {"l": 326.88, "t": 268.71299999999997, "r": 356.201, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 155, "label": "text", "bbox": {"l": 326.88, "t": 270.32399999999996, "r": 345.801, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 156, "label": "text", "bbox": {"l": 326.88, "t": 271.93399999999997, "r": 353.377, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 157, "label": "text", "bbox": {"l": 326.88, "t": 273.544, "r": 353.892, "b": 274.81399999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 158, "label": "text", "bbox": {"l": 326.88, "t": 275.154, "r": 356.017, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 159, "label": "text", "bbox": {"l": 326.88, "t": 276.765, "r": 351.888, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 160, "label": "text", "bbox": {"l": 326.88, "t": 278.375, "r": 335.467, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 161, "label": "text", "bbox": {"l": 326.88, "t": 281.595, "r": 354.578, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 162, "label": "text", "bbox": {"l": 326.88, "t": 283.205, "r": 335.175, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 163, "label": "text", "bbox": {"l": 326.88, "t": 284.816, "r": 355.748, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 164, "label": "text", "bbox": {"l": 326.88, "t": 286.426, "r": 355.576, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 165, "label": "text", "bbox": {"l": 326.88, "t": 288.036, "r": 355.17, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 166, "label": "text", "bbox": {"l": 326.882, "t": 289.646, "r": 356.021, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 167, "label": "text", "bbox": {"l": 326.882, "t": 291.256, "r": 351.575, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 168, "label": "text", "bbox": {"l": 326.882, "t": 292.867, "r": 336.207, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 169, "label": "text", "bbox": {"l": 326.882, "t": 296.087, "r": 335.394, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 170, "label": "text", "bbox": {"l": 326.882, "t": 297.697, "r": 353.186, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 171, "label": "text", "bbox": {"l": 326.883, "t": 299.308, "r": 354.432, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 172, "label": "text", "bbox": {"l": 326.883, "t": 300.918, "r": 333.328, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 173, "label": "text", "bbox": {"l": 326.883, "t": 302.528, "r": 355.709, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 174, "label": "text", "bbox": {"l": 326.883, "t": 304.138, "r": 356.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 175, "label": "text", "bbox": {"l": 326.883, "t": 305.748, "r": 354.466, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 176, "label": "text", "bbox": {"l": 326.883, "t": 307.359, "r": 357.261, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 177, "label": "text", "bbox": {"l": 326.883, "t": 308.969, "r": 356.449, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 178, "label": "text", "bbox": {"l": 326.883, "t": 310.579, "r": 343.173, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 179, "label": "text", "bbox": {"l": 326.883, "t": 313.799, "r": 344.134, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 180, "label": "text", "bbox": {"l": 326.883, "t": 315.41, "r": 348.502, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 181, "label": "text", "bbox": {"l": 326.883, "t": 317.02, "r": 354.081, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 182, "label": "text", "bbox": {"l": 326.885, "t": 318.63, "r": 354.842, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 183, "label": "text", "bbox": {"l": 326.885, "t": 320.24, "r": 348.021, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 184, "label": "text", "bbox": {"l": 326.885, "t": 321.851, "r": 352.968, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 185, "label": "text", "bbox": {"l": 326.886, "t": 323.461, "r": 357.529, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 186, "label": "text", "bbox": {"l": 326.886, "t": 325.071, "r": 350.138, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 187, "label": "text", "bbox": {"l": 326.886, "t": 326.681, "r": 353.141, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 188, "label": "text", "bbox": {"l": 326.886, "t": 328.291, "r": 336.892, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 189, "label": "text", "bbox": {"l": 358.42, "t": 263.88300000000004, "r": 366.933, "b": 265.153, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 190, "label": "text", "bbox": {"l": 358.42, "t": 265.49300000000005, "r": 388.997, "b": 266.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 191, "label": "text", "bbox": {"l": 358.42, "t": 267.10299999999995, "r": 384.59, "b": 268.37300000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 192, "label": "text", "bbox": {"l": 358.422, "t": 268.71299999999997, "r": 385.698, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 193, "label": "text", "bbox": {"l": 358.423, "t": 270.32399999999996, "r": 385.612, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 194, "label": "text", "bbox": {"l": 358.423, "t": 271.93399999999997, "r": 385.389, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 195, "label": "text", "bbox": {"l": 358.423, "t": 275.154, "r": 377.35, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 196, "label": "text", "bbox": {"l": 358.423, "t": 276.765, "r": 387.359, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 197, "label": "text", "bbox": {"l": 358.423, "t": 278.375, "r": 387.748, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 198, "label": "text", "bbox": {"l": 358.423, "t": 279.985, "r": 364.169, "b": 281.255, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 199, "label": "text", "bbox": {"l": 358.423, "t": 281.595, "r": 388.266, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 200, "label": "text", "bbox": {"l": 358.423, "t": 283.205, "r": 381.567, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 201, "label": "text", "bbox": {"l": 358.423, "t": 284.816, "r": 387.673, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 202, "label": "text", "bbox": {"l": 358.425, "t": 286.426, "r": 382.771, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 203, "label": "text", "bbox": {"l": 358.425, "t": 288.036, "r": 367.987, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 204, "label": "text", "bbox": {"l": 358.425, "t": 289.646, "r": 387.601, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 205, "label": "text", "bbox": {"l": 358.425, "t": 291.256, "r": 389.075, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 206, "label": "text", "bbox": {"l": 358.425, "t": 292.867, "r": 373.137, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 207, "label": "text", "bbox": {"l": 358.425, "t": 296.087, "r": 381.745, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 208, "label": "text", "bbox": {"l": 358.425, "t": 297.697, "r": 389.01, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 209, "label": "text", "bbox": {"l": 358.425, "t": 299.308, "r": 382.733, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 210, "label": "text", "bbox": {"l": 358.425, "t": 300.918, "r": 388.701, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 211, "label": "text", "bbox": {"l": 358.425, "t": 302.528, "r": 385.147, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 212, "label": "text", "bbox": {"l": 358.425, "t": 304.138, "r": 364.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 213, "label": "text", "bbox": {"l": 358.425, "t": 305.748, "r": 387.708, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 214, "label": "text", "bbox": {"l": 358.425, "t": 307.359, "r": 387.857, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 215, "label": "text", "bbox": {"l": 358.425, "t": 308.969, "r": 385.414, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 216, "label": "text", "bbox": {"l": 358.425, "t": 310.579, "r": 382.561, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 217, "label": "text", "bbox": {"l": 358.426, "t": 312.189, "r": 386.568, "b": 313.459, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 218, "label": "text", "bbox": {"l": 358.426, "t": 313.799, "r": 385.703, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 219, "label": "text", "bbox": {"l": 358.426, "t": 315.41, "r": 366.744, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 220, "label": "text", "bbox": {"l": 358.426, "t": 317.02, "r": 384.99, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 221, "label": "text", "bbox": {"l": 358.426, "t": 318.63, "r": 386.867, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 222, "label": "text", "bbox": {"l": 358.426, "t": 320.24, "r": 385.949, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 223, "label": "text", "bbox": {"l": 358.426, "t": 321.851, "r": 387.137, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 224, "label": "text", "bbox": {"l": 358.426, "t": 323.461, "r": 384.369, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 225, "label": "text", "bbox": {"l": 358.426, "t": 325.071, "r": 386.093, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 226, "label": "text", "bbox": {"l": 358.426, "t": 326.681, "r": 386.352, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 227, "label": "text", "bbox": {"l": 358.426, "t": 328.291, "r": 383.28, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 228, "label": "text", "bbox": {"l": 358.426, "t": 329.902, "r": 382.634, "b": 331.172, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 229, "label": "text", "bbox": {"l": 358.426, "t": 333.122, "r": 375.677, "b": 334.392, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 230, "label": "text", "bbox": {"l": 358.426, "t": 334.732, "r": 386.737, "b": 336.002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, "r_x3": 358.426, "r_y3": 334.732, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 231, "label": "text", "bbox": {"l": 358.426, "t": 336.342, "r": 372.014, "b": 337.613, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 232, "label": "text", "bbox": {"l": 358.426, "t": 337.953, "r": 385.634, "b": 339.223, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 233, "label": "text", "bbox": {"l": 358.426, "t": 339.563, "r": 386.395, "b": 340.833, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 340.833, "r_x1": 386.395, "r_y1": 340.833, "r_x2": 386.395, "r_y2": 339.563, "r_x3": 358.426, "r_y3": 339.563, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 234, "label": "text", "bbox": {"l": 358.426, "t": 341.173, "r": 379.572, "b": 342.443, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 342.443, "r_x1": 379.572, "r_y1": 342.443, "r_x2": 379.572, "r_y2": 341.173, "r_x3": 358.426, "r_y3": 341.173, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 235, "label": "text", "bbox": {"l": 358.427, "t": 342.783, "r": 384.511, "b": 344.053, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.427, "r_y0": 344.053, "r_x1": 384.511, "r_y1": 344.053, "r_x2": 384.511, "r_y2": 342.783, "r_x3": 358.427, "r_y3": 342.783, "coord_origin": "TOPLEFT"}, "text": "3. 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emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 296, "label": "text", "bbox": {"l": 449.101, "t": 427.727, "r": 522.034, "b": 429.405, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 271, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 429.405, "r_x1": 522.034, "r_y1": 429.405, "r_x2": 522.034, "r_y2": 427.727, "r_x3": 449.101, "r_y3": 427.727, "coord_origin": "TOPLEFT"}, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 297, "label": "text", "bbox": {"l": 449.101, "t": 430.005, "r": 524.672, "b": 431.683, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 272, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 431.683, "r_x1": 524.672, "r_y1": 431.683, "r_x2": 524.672, "r_y2": 430.005, "r_x3": 449.101, "r_y3": 430.005, "coord_origin": "TOPLEFT"}, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 298, "label": "text", "bbox": {"l": 449.101, "t": 432.284, "r": 527.172, "b": 433.962, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 273, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 433.962, "r_x1": 527.172, "r_y1": 433.962, "r_x2": 527.172, "r_y2": 432.284, "r_x3": 449.101, "r_y3": 432.284, "coord_origin": "TOPLEFT"}, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 299, "label": "text", "bbox": {"l": 449.101, "t": 434.563, "r": 502.395, "b": 436.241, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 274, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 436.241, "r_x1": 502.395, "r_y1": 436.241, "r_x2": 502.395, "r_y2": 434.563, "r_x3": 449.101, "r_y3": 434.563, "coord_origin": "TOPLEFT"}, "text": "dimensions (length and width) are shown for al active runways.", "orig": "dimensions (length and width) are shown for al active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 300, "label": "text", "bbox": {"l": 449.101, "t": 439.12, "r": 512.927, "b": 440.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 275, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 440.798, "r_x1": 512.927, "r_y1": 440.798, "r_x2": 512.927, "r_y2": 439.12, "r_x3": 449.101, "r_y3": 439.12, "coord_origin": "TOPLEFT"}, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 301, "label": "text", "bbox": {"l": 449.955, "t": 444.393, "r": 460.023, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 276, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 445.903, "r_x1": 460.023, "r_y1": 445.903, "r_x2": 460.023, "r_y2": 444.393, "r_x3": 449.955, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 302, "label": "text", "bbox": {"l": 464.9, "t": 444.393, "r": 473.988, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 277, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 445.903, "r_x1": 473.988, "r_y1": 445.903, "r_x2": 473.988, "r_y2": 444.393, "r_x3": 464.9, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 303, "label": "text", "bbox": {"l": 464.9, "t": 446.444, "r": 474.967, "b": 447.954, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 278, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 447.954, "r_x1": 474.967, "r_y1": 447.954, "r_x2": 474.967, "r_y2": 446.444, "r_x3": 464.9, "r_y3": 446.444, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 304, "label": "text", "bbox": {"l": 478.914, "t": 444.393, "r": 489.457, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 279, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 445.903, "r_x1": 489.457, "r_y1": 445.903, "r_x2": 489.457, "r_y2": 444.393, "r_x3": 478.914, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 305, "label": "text", "bbox": {"l": 493.064, "t": 444.393, "r": 505.031, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 280, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 445.903, "r_x1": 505.031, "r_y1": 445.903, "r_x2": 505.031, "r_y2": 444.393, "r_x3": 493.064, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 306, "label": "text", "bbox": {"l": 509.581, "t": 444.393, "r": 524.302, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 281, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.581, "r_y0": 445.903, "r_x1": 524.302, "r_y1": 445.903, "r_x2": 524.302, "r_y2": 444.393, "r_x3": 509.581, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 307, "label": "text", "bbox": {"l": 449.955, "t": 455.135, "r": 458.314, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 282, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 456.645, "r_x1": 458.314, "r_y1": 456.645, "r_x2": 458.314, "r_y2": 455.135, "r_x3": 449.955, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 308, "label": "text", "bbox": {"l": 449.955, "t": 457.186, "r": 461.921, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 283, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 458.696, "r_x1": 461.921, "r_y1": 458.696, "r_x2": 461.921, "r_y2": 457.186, "r_x3": 449.955, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 309, "label": "text", "bbox": {"l": 449.955, "t": 459.237, "r": 457.08, "b": 460.747, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 284, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 460.747, "r_x1": 457.08, "r_y1": 460.747, "r_x2": 457.08, "r_y2": 459.237, "r_x3": 449.955, "r_y3": 459.237, "coord_origin": "TOPLEFT"}, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 310, "label": "text", "bbox": {"l": 464.9, "t": 455.135, "r": 472.877, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 285, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 456.645, "r_x1": 472.877, "r_y1": 456.645, "r_x2": 472.877, "r_y2": 455.135, "r_x3": 464.9, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 311, "label": "text", "bbox": {"l": 464.9, "t": 457.186, "r": 472.498, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 286, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 458.696, "r_x1": 472.498, "r_y1": 458.696, "r_x2": 472.498, "r_y2": 457.186, "r_x3": 464.9, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 312, "label": "text", "bbox": {"l": 478.914, "t": 455.135, "r": 484.707, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 287, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 456.645, "r_x1": 484.707, "r_y1": 456.645, "r_x2": 484.707, "r_y2": 455.135, "r_x3": 478.914, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Closed ", "orig": "Closed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 313, "label": "text", "bbox": {"l": 478.914, "t": 457.186, "r": 486.608, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 288, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 458.696, "r_x1": 486.608, "r_y1": 458.696, "r_x2": 486.608, "r_y2": 457.186, "r_x3": 478.914, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 314, "label": "text", "bbox": {"l": 493.064, "t": 455.135, "r": 504.207, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 289, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 456.645, "r_x1": 504.207, "r_y1": 456.645, "r_x2": 504.207, "r_y2": 455.135, "r_x3": 493.064, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 315, "label": "text", "bbox": {"l": 449.101, "t": 469.689, "r": 548.597, "b": 471.367, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 290, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 471.367, "r_x1": 548.597, "r_y1": 471.367, "r_x2": 548.597, "r_y2": 469.689, "r_x3": 449.101, "r_y3": 469.689, "coord_origin": "TOPLEFT"}, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 316, "label": "text", "bbox": {"l": 449.101, "t": 471.967, "r": 500.082, "b": 473.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 291, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 473.645, "r_x1": 500.082, "r_y1": 473.645, "r_x2": 500.082, "r_y2": 471.967, "r_x3": 449.101, "r_y3": 471.967, "coord_origin": "TOPLEFT"}, "text": "sions, runway slope, arresting gear, and displaced threshold.", "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 317, "label": "text", "bbox": {"l": 449.101, "t": 476.525, "r": 547.826, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 292, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 478.203, "r_x1": 547.826, "r_y1": 478.203, "r_x2": 547.826, "r_y2": 476.525, "r_x3": 449.101, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "orig": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 318, "label": "text", "bbox": {"l": 547.826, "t": 476.525, "r": 548.459, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 293, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 547.826, "r_y0": 478.203, "r_x1": 548.459, "r_y1": 478.203, "r_x2": 548.459, "r_y2": 476.525, "r_x3": 547.826, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 319, "label": "text", "bbox": {"l": 449.101, "t": 478.803, "r": 470.526, "b": 480.481, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 294, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 480.481, "r_x1": 470.526, "r_y1": 480.481, "r_x2": 470.526, "r_y2": 478.803, "r_x3": 449.101, "r_y3": 478.803, "coord_origin": "TOPLEFT"}, "text": "pads may also be shown.", "orig": "pads may also be shown.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 320, "label": "text", "bbox": {"l": 449.101, "t": 483.361, "r": 493.379, "b": 485.039, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 295, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 485.039, "r_x1": 493.379, "r_y1": 485.039, "r_x2": 493.379, "r_y2": 483.361, "r_x3": 449.101, "r_y3": 483.361, "coord_origin": "TOPLEFT"}, "text": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "orig": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 449.101, "t": 487.918, "r": 551.8, "b": 496.432, "coord_origin": "TOPLEFT"}, "confidence": 0.5027860403060913, "cells": [{"index": 296, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 489.596, "r_x1": 549.162, "r_y1": 489.596, "r_x2": 549.162, "r_y2": 487.918, "r_x3": 449.101, "r_y3": 487.918, "coord_origin": "TOPLEFT"}, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 297, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 491.875, "r_x1": 546.909, "r_y1": 491.875, "r_x2": 546.909, "r_y2": 490.197, "r_x3": 449.101, "r_y3": 490.197, "coord_origin": "TOPLEFT"}, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. 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Circling only approaches wil not show a TDZE.", "orig": "the landing surface. 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", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 364, "label": "text", "bbox": {"l": 498.153, "t": 268.149, "r": 544.013, "b": 269.95899999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 365, "label": "text", "bbox": {"l": 498.153, "t": 270.192, "r": 544.12, "b": 272.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 366, "label": "text", "bbox": {"l": 498.153, "t": 272.235, "r": 515.411, "b": 274.04499999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 367, "label": "text", "bbox": {"l": 498.153, "t": 277.663, "r": 505.646, "b": 280.851, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 368, "label": "text", "bbox": {"l": 498.153, "t": 281.996, "r": 543.93, "b": 283.805, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 369, "label": "text", "bbox": {"l": 498.153, "t": 284.039, "r": 545.678, "b": 285.848, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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", "orig": "that are new to you. 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ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}], "headers": [{"label": "page_header", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 18.34, "t": 165.0, "r": 36.34, "b": 555.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8537883758544922, "cells": [{"index": 460, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "orig": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. 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The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 625.734, "r_x1": 167.917, "r_y1": 625.734, "r_x2": 167.917, "r_y2": 617.826, "r_x3": 78.207, "r_y3": 617.826, "coord_origin": "TOPLEFT"}, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.335, "r_y0": 625.69, "r_x1": 170.493, "r_y1": 625.69, "r_x2": 170.493, "r_y2": 617.889, "r_x3": 168.335, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.981, "r_y0": 625.69, "r_x1": 294.263, "r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 647.6510000000001, "r_x1": 143.517, "r_y1": 647.6510000000001, "r_x2": 143.517, "r_y2": 639.7429999999999, "r_x3": 78.207, "r_y3": 639.7429999999999, "coord_origin": "TOPLEFT"}, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.02, "r_y0": 647.607, "r_x1": 146.178, "r_y1": 647.607, "r_x2": 146.178, "r_y2": 639.806, "r_x3": 144.02, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 117.55700000000002, "r_x1": 487.253, "r_y1": 117.55700000000002, "r_x2": 487.253, "r_y2": 109.649, "r_x3": 342.364, "r_y3": 109.649, "coord_origin": "TOPLEFT"}, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.759, "r_y0": 117.51300000000003, "r_x1": 489.867, "r_y1": 117.51300000000003, "r_x2": 489.867, "r_y2": 109.71199999999999, "r_x3": 487.759, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 542.158, "r_y0": 642.554, "r_x1": 559.099, "r_y1": 642.554, "r_x2": 559.099, "r_y2": 634.646, "r_x3": 542.158, "r_y3": 634.646, "coord_origin": "TOPLEFT"}, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 653.513, "r_x1": 331.863, "r_y1": 653.513, "r_x2": 331.863, "r_y2": 645.605, "r_x3": 317.955, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.863, "r_y0": 653.469, "r_x1": 333.84, "r_y1": 653.469, "r_x2": 333.84, "r_y2": 645.668, "r_x3": 331.863, 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370.254, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "Formula List-item", "orig": "Formula List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.055, "r_y0": 653.469, "r_x1": 402.032, "r_y1": 653.469, "r_x2": 402.032, "r_y2": 645.668, "r_x3": 400.055, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.195, "r_y0": 653.469, "r_x1": 438.172, "r_y1": 653.469, "r_x2": 438.172, "r_y2": 645.668, "r_x3": 436.195, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.396, "r_y0": 653.513, "r_x1": 480.61, "r_y1": 653.513, "r_x2": 480.61, 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{"r_x0": 449.62, "r_y0": 664.428, "r_x1": 558.202, "r_y1": 664.428, "r_x2": 558.202, "r_y2": 656.627, "r_x3": 449.62, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": "Our reasoning for picking this", "orig": "Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.387, "r_x1": 472.222, "r_y1": 675.387, "r_x2": 472.222, "r_y2": 667.586, "r_x3": 317.955, "r_y3": 667.586, "coord_origin": "TOPLEFT"}, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.3050000000001, "r_x1": 558.199, "r_y1": 697.3050000000001, "r_x2": 558.199, "r_y2": 689.504, "r_x3": 317.955, "r_y3": 689.504, "coord_origin": "TOPLEFT"}, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.264, "r_x1": 558.204, "r_y1": 708.264, "r_x2": 558.204, "r_y2": 700.463, "r_x3": 317.955, "r_y3": 700.463, "coord_origin": "TOPLEFT"}, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 156.529, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 95.86500000000001, "r_x1": 156.529, "r_y1": 95.86500000000001, "r_x2": 156.529, "r_y2": 86.798, "r_x3": 70.314, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.529, "t": 111.41399999999999, "r": 303.017, "b": 228.80399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 130.17399999999998, "r_x1": 295.557, "r_y1": 130.17399999999998, "r_x2": 295.557, "r_y2": 122.37300000000005, "r_x3": 53.529, "r_y3": 122.37300000000005, "coord_origin": "TOPLEFT"}, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 141.13300000000004, "r_x1": 294.046, "r_y1": 141.13300000000004, "r_x2": 294.046, "r_y2": 133.332, "r_x3": 53.798, "r_y3": 133.332, "coord_origin": "TOPLEFT"}, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 152.091, "r_x1": 294.047, "r_y1": 152.091, "r_x2": 294.047, "r_y2": 144.28999999999996, "r_x3": 53.798, "r_y3": 144.28999999999996, "coord_origin": "TOPLEFT"}, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.529, "t": 231.962, "r": 295.564, "b": 502.776, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.592, "t": 505.934, "r": 295.564, "b": 579.489, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.9490000000001, "r": 295.562, "b": 614.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.826, "r": 294.263, "b": 636.648, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 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"r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.7429999999999, "r": 294.684, "b": 669.525, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 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0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.62, "r": 295.564, "b": 691.443, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 216.027, "b": 708.629, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 106.55399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.649, "r": 559.721, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, 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":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.623, "t": 168.40599999999995, "r": 559.19, "b": 220.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.731, "t": 223.20100000000002, "r": 559.582, "b": 307.714, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 421.744, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.716, "b": 464.153, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 467.311, "r": 559.186, "b": 551.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 477.457, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 559.713, "b": 675.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. 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Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 156.529, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 95.86500000000001, "r_x1": 156.529, "r_y1": 95.86500000000001, "r_x2": 156.529, "r_y2": 86.798, "r_x3": 70.314, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.529, "t": 111.41399999999999, "r": 303.017, "b": 228.80399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 130.17399999999998, "r_x1": 295.557, "r_y1": 130.17399999999998, "r_x2": 295.557, "r_y2": 122.37300000000005, "r_x3": 53.529, "r_y3": 122.37300000000005, "coord_origin": "TOPLEFT"}, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 141.13300000000004, "r_x1": 294.046, "r_y1": 141.13300000000004, "r_x2": 294.046, "r_y2": 133.332, "r_x3": 53.798, "r_y3": 133.332, "coord_origin": "TOPLEFT"}, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 152.091, "r_x1": 294.047, "r_y1": 152.091, "r_x2": 294.047, "r_y2": 144.28999999999996, "r_x3": 53.798, "r_y3": 144.28999999999996, "coord_origin": "TOPLEFT"}, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.529, "t": 231.962, "r": 295.564, "b": 502.776, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.592, "t": 505.934, "r": 295.564, "b": 579.489, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.9490000000001, "r": 295.562, "b": 614.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.826, "r": 294.263, "b": 636.648, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 625.734, "r_x1": 167.917, "r_y1": 625.734, "r_x2": 167.917, "r_y2": 617.826, "r_x3": 78.207, "r_y3": 617.826, "coord_origin": "TOPLEFT"}, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.335, "r_y0": 625.69, "r_x1": 170.493, "r_y1": 625.69, "r_x2": 170.493, "r_y2": 617.889, "r_x3": 168.335, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.981, "r_y0": 625.69, "r_x1": 294.263, "r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.7429999999999, "r": 294.684, "b": 669.525, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 647.6510000000001, "r_x1": 143.517, "r_y1": 647.6510000000001, "r_x2": 143.517, "r_y2": 639.7429999999999, "r_x3": 78.207, "r_y3": 639.7429999999999, "coord_origin": "TOPLEFT"}, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.02, "r_y0": 647.607, "r_x1": 146.178, "r_y1": 647.607, "r_x2": 146.178, "r_y2": 639.806, "r_x3": 144.02, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.62, "r": 295.564, "b": 691.443, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 216.027, "b": 708.629, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 106.55399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.649, "r": 559.721, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 117.55700000000002, "r_x1": 487.253, "r_y1": 117.55700000000002, "r_x2": 487.253, "r_y2": 109.649, "r_x3": 342.364, "r_y3": 109.649, "coord_origin": "TOPLEFT"}, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.759, "r_y0": 117.51300000000003, "r_x1": 489.867, "r_y1": 117.51300000000003, "r_x2": 489.867, "r_y2": 109.71199999999999, "r_x3": 487.759, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.623, "t": 168.40599999999995, "r": 559.19, "b": 220.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.731, "t": 223.20100000000002, "r": 559.582, "b": 307.714, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 421.744, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.716, "b": 464.153, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 467.311, "r": 559.186, "b": 551.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 477.457, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 559.713, "b": 675.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 542.158, "r_y0": 642.554, "r_x1": 559.099, "r_y1": 642.554, "r_x2": 559.099, "r_y2": 634.646, "r_x3": 542.158, "r_y3": 634.646, "coord_origin": "TOPLEFT"}, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 653.513, "r_x1": 331.863, "r_y1": 653.513, "r_x2": 331.863, "r_y2": 645.605, "r_x3": 317.955, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.863, "r_y0": 653.469, "r_x1": 333.84, "r_y1": 653.469, "r_x2": 333.84, "r_y2": 645.668, "r_x3": 331.863, 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414.343, "r_y0": 664.428, "r_x1": 427.568, "r_y1": 664.428, "r_x2": 427.568, "r_y2": 656.627, "r_x3": 414.343, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 429.744, "r_y0": 664.472, "r_x1": 445.508, "r_y1": 664.472, "r_x2": 445.508, "r_y2": 656.564, "r_x3": 429.744, "r_y3": 656.564, "coord_origin": "TOPLEFT"}, "text": "Title", "orig": "Title", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.508, "r_y0": 664.428, "r_x1": 447.441, "r_y1": 664.428, "r_x2": 447.441, "r_y2": 656.627, "r_x3": 445.508, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.62, "r_y0": 664.428, "r_x1": 558.202, "r_y1": 664.428, "r_x2": 558.202, "r_y2": 656.627, "r_x3": 449.62, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": "Our reasoning for picking this", "orig": "Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.387, "r_x1": 472.222, "r_y1": 675.387, "r_x2": 472.222, "r_y2": 667.586, "r_x3": 317.955, "r_y3": 667.586, "coord_origin": "TOPLEFT"}, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 678.545, "r": 558.204, "b": 708.264, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.3050000000001, "r_x1": 558.199, "r_y1": 697.3050000000001, "r_x2": 558.199, "r_y2": 689.504, "r_x3": 317.955, "r_y3": 689.504, "coord_origin": "TOPLEFT"}, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.264, "r_x1": 558.204, "r_y1": 708.264, "r_x2": 558.204, "r_y2": 700.463, "r_x3": 317.955, "r_y3": 700.463, "coord_origin": "TOPLEFT"}, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 156.529, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 95.86500000000001, "r_x1": 156.529, "r_y1": 95.86500000000001, "r_x2": 156.529, "r_y2": 86.798, "r_x3": 70.314, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.529, "t": 111.41399999999999, "r": 303.017, "b": 228.80399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 130.17399999999998, "r_x1": 295.557, "r_y1": 130.17399999999998, "r_x2": 295.557, "r_y2": 122.37300000000005, "r_x3": 53.529, "r_y3": 122.37300000000005, "coord_origin": "TOPLEFT"}, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 141.13300000000004, "r_x1": 294.046, "r_y1": 141.13300000000004, "r_x2": 294.046, "r_y2": 133.332, "r_x3": 53.798, "r_y3": 133.332, "coord_origin": "TOPLEFT"}, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 152.091, "r_x1": 294.047, "r_y1": 152.091, "r_x2": 294.047, "r_y2": 144.28999999999996, "r_x3": 53.798, "r_y3": 144.28999999999996, "coord_origin": "TOPLEFT"}, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.529, "t": 231.962, "r": 295.564, "b": 502.776, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.592, "t": 505.934, "r": 295.564, "b": 579.489, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.9490000000001, "r": 295.562, "b": 614.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.826, "r": 294.263, "b": 636.648, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 625.734, "r_x1": 167.917, "r_y1": 625.734, "r_x2": 167.917, "r_y2": 617.826, "r_x3": 78.207, "r_y3": 617.826, "coord_origin": "TOPLEFT"}, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.335, "r_y0": 625.69, "r_x1": 170.493, "r_y1": 625.69, "r_x2": 170.493, "r_y2": 617.889, "r_x3": 168.335, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.981, "r_y0": 625.69, "r_x1": 294.263, "r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.7429999999999, "r": 294.684, "b": 669.525, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 647.6510000000001, "r_x1": 143.517, "r_y1": 647.6510000000001, "r_x2": 143.517, "r_y2": 639.7429999999999, "r_x3": 78.207, "r_y3": 639.7429999999999, "coord_origin": "TOPLEFT"}, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.02, "r_y0": 647.607, "r_x1": 146.178, "r_y1": 647.607, "r_x2": 146.178, "r_y2": 639.806, "r_x3": 144.02, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.62, "r": 295.564, "b": 691.443, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 216.027, "b": 708.629, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 106.55399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.649, "r": 559.721, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 117.55700000000002, "r_x1": 487.253, "r_y1": 117.55700000000002, "r_x2": 487.253, "r_y2": 109.649, "r_x3": 342.364, "r_y3": 109.649, "coord_origin": "TOPLEFT"}, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.759, "r_y0": 117.51300000000003, "r_x1": 489.867, "r_y1": 117.51300000000003, "r_x2": 489.867, "r_y2": 109.71199999999999, "r_x3": 487.759, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.623, "t": 168.40599999999995, "r": 559.19, "b": 220.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.731, "t": 223.20100000000002, "r": 559.582, "b": 307.714, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 421.744, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.716, "b": 464.153, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 467.311, "r": 559.186, "b": 551.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 477.457, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 559.713, "b": 675.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 542.158, "r_y0": 642.554, "r_x1": 559.099, "r_y1": 642.554, "r_x2": 559.099, "r_y2": 634.646, "r_x3": 542.158, "r_y3": 634.646, "coord_origin": "TOPLEFT"}, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 653.513, "r_x1": 331.863, "r_y1": 653.513, "r_x2": 331.863, "r_y2": 645.605, "r_x3": 317.955, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.863, "r_y0": 653.469, "r_x1": 333.84, "r_y1": 653.469, "r_x2": 333.84, "r_y2": 645.668, "r_x3": 331.863, 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370.254, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "Formula List-item", "orig": "Formula List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.055, "r_y0": 653.469, "r_x1": 402.032, "r_y1": 653.469, "r_x2": 402.032, "r_y2": 645.668, "r_x3": 400.055, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.195, "r_y0": 653.469, "r_x1": 438.172, "r_y1": 653.469, "r_x2": 438.172, "r_y2": 645.668, "r_x3": 436.195, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.396, "r_y0": 653.513, "r_x1": 480.61, "r_y1": 653.513, "r_x2": 480.61, 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{"r_x0": 449.62, "r_y0": 664.428, "r_x1": 558.202, "r_y1": 664.428, "r_x2": 558.202, "r_y2": 656.627, "r_x3": 449.62, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": "Our reasoning for picking this", "orig": "Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.387, "r_x1": 472.222, "r_y1": 675.387, "r_x2": 472.222, "r_y2": 667.586, "r_x3": 317.955, "r_y3": 667.586, "coord_origin": "TOPLEFT"}, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 678.545, "r": 558.204, "b": 708.264, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.3050000000001, "r_x1": 558.199, "r_y1": 697.3050000000001, "r_x2": 558.199, "r_y2": 689.504, "r_x3": 317.955, "r_y3": 689.504, "coord_origin": "TOPLEFT"}, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.264, "r_x1": 558.204, "r_y1": 708.264, "r_x2": 558.204, "r_y2": 700.463, "r_x3": 317.955, "r_y3": 700.463, "coord_origin": "TOPLEFT"}, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", 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"coord_origin": "TOPLEFT"}, "text": "Manuals", "orig": "Manuals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.407, "r_y0": 179.30899999999997, "r_x1": 209.669, "r_y1": 179.30899999999997, "r_x2": 209.669, "r_y2": 172.57100000000003, "r_x3": 194.407, "r_y3": 172.57100000000003, "coord_origin": "TOPLEFT"}, "text": "21%", "orig": "21%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 244.33900000000006, "r_x1": 294.044, "r_y1": 244.33900000000006, "r_x2": 294.044, "r_y2": 236.88599999999997, "r_x3": 53.798, "r_y3": 236.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 255.298, "r_x1": 96.756, "r_y1": 255.298, "r_x2": 96.756, "r_y2": 247.84500000000003, "r_x3": 53.798, "r_y3": 247.84500000000003, "coord_origin": "TOPLEFT"}, "text": "categories.", "orig": "categories.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 300.994, "r_x1": 294.049, "r_y1": 300.994, "r_x2": 294.049, "r_y2": 293.193, "r_x3": 53.798, "r_y3": 293.193, "coord_origin": "TOPLEFT"}, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 311.953, "r_x1": 98.881, "r_y1": 311.953, "r_x2": 98.881, "r_y2": 304.152, "r_x3": 53.798, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.071, "r_y0": 310.088, "r_x1": 104.774, "r_y1": 310.088, "r_x2": 104.774, "r_y2": 306.08, "r_x3": 99.071, "r_y3": 306.08, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.464, "r_y0": 311.953, "r_x1": 294.262, "r_y1": 311.953, "r_x2": 294.262, "r_y2": 304.152, "r_x3": 107.464, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "10 pages) with technical content, dense in complex", "orig": "10 pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 322.912, "r_x1": 294.047, "r_y1": 322.912, "r_x2": 294.047, "r_y2": 315.111, "r_x3": 53.798, "r_y3": 315.111, "coord_origin": "TOPLEFT"}, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, 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For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. 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"coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 300.994, "r_x1": 294.049, "r_y1": 300.994, "r_x2": 294.049, "r_y2": 293.193, "r_x3": 53.798, "r_y3": 293.193, "coord_origin": "TOPLEFT"}, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 311.953, "r_x1": 98.881, "r_y1": 311.953, "r_x2": 98.881, "r_y2": 304.152, "r_x3": 53.798, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.071, "r_y0": 310.088, "r_x1": 104.774, "r_y1": 310.088, "r_x2": 104.774, "r_y2": 306.08, "r_x3": 99.071, "r_y3": 306.08, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.464, "r_y0": 311.953, "r_x1": 294.262, "r_y1": 311.953, "r_x2": 294.262, "r_y2": 304.152, "r_x3": 107.464, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "10 pages) with technical content, dense in complex", "orig": "10 pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 322.912, "r_x1": 294.047, "r_y1": 322.912, "r_x2": 294.047, "r_y2": 315.111, "r_x3": 53.798, "r_y3": 315.111, "coord_origin": "TOPLEFT"}, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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"coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.367, "r_y0": 399.624, "r_x1": 295.557, "r_y1": 399.624, "r_x2": 295.557, "r_y2": 391.823, "r_x3": 219.367, "r_y3": 391.823, "coord_origin": "TOPLEFT"}, "text": "Each document cate-", "orig": "Each document cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 410.583, "r_x1": 294.045, "r_y1": 410.583, "r_x2": 294.045, "r_y2": 402.782, "r_x3": 53.798, "r_y3": 402.782, "coord_origin": "TOPLEFT"}, "text": "gory was sourced from various repositories. For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 512.371, "r": 295.562, "b": 607.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 611.001, "r": 295.564, "b": 684.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 195.79, "b": 708.059, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 87.79399999999998, "r": 559.192, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 164.50599999999997, "r": 558.438, "b": 270.937, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.942, "t": 274.095, "r": 559.722, "b": 588.746, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 470.213, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 559.714, "b": 705.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. 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"coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 300.994, "r_x1": 294.049, "r_y1": 300.994, "r_x2": 294.049, "r_y2": 293.193, "r_x3": 53.798, "r_y3": 293.193, "coord_origin": "TOPLEFT"}, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). 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Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. 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For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 512.371, "r": 295.562, "b": 607.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 611.001, "r": 295.564, "b": 684.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 195.79, "b": 708.059, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 87.79399999999998, "r": 559.192, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 164.50599999999997, "r": 558.438, "b": 270.937, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.942, "t": 274.095, "r": 559.722, "b": 588.746, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 470.213, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 559.714, "b": 705.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. In phase four,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "body": [{"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.113, "r_y0": 141.27499999999998, "r_x1": 262.976, "r_y1": 141.27499999999998, "r_x2": 262.976, "r_y2": 134.53599999999994, "r_x3": 237.113, "r_y3": 134.53599999999994, "coord_origin": "TOPLEFT"}, "text": "Patents", "orig": "Patents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.879, "r_y0": 148.64999999999998, "r_x1": 213.9, "r_y1": 148.64999999999998, "r_x2": 213.9, "r_y2": 141.91099999999994, "r_x3": 202.879, "r_y3": 141.91099999999994, "coord_origin": "TOPLEFT"}, "text": "8%", "orig": "8%", "text_direction": 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Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 255.298, "r_x1": 96.756, "r_y1": 255.298, "r_x2": 96.756, "r_y2": 247.84500000000003, "r_x3": 53.798, "r_y3": 247.84500000000003, "coord_origin": "TOPLEFT"}, "text": "categories.", "orig": "categories.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 282.234, "r": 294.274, "b": 366.747, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 300.994, "r_x1": 294.049, "r_y1": 300.994, "r_x2": 294.049, "r_y2": 293.193, "r_x3": 53.798, "r_y3": 293.193, "coord_origin": "TOPLEFT"}, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 311.953, "r_x1": 98.881, "r_y1": 311.953, "r_x2": 98.881, "r_y2": 304.152, "r_x3": 53.798, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.071, "r_y0": 310.088, "r_x1": 104.774, "r_y1": 310.088, "r_x2": 104.774, "r_y2": 306.08, "r_x3": 99.071, "r_y3": 306.08, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.464, "r_y0": 311.953, "r_x1": 294.262, "r_y1": 311.953, "r_x2": 294.262, "r_y2": 304.152, "r_x3": 107.464, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "10 pages) with technical content, dense in complex", "orig": "10 pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 322.912, "r_x1": 294.047, "r_y1": 322.912, "r_x2": 294.047, "r_y2": 315.111, "r_x3": 53.798, "r_y3": 315.111, "coord_origin": "TOPLEFT"}, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.574, "t": 369.905, "r": 295.56, "b": 509.213, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 388.665, "r_x1": 105.905, "r_y1": 388.665, "r_x2": 105.905, "r_y2": 380.864, "r_x3": 53.798, 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"b": 0, "a": 255}, "rect": {"r_x0": 215.156, "r_y0": 399.624, "r_x1": 217.119, "r_y1": 399.624, "r_x2": 217.119, "r_y2": 391.823, "r_x3": 215.156, "r_y3": 391.823, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.367, "r_y0": 399.624, "r_x1": 295.557, "r_y1": 399.624, "r_x2": 295.557, "r_y2": 391.823, "r_x3": 219.367, "r_y3": 391.823, "coord_origin": "TOPLEFT"}, "text": "Each document cate-", "orig": "Each document cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 410.583, "r_x1": 294.045, "r_y1": 410.583, "r_x2": 294.045, "r_y2": 402.782, "r_x3": 53.798, "r_y3": 402.782, "coord_origin": "TOPLEFT"}, "text": "gory was sourced from various repositories. For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 512.371, "r": 295.562, "b": 607.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 611.001, "r": 295.564, "b": 684.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 195.79, "b": 708.059, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 87.79399999999998, "r": 559.192, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 164.50599999999997, "r": 558.438, "b": 270.937, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.942, "t": 274.095, "r": 559.722, "b": 588.746, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 470.213, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 559.714, "b": 705.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. In phase four,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 104.13800000000003, "r_x1": 558.203, "r_y1": 104.13800000000003, "r_x2": 558.203, "r_y2": 96.68499999999995, "r_x3": 53.798, "r_y3": 96.68499999999995, "coord_origin": "TOPLEFT"}, "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 115.09699999999998, "r_x1": 469.848, "r_y1": 115.09699999999998, "r_x2": 469.848, "r_y2": 107.644, "r_x3": 53.798, "r_y3": 107.644, "coord_origin": "TOPLEFT"}, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, "r_y0": 148.45500000000004, "r_x1": 270.043, "r_y1": 148.45500000000004, "r_x2": 270.043, "r_y2": 140.654, "r_x3": 233.944, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "% of Total", "orig": "% of Total", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.05, "r_y0": 148.45500000000004, "r_x1": 407.372, "r_y1": 148.45500000000004, "r_x2": 407.372, "r_y2": 140.654, "r_x3": 329.05, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "triple inter-annotator", "orig": "triple inter-annotator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.604, "r_y0": 148.45500000000004, "r_x1": 483.398, "r_y1": 148.45500000000004, "r_x2": 483.398, "r_y2": 140.654, "r_x3": 411.604, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "mAP @ 0.5-0.95 (%)", "orig": "mAP @ 0.5-0.95 (%)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 104.825, "r_y0": 159.414, "r_x1": 141.713, "r_y1": 159.414, "r_x2": 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The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"r_x3": 317.955, "r_y3": 575.523, "coord_origin": "TOPLEFT"}, "text": "Critical factors that were considered for the choice of these class", "orig": "Critical factors that were considered for the choice of these class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 594.283, "r_x1": 558.431, "r_y1": 594.283, "r_x2": 558.431, "r_y2": 586.482, "r_x3": 317.955, "r_y3": 586.482, "coord_origin": "TOPLEFT"}, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 229, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 605.242, "r_x1": 558.369, "r_y1": 605.242, "r_x2": 558.369, "r_y2": 597.441, "r_x3": 317.955, "r_y3": 597.441, "coord_origin": "TOPLEFT"}, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 232, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 233, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. 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We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 239, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.954, "r_x1": 447.652, "r_y1": 681.954, "r_x2": 447.652, "r_y2": 674.153, "r_x3": 317.955, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 240, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.86, "r_y0": 681.998, "r_x1": 474.314, "r_y1": 681.998, "r_x2": 474.314, "r_y2": 674.09, "r_x3": 449.86, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 241, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.172, "r_y0": 681.954, "r_x1": 490.397, "r_y1": 681.954, "r_x2": 490.397, "r_y2": 674.153, "r_x3": 477.172, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 242, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.606, "r_y0": 681.998, "r_x1": 528.299, "r_y1": 681.998, "r_x2": 528.299, "r_y2": 674.09, "r_x3": 492.606, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 243, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.299, "r_y0": 681.954, "r_x1": 530.232, "r_y1": 681.954, "r_x2": 530.232, "r_y2": 674.153, "r_x3": 528.299, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 244, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.438, "r_y0": 681.954, "r_x1": 558.201, "r_y1": 681.954, "r_x2": 558.201, "r_y2": 674.153, "r_x3": 532.438, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "as seen", "orig": "as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 245, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 692.913, "r_x1": 558.204, "r_y1": 692.913, "r_x2": 558.204, "r_y2": 685.112, "r_x3": 317.955, "r_y3": 685.112, "coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 246, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 247, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.502, "t": 85.726, "r": 558.49, "b": 115.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 104.13800000000003, "r_x1": 558.203, "r_y1": 104.13800000000003, "r_x2": 558.203, "r_y2": 96.68499999999995, "r_x3": 53.798, "r_y3": 96.68499999999995, "coord_origin": "TOPLEFT"}, "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 115.09699999999998, "r_x1": 469.848, "r_y1": 115.09699999999998, "r_x2": 469.848, "r_y2": 107.644, "r_x3": 53.798, "r_y3": 107.644, "coord_origin": "TOPLEFT"}, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, 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"r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 573.193, "r_x1": 295.649, "r_y1": 573.193, "r_x2": 295.649, "r_y2": 565.74, "r_x3": 53.798, "r_y3": 565.74, "coord_origin": "TOPLEFT"}, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 675.4, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.798, "t": 678.558, "r": 295.558, "b": 708.277, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 310.361, "r": 559.185, "b": 375.105, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 378.263, "r": 559.713, "b": 506.612, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 509.77, "r": 559.718, "b": 692.913, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"r_x3": 317.955, "r_y3": 575.523, "coord_origin": "TOPLEFT"}, "text": "Critical factors that were considered for the choice of these class", "orig": "Critical factors that were considered for the choice of these class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 594.283, "r_x1": 558.431, "r_y1": 594.283, "r_x2": 558.431, "r_y2": 586.482, "r_x3": 317.955, "r_y3": 586.482, "coord_origin": "TOPLEFT"}, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 229, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 605.242, "r_x1": 558.369, "r_y1": 605.242, "r_x2": 558.369, "r_y2": 597.441, "r_x3": 317.955, "r_y3": 597.441, "coord_origin": "TOPLEFT"}, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 232, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 233, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. 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"coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.955, "t": 701.024, "r": 369.246, "b": 708.756, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"index": 246, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 247, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, "r_y0": 148.45500000000004, "r_x1": 270.043, "r_y1": 148.45500000000004, "r_x2": 270.043, "r_y2": 140.654, "r_x3": 233.944, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "% of Total", "orig": "% of Total", 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August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.502, "t": 85.726, "r": 558.49, "b": 115.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 104.13800000000003, "r_x1": 558.203, "r_y1": 104.13800000000003, "r_x2": 558.203, "r_y2": 96.68499999999995, "r_x3": 53.798, "r_y3": 96.68499999999995, "coord_origin": "TOPLEFT"}, "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 115.09699999999998, "r_x1": 469.848, "r_y1": 115.09699999999998, "r_x2": 469.848, "r_y2": 107.644, "r_x3": 53.798, "r_y3": 107.644, "coord_origin": "TOPLEFT"}, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, "r_y0": 148.45500000000004, "r_x1": 270.043, "r_y1": 148.45500000000004, "r_x2": 270.043, "r_y2": 140.654, "r_x3": 233.944, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "% of Total", "orig": "% of Total", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.05, "r_y0": 148.45500000000004, "r_x1": 407.372, "r_y1": 148.45500000000004, "r_x2": 407.372, "r_y2": 140.654, "r_x3": 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"start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 9, "end_col_offset_idx": 10, "text": "86-91", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 457.8, "t": 283.917, "r": 477.509, "b": 291.718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 10, "end_col_offset_idx": 11, "text": "71-76", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 487.47, "t": 283.917, "r": 507.178, "b": 291.718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 11, "end_col_offset_idx": 12, "text": "68-85", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 554.781, "r": 295.649, "b": 606.0699999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.810490071773529, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 573.193, "r_x1": 295.649, "r_y1": 573.193, "r_x2": 295.649, "r_y2": 565.74, "r_x3": 53.798, "r_y3": 565.74, "coord_origin": "TOPLEFT"}, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 675.4, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.798, "t": 678.558, "r": 295.558, "b": 708.277, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 310.361, "r": 559.185, "b": 375.105, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 378.263, "r": 559.713, "b": 506.612, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 509.77, "r": 559.718, "b": 692.913, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"r_x3": 317.955, "r_y3": 575.523, "coord_origin": "TOPLEFT"}, "text": "Critical factors that were considered for the choice of these class", "orig": "Critical factors that were considered for the choice of these class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 594.283, "r_x1": 558.431, "r_y1": 594.283, "r_x2": 558.431, "r_y2": 586.482, "r_x3": 317.955, "r_y3": 586.482, "coord_origin": "TOPLEFT"}, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 229, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 605.242, "r_x1": 558.369, "r_y1": 605.242, "r_x2": 558.369, "r_y2": 597.441, "r_x3": 317.955, "r_y3": 597.441, "coord_origin": "TOPLEFT"}, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 232, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 233, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 234, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 660.037, "r_x1": 436.906, "r_y1": 660.037, "r_x2": 436.906, "r_y2": 652.236, "r_x3": 317.955, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 235, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.138, "r_y0": 660.081, "r_x1": 469.691, "r_y1": 660.081, "r_x2": 469.691, "r_y2": 652.173, "r_x3": 439.138, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 236, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.429, "r_y0": 660.037, "r_x1": 493.973, "r_y1": 660.037, "r_x2": 493.973, "r_y2": 652.236, "r_x3": 472.429, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 237, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 496.207, "r_y0": 660.081, "r_x1": 558.2, "r_y1": 660.081, "r_x2": 558.2, "r_y2": 652.173, "r_x3": 496.207, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 238, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 670.996, "r_x1": 558.206, "r_y1": 670.996, "r_x2": 558.206, "r_y2": 663.1949999999999, "r_x3": 317.955, "r_y3": 663.1949999999999, "coord_origin": "TOPLEFT"}, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 239, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.954, "r_x1": 447.652, "r_y1": 681.954, "r_x2": 447.652, "r_y2": 674.153, "r_x3": 317.955, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 240, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.86, "r_y0": 681.998, "r_x1": 474.314, "r_y1": 681.998, "r_x2": 474.314, "r_y2": 674.09, "r_x3": 449.86, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 241, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.172, "r_y0": 681.954, "r_x1": 490.397, "r_y1": 681.954, "r_x2": 490.397, "r_y2": 674.153, "r_x3": 477.172, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 242, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.606, "r_y0": 681.998, "r_x1": 528.299, "r_y1": 681.998, "r_x2": 528.299, "r_y2": 674.09, "r_x3": 492.606, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 243, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.299, "r_y0": 681.954, "r_x1": 530.232, "r_y1": 681.954, "r_x2": 530.232, "r_y2": 674.153, "r_x3": 528.299, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 244, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.438, "r_y0": 681.954, "r_x1": 558.201, "r_y1": 681.954, "r_x2": 558.201, "r_y2": 674.153, "r_x3": 532.438, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "as seen", "orig": "as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 245, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 692.913, "r_x1": 558.204, "r_y1": 692.913, "r_x2": 558.204, "r_y2": 685.112, "r_x3": 317.955, "r_y3": 685.112, "coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.955, "t": 701.024, "r": 369.246, "b": 708.756, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"index": 246, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 247, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.502, "t": 85.726, "r": 558.49, "b": 115.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 104.13800000000003, "r_x1": 558.203, "r_y1": 104.13800000000003, "r_x2": 558.203, "r_y2": 96.68499999999995, "r_x3": 53.798, "r_y3": 96.68499999999995, "coord_origin": "TOPLEFT"}, "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 115.09699999999998, "r_x1": 469.848, "r_y1": 115.09699999999998, "r_x2": 469.848, "r_y2": 107.644, "r_x3": 53.798, "r_y3": 107.644, "coord_origin": "TOPLEFT"}, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, "r_y0": 148.45500000000004, "r_x1": 270.043, "r_y1": 148.45500000000004, "r_x2": 270.043, "r_y2": 140.654, "r_x3": 233.944, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "% of Total", "orig": "% of Total", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.05, "r_y0": 148.45500000000004, "r_x1": 407.372, "r_y1": 148.45500000000004, "r_x2": 407.372, "r_y2": 140.654, "r_x3": 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"TOPLEFT"}, "confidence": 0.810490071773529, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 573.193, "r_x1": 295.649, "r_y1": 573.193, "r_x2": 295.649, "r_y2": 565.74, "r_x3": 53.798, "r_y3": 565.74, "coord_origin": "TOPLEFT"}, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 675.4, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.798, "t": 678.558, "r": 295.558, "b": 708.277, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 310.361, "r": 559.185, "b": 375.105, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 378.263, "r": 559.713, "b": 506.612, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 509.77, "r": 559.718, "b": 692.913, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 232, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 233, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 234, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 660.037, "r_x1": 436.906, "r_y1": 660.037, "r_x2": 436.906, "r_y2": 652.236, "r_x3": 317.955, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 235, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.138, "r_y0": 660.081, "r_x1": 469.691, "r_y1": 660.081, "r_x2": 469.691, "r_y2": 652.173, "r_x3": 439.138, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 236, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.429, "r_y0": 660.037, "r_x1": 493.973, "r_y1": 660.037, "r_x2": 493.973, "r_y2": 652.236, "r_x3": 472.429, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 237, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 496.207, "r_y0": 660.081, "r_x1": 558.2, "r_y1": 660.081, "r_x2": 558.2, "r_y2": 652.173, "r_x3": 496.207, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 238, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 670.996, "r_x1": 558.206, "r_y1": 670.996, "r_x2": 558.206, "r_y2": 663.1949999999999, "r_x3": 317.955, "r_y3": 663.1949999999999, "coord_origin": "TOPLEFT"}, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 239, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.954, "r_x1": 447.652, "r_y1": 681.954, "r_x2": 447.652, "r_y2": 674.153, "r_x3": 317.955, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 240, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.86, "r_y0": 681.998, "r_x1": 474.314, "r_y1": 681.998, "r_x2": 474.314, "r_y2": 674.09, "r_x3": 449.86, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 241, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.172, "r_y0": 681.954, "r_x1": 490.397, "r_y1": 681.954, "r_x2": 490.397, "r_y2": 674.153, "r_x3": 477.172, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 242, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.606, "r_y0": 681.998, "r_x1": 528.299, "r_y1": 681.998, "r_x2": 528.299, "r_y2": 674.09, "r_x3": 492.606, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 243, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.299, "r_y0": 681.954, "r_x1": 530.232, "r_y1": 681.954, "r_x2": 530.232, "r_y2": 674.153, "r_x3": 528.299, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 244, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.438, "r_y0": 681.954, "r_x1": 558.201, "r_y1": 681.954, "r_x2": 558.201, "r_y2": 674.153, "r_x3": 532.438, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "as seen", "orig": "as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 245, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 692.913, "r_x1": 558.204, "r_y1": 692.913, "r_x2": 558.204, "r_y2": 685.112, "r_x3": 317.955, "r_y3": 685.112, "coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.955, "t": 701.024, "r": 369.246, "b": 708.756, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"index": 246, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 247, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.691, "r_x1": 67.283, "r_y1": 336.691, "r_x2": 67.283, "r_y2": 328.89, "r_x3": 53.798, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 356.744, "r_x1": 74.221, "r_y1": 356.744, "r_x2": 74.221, "r_y2": 348.943, "r_x3": 64.708, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 356.744, "r_x1": 294.046, "r_y1": 356.744, "r_x2": 294.046, "r_y2": 348.943, "r_x3": 78.207, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 367.703, "r_x1": 95.73, "r_y1": 367.703, "r_x2": 95.73, "r_y2": 359.902, "r_x3": 78.207, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.977, "r_y0": 367.747, "r_x1": 130.171, "r_y1": 367.747, "r_x2": 130.171, "r_y2": 359.839, "r_x3": 97.977, "r_y3": 359.839, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 389.621, "r_x1": 91.594, "r_y1": 389.621, "r_x2": 91.594, "r_y2": 381.82, "r_x3": 78.207, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.835, "r_y0": 389.665, "r_x1": 106.881, "r_y1": 389.665, "r_x2": 106.881, "r_y2": 381.757, "r_x3": 93.835, "r_y3": 381.757, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 109.629, "r_y0": 389.621, "r_x1": 133.722, "r_y1": 389.621, "r_x2": 133.722, "r_y2": 381.82, "r_x3": 109.629, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 400.58, "r_x1": 74.221, "r_y1": 400.58, "r_x2": 74.221, "r_y2": 392.779, "r_x3": 64.708, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 400.58, "r_x1": 84.351, "r_y1": 400.58, "r_x2": 84.351, "r_y2": 392.779, "r_x3": 78.207, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 86.584, "r_y0": 400.624, "r_x1": 118.013, "r_y1": 400.624, "r_x2": 118.013, "r_y2": 392.716, "r_x3": 86.584, "r_y3": 392.716, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.269, "r_y0": 400.58, "r_x1": 295.557, "r_y1": 400.58, "r_x2": 295.557, "r_y2": 392.779, "r_x3": 120.269, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 433.457, "r_x1": 192.009, "r_y1": 433.457, "r_x2": 192.009, "r_y2": 425.656, "r_x3": 78.207, "r_y3": 425.656, "coord_origin": "TOPLEFT"}, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 444.416, "r_x1": 74.221, "r_y1": 444.416, "r_x2": 74.221, "r_y2": 436.615, "r_x3": 64.708, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 444.416, "r_x1": 112.616, "r_y1": 444.416, "r_x2": 112.616, "r_y2": 436.615, "r_x3": 78.207, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 114.861, "r_y0": 444.46, "r_x1": 142.612, "r_y1": 444.46, "r_x2": 142.612, "r_y2": 436.552, "r_x3": 114.861, "r_y3": 436.552, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.612, "r_y0": 444.416, "r_x1": 144.557, "r_y1": 444.416, "r_x2": 144.557, "r_y2": 436.615, "r_x3": 142.612, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.794, "r_y0": 444.416, "r_x1": 294.047, "r_y1": 444.416, "r_x2": 294.047, "r_y2": 436.615, "r_x3": 146.794, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "there must be exactly one corresponding", "orig": "there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 455.419, "r_x1": 102.793, "r_y1": 455.419, "r_x2": 102.793, "r_y2": 447.511, "r_x3": 78.207, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 105.245, "r_y0": 455.375, "r_x1": 113.1, "r_y1": 455.375, "r_x2": 113.1, "r_y2": 447.574, "r_x3": 105.245, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.341, "r_y0": 455.419, "r_x1": 134.404, "r_y1": 455.419, "r_x2": 134.404, "r_y2": 447.511, "r_x3": 115.341, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.403, "r_y0": 455.375, "r_x1": 136.376, "r_y1": 455.375, "r_x2": 136.376, "r_y2": 447.574, "r_x3": 134.403, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 466.333, "r_x1": 74.221, "r_y1": 466.333, "r_x2": 74.221, "r_y2": 458.532, "r_x3": 64.708, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 466.333, "r_x1": 267.468, "r_y1": 466.333, "r_x2": 267.468, "r_y2": 458.532, "r_x3": 78.207, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 269.706, "r_y0": 466.377, "r_x1": 294.046, "r_y1": 466.377, "r_x2": 294.046, "r_y2": 458.469, "r_x3": 269.706, "r_y3": 458.469, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 477.292, "r_x1": 102.3, "r_y1": 477.292, "r_x2": 102.3, "r_y2": 469.491, "r_x3": 78.207, "r_y3": 469.491, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 488.251, "r_x1": 74.221, "r_y1": 488.251, "r_x2": 74.221, "r_y2": 480.45, "r_x3": 64.708, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.202, "r_y0": 488.251, "r_x1": 206.135, "r_y1": 488.251, "r_x2": 206.135, "r_y2": 480.45, "r_x3": 78.202, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.38, "r_y0": 488.295, "r_x1": 238.122, "r_y1": 488.295, "r_x2": 238.122, "r_y2": 480.387, "r_x3": 208.38, "r_y3": 480.387, "coord_origin": "TOPLEFT"}, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.413, "r_y0": 488.251, "r_x1": 264.506, "r_y1": 488.251, "r_x2": 264.506, "r_y2": 480.45, "r_x3": 240.413, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 499.21, "r_x1": 294.046, "r_y1": 499.21, "r_x2": 294.046, "r_y2": 491.409, "r_x3": 78.207, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 510.169, "r_x1": 200.348, "r_y1": 510.169, "r_x2": 200.348, "r_y2": 502.368, "r_x3": 78.207, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.667, "r_y0": 510.213, "r_x1": 256.575, "r_y1": 510.213, "r_x2": 256.575, "r_y2": 502.305, "r_x3": 203.667, "r_y3": 502.305, "coord_origin": "TOPLEFT"}, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 256.574, "r_y0": 510.169, "r_x1": 258.586, "r_y1": 510.169, "r_x2": 258.586, "r_y2": 502.368, "r_x3": 256.574, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.906, "r_y0": 510.169, "r_x1": 294.044, "r_y1": 510.169, "r_x2": 294.044, "r_y2": 502.368, "r_x3": 261.906, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "unless it", "orig": "unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 521.1279999999999, "r_x1": 208.13, "r_y1": 521.1279999999999, "r_x2": 208.13, "r_y2": 513.327, "r_x3": 78.207, "r_y3": 513.327, "coord_origin": "TOPLEFT"}, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.14, "r_x1": 295.563, "r_y1": 552.14, "r_x2": 295.563, "r_y2": 544.3389999999999, "r_x3": 53.798, "r_y3": 544.3389999999999, "coord_origin": "TOPLEFT"}, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 181.83299999999997, "r_x1": 416.179, "r_y1": 181.83299999999997, "r_x2": 416.179, "r_y2": 180.015, "r_x3": 340.002, "r_y3": 180.015, "coord_origin": "TOPLEFT"}, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.383, "r_y0": 92.00300000000004, "r_x1": 417.395, "r_y1": 92.00300000000004, "r_x2": 417.395, "r_y2": 86.39099999999996, "r_x3": 339.383, "r_y3": 86.39099999999996, "coord_origin": "TOPLEFT"}, "text": "Compliant with guidelines", "orig": "Compliant with guidelines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.428, "r_y0": 92.00300000000004, "r_x1": 545.834, "r_y1": 92.00300000000004, "r_x2": 545.834, "r_y2": 86.39099999999996, "r_x3": 451.428, "r_y3": 86.39099999999996, "coord_origin": "TOPLEFT"}, "text": "Plausible but invalid alternative", "orig": "Plausible but invalid alternative", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.337, "r_y0": 371.669, "r_x1": 513.216, "r_y1": 371.669, "r_x2": 513.216, "r_y2": 366.057, "r_x3": 350.337, "r_y3": 366.057, "coord_origin": "TOPLEFT"}, "text": "Borderline case: Two guideline-compliant alternatives", "orig": "Borderline case: Two guideline-compliant alternatives", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 247.11400000000003, "r_x1": 416.179, 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"from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 322.194, "r_y0": 259.21900000000005, "r_x1": 326.015, "r_y1": 259.21900000000005, "r_x2": 326.015, "r_y2": 254.54200000000003, "r_x3": 322.194, "r_y3": 254.54200000000003, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 322.194, "r_y0": 372.762, "r_x1": 326.015, "r_y1": 372.762, "r_x2": 326.015, "r_y2": 368.085, "r_x3": 322.194, "r_y3": 368.085, "coord_origin": "TOPLEFT"}, "text": "D", "orig": "D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 481.718, "r_x1": 558.389, "r_y1": 481.718, "r_x2": 558.389, "r_y2": 474.265, "r_x3": 317.955, "r_y3": 474.265, "coord_origin": "TOPLEFT"}, "text": "Figure 4: Examples of plausible annotation alternatives for", "orig": "Figure 4: Examples of plausible annotation alternatives for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 492.677, "r_x1": 559.806, "r_y1": 492.677, "r_x2": 559.806, "r_y2": 485.224, "r_x3": 317.955, "r_y3": 485.224, "coord_origin": "TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 53.798, "t": 87.79399999999998, "r": 294.045, "b": 106.59799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 109.71199999999999, "r": 295.559, "b": 249.019, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.798, "t": 252.17700000000002, "r": 295.56, "b": 336.691, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.691, "r_x1": 67.283, "r_y1": 336.691, "r_x2": 67.283, "r_y2": 328.89, "r_x3": 53.798, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.943, "r": 294.046, "b": 389.665, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 356.744, "r_x1": 74.221, "r_y1": 356.744, "r_x2": 74.221, "r_y2": 348.943, "r_x3": 64.708, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, 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Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. 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Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 577.216, "r": 295.562, "b": 705.565, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. 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"TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.623, "t": 525.928, "r": 558.204, "b": 544.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.623, "t": 547.846, "r": 559.715, "b": 709.072, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, 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"bbox": {"l": 53.798, "t": 87.79399999999998, "r": 294.045, "b": 106.59799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 109.71199999999999, "r": 295.559, "b": 249.019, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.798, "t": 252.17700000000002, "r": 295.56, "b": 336.691, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.691, "r_x1": 67.283, "r_y1": 336.691, "r_x2": 67.283, "r_y2": 328.89, "r_x3": 53.798, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.943, "r": 294.046, "b": 389.665, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 356.744, "r_x1": 74.221, "r_y1": 356.744, "r_x2": 74.221, "r_y2": 348.943, "r_x3": 64.708, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 356.744, "r_x1": 294.046, "r_y1": 356.744, "r_x2": 294.046, "r_y2": 348.943, "r_x3": 78.207, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 367.703, "r_x1": 95.73, "r_y1": 367.703, "r_x2": 95.73, "r_y2": 359.902, "r_x3": 78.207, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.977, "r_y0": 367.747, "r_x1": 130.171, "r_y1": 367.747, "r_x2": 130.171, "r_y2": 359.839, "r_x3": 97.977, "r_y3": 359.839, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.171, "r_y0": 367.703, "r_x1": 132.163, "r_y1": 367.703, "r_x2": 132.163, "r_y2": 359.902, "r_x3": 130.171, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.409, "r_y0": 367.703, "r_x1": 294.04, "r_y1": 367.703, "r_x2": 294.04, "r_y2": 359.902, "r_x3": 134.409, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "This definition is different from PubLayNet", "orig": "This definition is different from PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 378.662, "r_x1": 294.044, "r_y1": 378.662, "r_x2": 294.044, "r_y2": 370.861, "r_x3": 78.207, "r_y3": 370.861, "coord_origin": "TOPLEFT"}, "text": "and DocBank, where all list-items are grouped together into", "orig": "and DocBank, where all list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 389.621, "r_x1": 91.594, "r_y1": 389.621, "r_x2": 91.594, "r_y2": 381.82, "r_x3": 78.207, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.835, "r_y0": 389.665, "r_x1": 106.881, "r_y1": 389.665, "r_x2": 106.881, "r_y2": 381.757, "r_x3": 93.835, "r_y3": 381.757, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 109.629, "r_y0": 389.621, "r_x1": 133.722, "r_y1": 389.621, "r_x2": 133.722, "r_y2": 381.82, "r_x3": 109.629, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.708, "t": 392.716, "r": 295.564, "b": 433.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 400.58, "r_x1": 74.221, "r_y1": 400.58, "r_x2": 74.221, "r_y2": 392.779, "r_x3": 64.708, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 400.58, "r_x1": 84.351, "r_y1": 400.58, "r_x2": 84.351, "r_y2": 392.779, "r_x3": 78.207, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 86.584, "r_y0": 400.624, "r_x1": 118.013, "r_y1": 400.624, "r_x2": 118.013, "r_y2": 392.716, "r_x3": 86.584, "r_y3": 392.716, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.269, "r_y0": 400.58, "r_x1": 295.557, "r_y1": 400.58, "r_x2": 295.557, "r_y2": 392.779, "r_x3": 120.269, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 433.457, "r_x1": 192.009, "r_y1": 433.457, "r_x2": 192.009, "r_y2": 425.656, "r_x3": 78.207, "r_y3": 425.656, "coord_origin": "TOPLEFT"}, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.552, "r": 294.047, "b": 455.419, "coord_origin": "TOPLEFT"}, "confidence": 0.9413429498672485, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 444.416, "r_x1": 74.221, "r_y1": 444.416, "r_x2": 74.221, "r_y2": 436.615, "r_x3": 64.708, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 444.416, "r_x1": 112.616, "r_y1": 444.416, "r_x2": 112.616, "r_y2": 436.615, "r_x3": 78.207, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 114.861, "r_y0": 444.46, "r_x1": 142.612, "r_y1": 444.46, "r_x2": 142.612, "r_y2": 436.552, "r_x3": 114.861, "r_y3": 436.552, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.612, "r_y0": 444.416, "r_x1": 144.557, "r_y1": 444.416, "r_x2": 144.557, "r_y2": 436.615, "r_x3": 142.612, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.794, "r_y0": 444.416, "r_x1": 294.047, "r_y1": 444.416, "r_x2": 294.047, "r_y2": 436.615, "r_x3": 146.794, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "there must be exactly one corresponding", "orig": "there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 455.419, "r_x1": 102.793, "r_y1": 455.419, "r_x2": 102.793, "r_y2": 447.511, "r_x3": 78.207, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 105.245, "r_y0": 455.375, "r_x1": 113.1, "r_y1": 455.375, "r_x2": 113.1, "r_y2": 447.574, "r_x3": 105.245, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.341, "r_y0": 455.419, "r_x1": 134.404, "r_y1": 455.419, "r_x2": 134.404, "r_y2": 447.511, "r_x3": 115.341, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.403, "r_y0": 455.375, "r_x1": 136.376, "r_y1": 455.375, "r_x2": 136.376, "r_y2": 447.574, "r_x3": 134.403, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.469, "r": 294.046, "b": 477.292, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 466.333, "r_x1": 74.221, "r_y1": 466.333, "r_x2": 74.221, "r_y2": 458.532, "r_x3": 64.708, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 466.333, "r_x1": 267.468, "r_y1": 466.333, "r_x2": 267.468, "r_y2": 458.532, "r_x3": 78.207, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 269.706, "r_y0": 466.377, "r_x1": 294.046, "r_y1": 466.377, "r_x2": 294.046, "r_y2": 458.469, "r_x3": 269.706, "r_y3": 458.469, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 477.292, "r_x1": 102.3, "r_y1": 477.292, "r_x2": 102.3, "r_y2": 469.491, "r_x3": 78.207, "r_y3": 469.491, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.387, "r": 264.506, "b": 488.295, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 488.251, "r_x1": 74.221, "r_y1": 488.251, "r_x2": 74.221, "r_y2": 480.45, "r_x3": 64.708, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.202, "r_y0": 488.251, "r_x1": 206.135, "r_y1": 488.251, "r_x2": 206.135, "r_y2": 480.45, "r_x3": 78.202, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.38, "r_y0": 488.295, "r_x1": 238.122, "r_y1": 488.295, "r_x2": 238.122, "r_y2": 480.387, "r_x3": 208.38, "r_y3": 480.387, "coord_origin": "TOPLEFT"}, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.413, "r_y0": 488.251, "r_x1": 264.506, "r_y1": 488.251, "r_x2": 264.506, "r_y2": 480.45, "r_x3": 240.413, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708, "t": 491.409, "r": 294.046, "b": 521.1279999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9544308185577393, "cells": [{"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 499.21, "r_x1": 294.046, "r_y1": 499.21, "r_x2": 294.046, "r_y2": 491.409, "r_x3": 78.207, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 510.169, "r_x1": 200.348, "r_y1": 510.169, "r_x2": 200.348, "r_y2": 502.368, "r_x3": 78.207, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.667, "r_y0": 510.213, "r_x1": 256.575, "r_y1": 510.213, "r_x2": 256.575, "r_y2": 502.305, "r_x3": 203.667, "r_y3": 502.305, "coord_origin": "TOPLEFT"}, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 256.574, "r_y0": 510.169, "r_x1": 258.586, "r_y1": 510.169, "r_x2": 258.586, "r_y2": 502.368, "r_x3": 256.574, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.906, "r_y0": 510.169, "r_x1": 294.044, "r_y1": 510.169, "r_x2": 294.044, "r_y2": 502.368, "r_x3": 261.906, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "unless it", "orig": "unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 521.1279999999999, "r_x1": 208.13, "r_y1": 521.1279999999999, "r_x2": 208.13, "r_y2": 513.327, "r_x3": 78.207, "r_y3": 513.327, "coord_origin": "TOPLEFT"}, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.529, "t": 533.381, "r": 295.563, "b": 574.058, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.14, "r_x1": 295.563, "r_y1": 552.14, "r_x2": 295.563, "r_y2": 544.3389999999999, "r_x3": 53.798, "r_y3": 544.3389999999999, "coord_origin": "TOPLEFT"}, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 577.216, "r": 295.562, "b": 705.565, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. 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false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 492.677, "r_x1": 559.806, "r_y1": 492.677, "r_x2": 559.806, "r_y2": 485.224, "r_x3": 317.955, "r_y3": 485.224, "coord_origin": "TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.623, "t": 525.928, "r": 558.204, "b": 544.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.623, "t": 547.846, "r": 559.715, "b": 709.072, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "body": [{"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.798, "t": 87.79399999999998, "r": 294.045, "b": 106.59799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 109.71199999999999, "r": 295.559, "b": 249.019, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.798, "t": 252.17700000000002, "r": 295.56, "b": 336.691, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.691, "r_x1": 67.283, "r_y1": 336.691, "r_x2": 67.283, "r_y2": 328.89, "r_x3": 53.798, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.943, "r": 294.046, "b": 389.665, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 356.744, "r_x1": 74.221, "r_y1": 356.744, "r_x2": 74.221, "r_y2": 348.943, "r_x3": 64.708, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 356.744, "r_x1": 294.046, "r_y1": 356.744, "r_x2": 294.046, "r_y2": 348.943, "r_x3": 78.207, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 367.703, "r_x1": 95.73, "r_y1": 367.703, "r_x2": 95.73, "r_y2": 359.902, "r_x3": 78.207, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.977, "r_y0": 367.747, "r_x1": 130.171, "r_y1": 367.747, "r_x2": 130.171, "r_y2": 359.839, "r_x3": 97.977, "r_y3": 359.839, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.171, "r_y0": 367.703, "r_x1": 132.163, "r_y1": 367.703, "r_x2": 132.163, "r_y2": 359.902, "r_x3": 130.171, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.409, "r_y0": 367.703, "r_x1": 294.04, "r_y1": 367.703, "r_x2": 294.04, "r_y2": 359.902, "r_x3": 134.409, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "This definition is different from PubLayNet", "orig": "This definition is different from PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 378.662, "r_x1": 294.044, "r_y1": 378.662, "r_x2": 294.044, "r_y2": 370.861, "r_x3": 78.207, "r_y3": 370.861, "coord_origin": "TOPLEFT"}, "text": "and DocBank, where all list-items are grouped together into", "orig": "and DocBank, where all list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 389.621, "r_x1": 91.594, "r_y1": 389.621, "r_x2": 91.594, "r_y2": 381.82, "r_x3": 78.207, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.835, "r_y0": 389.665, "r_x1": 106.881, "r_y1": 389.665, "r_x2": 106.881, "r_y2": 381.757, "r_x3": 93.835, "r_y3": 381.757, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 109.629, "r_y0": 389.621, "r_x1": 133.722, "r_y1": 389.621, "r_x2": 133.722, "r_y2": 381.82, "r_x3": 109.629, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.708, "t": 392.716, "r": 295.564, "b": 433.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 400.58, "r_x1": 74.221, "r_y1": 400.58, "r_x2": 74.221, "r_y2": 392.779, "r_x3": 64.708, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 400.58, "r_x1": 84.351, "r_y1": 400.58, "r_x2": 84.351, "r_y2": 392.779, "r_x3": 78.207, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 86.584, "r_y0": 400.624, "r_x1": 118.013, "r_y1": 400.624, "r_x2": 118.013, "r_y2": 392.716, "r_x3": 86.584, "r_y3": 392.716, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.269, "r_y0": 400.58, "r_x1": 295.557, "r_y1": 400.58, "r_x2": 295.557, "r_y2": 392.779, "r_x3": 120.269, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 433.457, "r_x1": 192.009, "r_y1": 433.457, "r_x2": 192.009, "r_y2": 425.656, "r_x3": 78.207, "r_y3": 425.656, "coord_origin": "TOPLEFT"}, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.552, "r": 294.047, "b": 455.419, "coord_origin": "TOPLEFT"}, "confidence": 0.9413429498672485, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 444.416, "r_x1": 74.221, "r_y1": 444.416, "r_x2": 74.221, "r_y2": 436.615, "r_x3": 64.708, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 444.416, "r_x1": 112.616, "r_y1": 444.416, "r_x2": 112.616, "r_y2": 436.615, "r_x3": 78.207, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 114.861, "r_y0": 444.46, "r_x1": 142.612, "r_y1": 444.46, "r_x2": 142.612, "r_y2": 436.552, "r_x3": 114.861, "r_y3": 436.552, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.612, "r_y0": 444.416, "r_x1": 144.557, "r_y1": 444.416, "r_x2": 144.557, "r_y2": 436.615, "r_x3": 142.612, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.794, "r_y0": 444.416, "r_x1": 294.047, "r_y1": 444.416, "r_x2": 294.047, "r_y2": 436.615, "r_x3": 146.794, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "there must be exactly one corresponding", "orig": "there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 455.419, "r_x1": 102.793, "r_y1": 455.419, "r_x2": 102.793, "r_y2": 447.511, "r_x3": 78.207, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 105.245, "r_y0": 455.375, "r_x1": 113.1, "r_y1": 455.375, "r_x2": 113.1, "r_y2": 447.574, "r_x3": 105.245, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.341, "r_y0": 455.419, "r_x1": 134.404, "r_y1": 455.419, "r_x2": 134.404, "r_y2": 447.511, "r_x3": 115.341, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.403, "r_y0": 455.375, "r_x1": 136.376, "r_y1": 455.375, "r_x2": 136.376, "r_y2": 447.574, "r_x3": 134.403, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.469, "r": 294.046, "b": 477.292, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 466.333, "r_x1": 74.221, "r_y1": 466.333, "r_x2": 74.221, "r_y2": 458.532, "r_x3": 64.708, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 466.333, "r_x1": 267.468, "r_y1": 466.333, "r_x2": 267.468, "r_y2": 458.532, "r_x3": 78.207, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 269.706, "r_y0": 466.377, "r_x1": 294.046, "r_y1": 466.377, "r_x2": 294.046, "r_y2": 458.469, "r_x3": 269.706, "r_y3": 458.469, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 477.292, "r_x1": 102.3, "r_y1": 477.292, "r_x2": 102.3, "r_y2": 469.491, "r_x3": 78.207, "r_y3": 469.491, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.387, "r": 264.506, "b": 488.295, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 488.251, "r_x1": 74.221, "r_y1": 488.251, "r_x2": 74.221, "r_y2": 480.45, "r_x3": 64.708, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.202, "r_y0": 488.251, "r_x1": 206.135, "r_y1": 488.251, "r_x2": 206.135, "r_y2": 480.45, "r_x3": 78.202, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.38, "r_y0": 488.295, "r_x1": 238.122, "r_y1": 488.295, "r_x2": 238.122, "r_y2": 480.387, "r_x3": 208.38, "r_y3": 480.387, "coord_origin": "TOPLEFT"}, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.413, "r_y0": 488.251, "r_x1": 264.506, "r_y1": 488.251, "r_x2": 264.506, "r_y2": 480.45, "r_x3": 240.413, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708, "t": 491.409, "r": 294.046, "b": 521.1279999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9544308185577393, "cells": [{"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 499.21, "r_x1": 294.046, "r_y1": 499.21, "r_x2": 294.046, "r_y2": 491.409, "r_x3": 78.207, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 510.169, "r_x1": 200.348, "r_y1": 510.169, "r_x2": 200.348, "r_y2": 502.368, "r_x3": 78.207, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.667, "r_y0": 510.213, "r_x1": 256.575, "r_y1": 510.213, "r_x2": 256.575, "r_y2": 502.305, "r_x3": 203.667, "r_y3": 502.305, "coord_origin": "TOPLEFT"}, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 256.574, "r_y0": 510.169, "r_x1": 258.586, "r_y1": 510.169, "r_x2": 258.586, "r_y2": 502.368, "r_x3": 256.574, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.906, "r_y0": 510.169, "r_x1": 294.044, "r_y1": 510.169, "r_x2": 294.044, "r_y2": 502.368, "r_x3": 261.906, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "unless it", "orig": "unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 521.1279999999999, "r_x1": 208.13, "r_y1": 521.1279999999999, "r_x2": 208.13, "r_y2": 513.327, "r_x3": 78.207, "r_y3": 513.327, "coord_origin": "TOPLEFT"}, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.529, "t": 533.381, "r": 295.563, "b": 574.058, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.14, "r_x1": 295.563, "r_y1": 552.14, "r_x2": 295.563, "r_y2": 544.3389999999999, "r_x3": 53.798, "r_y3": 544.3389999999999, "coord_origin": "TOPLEFT"}, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 577.216, "r": 295.562, "b": 705.565, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264115214347839, "cells": [{"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 181.83299999999997, "r_x1": 416.179, "r_y1": 181.83299999999997, "r_x2": 416.179, "r_y2": 180.015, "r_x3": 340.002, "r_y3": 180.015, "coord_origin": "TOPLEFT"}, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.383, "r_y0": 92.00300000000004, "r_x1": 417.395, "r_y1": 92.00300000000004, "r_x2": 417.395, "r_y2": 86.39099999999996, "r_x3": 339.383, "r_y3": 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false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 492.677, "r_x1": 559.806, "r_y1": 492.677, "r_x2": 559.806, "r_y2": 485.224, "r_x3": 317.955, "r_y3": 485.224, "coord_origin": "TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.623, "t": 525.928, "r": 558.204, "b": 544.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.623, "t": 547.846, "r": 559.715, "b": 709.072, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 91.52, "r_y1": 106.05600000000004, "r_x2": 91.52, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "detection", "orig": "detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.976, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 95.976, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "networks on DocLayNet test set. The MRCNN", "orig": "networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.52, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 202.434, "r_y1": 138.933, "r_x2": 202.434, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.085, "r_y0": 138.95899999999995, "r_x1": 247.142, "r_y1": 138.95899999999995, "r_x2": 247.142, "r_y2": 131.48000000000002, "r_x3": 206.085, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.95, "r_y0": 138.933, "r_x1": 294.043, "r_y1": 138.933, "r_x2": 294.043, "r_y2": 131.48000000000002, "r_x3": 250.95, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 149.89200000000005, "r_x1": 294.044, "r_y1": 149.89200000000005, "r_x2": 294.044, "r_y2": 142.43899999999996, "r_x3": 53.52, "r_y3": 142.43899999999996, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 160.851, "r_x1": 294.044, "r_y1": 160.851, "r_x2": 294.044, "r_y2": 153.39800000000002, "r_x3": 53.798, "r_y3": 153.39800000000002, "coord_origin": "TOPLEFT"}, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 171.80999999999995, "r_x1": 295.649, "r_y1": 171.80999999999995, "r_x2": 295.649, "r_y2": 164.35699999999997, "r_x3": 53.798, "r_y3": 164.35699999999997, "coord_origin": "TOPLEFT"}, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 182.76800000000003, "r_x1": 268.624, "r_y1": 182.76800000000003, "r_x2": 268.624, "r_y2": 175.31500000000005, "r_x3": 53.798, "r_y3": 175.31500000000005, "coord_origin": "TOPLEFT"}, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.365, "r_y0": 206.20500000000004, "r_x1": 157.991, "r_y1": 206.20500000000004, "r_x2": 157.991, "r_y2": 198.404, "r_x3": 132.365, "r_y3": 198.404, "coord_origin": "TOPLEFT"}, "text": "human", "orig": "human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 401.077, "r_x1": 140.362, "r_y1": 401.077, "r_x2": 140.362, "r_y2": 393.276, "r_x3": 53.798, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.99, "r_y0": 401.121, "r_x1": 175.969, "r_y1": 401.121, "r_x2": 175.969, "r_y2": 393.213, "r_x3": 142.99, "r_y3": 393.213, "coord_origin": "TOPLEFT"}, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.951, "r_y0": 401.077, "r_x1": 294.041, "r_y1": 401.077, "r_x2": 294.041, "r_y2": 393.276, "r_x3": 178.951, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 412.036, "r_x1": 295.425, "r_y1": 412.036, "r_x2": 295.425, "r_y2": 404.235, "r_x3": 53.798, "r_y3": 404.235, "coord_origin": "TOPLEFT"}, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 422.995, "r_x1": 294.043, "r_y1": 422.995, "r_x2": 294.043, "r_y2": 415.194, "r_x3": 53.529, "r_y3": 415.194, "coord_origin": "TOPLEFT"}, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool 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"coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 385.937, "r_y0": 252.75400000000002, "r_x1": 392.569, "r_y1": 252.75400000000002, "r_x2": 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"coord_origin": "TOPLEFT"}, "text": "ing fractions of the DocLayNet dataset. The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, 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295.556, "b": 576.42, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 379.159, "r_x1": 294.047, "r_y1": 379.159, "r_x2": 294.047, "r_y2": 371.358, "r_x3": 53.798, "r_y3": 371.358, "coord_origin": "TOPLEFT"}, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 390.118, "r_x1": 295.556, "r_y1": 390.118, "r_x2": 295.556, "r_y2": 382.317, "r_x3": 53.798, "r_y3": 382.317, "coord_origin": "TOPLEFT"}, "text": "numbers for human document-layout annotation. Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 401.077, "r_x1": 140.362, "r_y1": 401.077, "r_x2": 140.362, "r_y2": 393.276, "r_x3": 53.798, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.99, "r_y0": 401.121, "r_x1": 175.969, "r_y1": 401.121, "r_x2": 175.969, "r_y2": 393.213, "r_x3": 142.99, "r_y3": 393.213, "coord_origin": "TOPLEFT"}, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.951, "r_y0": 401.077, "r_x1": 294.041, "r_y1": 401.077, "r_x2": 294.041, "r_y2": 393.276, "r_x3": 178.951, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 412.036, "r_x1": 295.425, "r_y1": 412.036, "r_x2": 295.425, "r_y2": 404.235, "r_x3": 53.798, "r_y3": 404.235, "coord_origin": "TOPLEFT"}, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 422.995, "r_x1": 294.043, "r_y1": 422.995, "r_x2": 294.043, "r_y2": 415.194, "r_x3": 53.529, "r_y3": 415.194, "coord_origin": "TOPLEFT"}, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 433.954, "r_x1": 294.048, "r_y1": 433.954, "r_x2": 294.048, "r_y2": 426.153, "r_x3": 53.798, "r_y3": 426.153, "coord_origin": "TOPLEFT"}, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 444.913, "r_x1": 256.806, "r_y1": 444.913, "r_x2": 256.806, "r_y2": 437.112, "r_x3": 53.798, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": 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1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.877, "r_y0": 455.872, "r_x1": 80.889, "r_y1": 455.872, "r_x2": 80.889, "r_y2": 448.071, "r_x3": 78.877, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.139, "r_y0": 455.872, "r_x1": 294.049, "r_y1": 455.872, "r_x2": 294.049, "r_y2": 448.071, "r_x3": 83.139, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": "For the latter, we instructed annotation staff to minimise", "orig": "For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 466.831, "r_x1": 294.046, "r_y1": 466.831, "r_x2": 294.046, "r_y2": 459.03, "r_x3": 53.798, "r_y3": 459.03, "coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 589.068, "r": 147.485, "b": 598.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484, "t": 613.684, "r": 295.428, "b": 709.156, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", 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The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 384.442, "r": 558.204, "b": 403.202, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.641, "t": 406.36, "r": 558.437, "b": 479.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 497.76, "r": 466.853, "b": 506.827, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.749, "t": 512.4549999999999, "r": 558.431, "b": 706.557, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, 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Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 182.76800000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9782760143280029, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 91.52, "r_y1": 106.05600000000004, "r_x2": 91.52, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "detection", "orig": "detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.976, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 95.976, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "networks on DocLayNet test set. The MRCNN", "orig": "networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.52, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 202.434, "r_y1": 138.933, "r_x2": 202.434, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.085, "r_y0": 138.95899999999995, "r_x1": 247.142, "r_y1": 138.95899999999995, "r_x2": 247.142, "r_y2": 131.48000000000002, "r_x3": 206.085, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.95, "r_y0": 138.933, "r_x1": 294.043, "r_y1": 138.933, "r_x2": 294.043, "r_y2": 131.48000000000002, "r_x3": 250.95, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 149.89200000000005, "r_x1": 294.044, "r_y1": 149.89200000000005, "r_x2": 294.044, "r_y2": 142.43899999999996, "r_x3": 53.52, "r_y3": 142.43899999999996, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 160.851, "r_x1": 294.044, "r_y1": 160.851, "r_x2": 294.044, "r_y2": 153.39800000000002, "r_x3": 53.798, "r_y3": 153.39800000000002, "coord_origin": "TOPLEFT"}, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 171.80999999999995, "r_x1": 295.649, "r_y1": 171.80999999999995, "r_x2": 295.649, "r_y2": 164.35699999999997, "r_x3": 53.798, "r_y3": 164.35699999999997, "coord_origin": "TOPLEFT"}, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 182.76800000000003, "r_x1": 268.624, "r_y1": 182.76800000000003, "r_x2": 268.624, "r_y2": 175.31500000000005, "r_x3": 53.798, "r_y3": 175.31500000000005, "coord_origin": "TOPLEFT"}, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. 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"start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.529, "t": 371.358, "r": 295.556, "b": 576.42, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 379.159, "r_x1": 294.047, "r_y1": 379.159, "r_x2": 294.047, "r_y2": 371.358, "r_x3": 53.798, "r_y3": 371.358, "coord_origin": "TOPLEFT"}, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 390.118, "r_x1": 295.556, "r_y1": 390.118, "r_x2": 295.556, "r_y2": 382.317, "r_x3": 53.798, "r_y3": 382.317, "coord_origin": "TOPLEFT"}, "text": "numbers for human document-layout annotation. Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 401.077, "r_x1": 140.362, "r_y1": 401.077, "r_x2": 140.362, "r_y2": 393.276, "r_x3": 53.798, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.99, "r_y0": 401.121, "r_x1": 175.969, "r_y1": 401.121, "r_x2": 175.969, "r_y2": 393.213, "r_x3": 142.99, "r_y3": 393.213, "coord_origin": "TOPLEFT"}, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.951, "r_y0": 401.077, "r_x1": 294.041, "r_y1": 401.077, "r_x2": 294.041, "r_y2": 393.276, "r_x3": 178.951, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 412.036, "r_x1": 295.425, "r_y1": 412.036, "r_x2": 295.425, "r_y2": 404.235, "r_x3": 53.798, "r_y3": 404.235, "coord_origin": "TOPLEFT"}, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 422.995, "r_x1": 294.043, "r_y1": 422.995, "r_x2": 294.043, "r_y2": 415.194, "r_x3": 53.529, "r_y3": 415.194, "coord_origin": "TOPLEFT"}, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 433.954, "r_x1": 294.048, "r_y1": 433.954, "r_x2": 294.048, "r_y2": 426.153, "r_x3": 53.798, "r_y3": 426.153, "coord_origin": "TOPLEFT"}, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 444.913, "r_x1": 256.806, "r_y1": 444.913, "r_x2": 256.806, "r_y2": 437.112, "r_x3": 53.798, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 259.042, "r_y0": 444.957, "r_x1": 278.105, "r_y1": 444.957, "r_x2": 278.105, "r_y2": 437.049, "r_x3": 259.042, "r_y3": 437.049, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.55, "r_y0": 444.913, "r_x1": 294.044, "r_y1": 444.913, "r_x2": 294.044, "r_y2": 437.112, "r_x3": 280.55, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 455.916, "r_x1": 78.876, "r_y1": 455.916, "r_x2": 78.876, "r_y2": 448.008, "r_x3": 53.798, "r_y3": 448.008, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.877, "r_y0": 455.872, "r_x1": 80.889, "r_y1": 455.872, "r_x2": 80.889, "r_y2": 448.071, "r_x3": 78.877, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.139, "r_y0": 455.872, "r_x1": 294.049, "r_y1": 455.872, "r_x2": 294.049, "r_y2": 448.071, "r_x3": 83.139, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": "For the latter, we instructed annotation staff to minimise", "orig": "For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 466.831, "r_x1": 294.046, "r_y1": 466.831, "r_x2": 294.046, "r_y2": 459.03, "r_x3": 53.798, "r_y3": 459.03, "coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 589.068, "r": 147.485, "b": 598.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484, "t": 613.684, "r": 295.428, "b": 709.156, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 385.937, "r_y0": 252.75400000000002, "r_x1": 392.569, "r_y1": 252.75400000000002, "r_x2": 392.569, "r_y2": 246.67999999999995, "r_x3": 385.937, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "20", 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The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 384.442, "r": 558.204, "b": 403.202, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.641, "t": 406.36, "r": 558.437, "b": 479.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 497.76, "r": 466.853, "b": 506.827, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.749, "t": 512.4549999999999, "r": 558.431, "b": 706.557, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, 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"TOPLEFT"}, "text": "the most visually distinctive in a document.", "orig": "the most visually distinctive in a document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 182.76800000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9782760143280029, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 91.52, "r_y1": 106.05600000000004, "r_x2": 91.52, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "detection", "orig": "detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.976, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 95.976, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "networks on DocLayNet test set. The MRCNN", "orig": "networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.52, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 202.434, "r_y1": 138.933, "r_x2": 202.434, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.085, "r_y0": 138.95899999999995, "r_x1": 247.142, "r_y1": 138.95899999999995, "r_x2": 247.142, "r_y2": 131.48000000000002, "r_x3": 206.085, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.95, "r_y0": 138.933, "r_x1": 294.043, "r_y1": 138.933, "r_x2": 294.043, "r_y2": 131.48000000000002, "r_x3": 250.95, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 149.89200000000005, "r_x1": 294.044, "r_y1": 149.89200000000005, "r_x2": 294.044, "r_y2": 142.43899999999996, "r_x3": 53.52, "r_y3": 142.43899999999996, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 160.851, "r_x1": 294.044, "r_y1": 160.851, "r_x2": 294.044, "r_y2": 153.39800000000002, "r_x3": 53.798, "r_y3": 153.39800000000002, "coord_origin": "TOPLEFT"}, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 171.80999999999995, "r_x1": 295.649, "r_y1": 171.80999999999995, "r_x2": 295.649, "r_y2": 164.35699999999997, "r_x3": 53.798, "r_y3": 164.35699999999997, "coord_origin": "TOPLEFT"}, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 182.76800000000003, "r_x1": 268.624, "r_y1": 182.76800000000003, "r_x2": 268.624, "r_y2": 175.31500000000005, "r_x3": 53.798, "r_y3": 175.31500000000005, "coord_origin": "TOPLEFT"}, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 433.954, "r_x1": 294.048, "r_y1": 433.954, "r_x2": 294.048, "r_y2": 426.153, "r_x3": 53.798, "r_y3": 426.153, "coord_origin": "TOPLEFT"}, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 444.913, "r_x1": 256.806, "r_y1": 444.913, "r_x2": 256.806, "r_y2": 437.112, "r_x3": 53.798, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 259.042, "r_y0": 444.957, "r_x1": 278.105, "r_y1": 444.957, "r_x2": 278.105, "r_y2": 437.049, "r_x3": 259.042, "r_y3": 437.049, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.55, "r_y0": 444.913, "r_x1": 294.044, "r_y1": 444.913, "r_x2": 294.044, "r_y2": 437.112, "r_x3": 280.55, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 455.916, "r_x1": 78.876, "r_y1": 455.916, "r_x2": 78.876, "r_y2": 448.008, "r_x3": 53.798, "r_y3": 448.008, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.877, "r_y0": 455.872, "r_x1": 80.889, "r_y1": 455.872, "r_x2": 80.889, "r_y2": 448.071, "r_x3": 78.877, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.139, "r_y0": 455.872, "r_x1": 294.049, "r_y1": 455.872, "r_x2": 294.049, "r_y2": 448.071, "r_x3": 83.139, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": "For the latter, we instructed annotation staff to minimise", "orig": "For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 466.831, "r_x1": 294.046, "r_y1": 466.831, "r_x2": 294.046, "r_y2": 459.03, "r_x3": 53.798, "r_y3": 459.03, "coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 589.068, "r": 147.485, "b": 598.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484, "t": 613.684, "r": 295.428, "b": 709.156, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 385.937, "r_y0": 252.75400000000002, "r_x1": 392.569, "r_y1": 252.75400000000002, "r_x2": 392.569, "r_y2": 246.67999999999995, "r_x3": 385.937, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "20", 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null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.955, "t": 279.78700000000003, "r": 559.806, "b": 342.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9639935493469238, "cells": [{"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 287.24, "r_x1": 558.479, "r_y1": 287.24, "r_x2": 558.479, "r_y2": 279.78700000000003, "r_x3": 317.955, "r_y3": 279.78700000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 298.199, "r_x1": 559.806, "r_y1": 298.199, "r_x2": 559.806, "r_y2": 290.746, "r_x3": 317.955, "r_y3": 290.746, "coord_origin": "TOPLEFT"}, "text": "R-CNNnetworkwithResNet50backbonetrainedonincreas-", "orig": "R-CNNnetworkwithResNet50backbonetrainedonincreas-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 309.158, "r_x1": 558.201, "r_y1": 309.158, "r_x2": 558.201, "r_y2": 301.705, "r_x3": 317.955, "r_y3": 301.705, "coord_origin": "TOPLEFT"}, "text": "ing fractions of the DocLayNet dataset. The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 384.442, "r": 558.204, "b": 403.202, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.641, "t": 406.36, "r": 558.437, "b": 479.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 497.76, "r": 466.853, "b": 506.827, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.749, "t": 512.4549999999999, "r": 558.431, "b": 706.557, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, 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"TOPLEFT"}, "text": "the most visually distinctive in a document.", "orig": "the most visually distinctive in a document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8662876486778259, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. 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"orig": "78", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.275, "r_y0": 293.636, "r_x1": 253.614, "r_y1": 293.636, "r_x2": 253.614, "r_y2": 285.835, "r_x3": 245.275, "r_y3": 285.835, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 329.575, "r_x1": 131.056, "r_y1": 329.575, "r_x2": 131.056, "r_y2": 320.508, "r_x3": 53.798, "r_y3": 320.508, "coord_origin": "TOPLEFT"}, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", 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The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.488, "r_x1": 531.077, "r_y1": 708.488, "r_x2": 531.077, "r_y2": 700.687, "r_x3": 317.955, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 533.162, "r_y0": 708.532, "r_x1": 557.256, "r_y1": 708.532, "r_x2": 557.256, "r_y2": 700.624, "r_x3": 533.162, "r_y3": 700.624, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 557.255, "r_y0": 708.488, "r_x1": 559.188, "r_y1": 708.488, "r_x2": 559.188, "r_y2": 700.687, "r_x3": 557.255, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "orig": "mAP@0.5-0.95 scores trained on DocLayNet with different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 295.649, "r_y1": 117.01499999999999, "r_x2": 295.649, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "class label sets. The reduced label sets were obtained by ei-", "orig": "class label sets. 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"left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 335.202, "r": 295.558, "b": 529.3050000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 558.142, "r": 295.557, "b": 708.409, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 559.807, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.01499999999999, "r_x1": 455.114, "r_y1": 117.01499999999999, "r_x2": 455.114, "r_y2": 109.56200000000001, "r_x3": 317.955, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "Naive page-wise split will result", "orig": "Naive page-wise split will result", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 459.31, "r_y0": 117.01499999999999, "r_x1": 467.721, "r_y1": 117.01499999999999, "r_x2": 467.721, "r_y2": 109.56200000000001, "r_x3": 459.31, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.909, "r_y0": 116.19299999999998, "r_x1": 477.539, "r_y1": 116.19299999999998, "r_x2": 477.539, "r_y2": 104.81500000000005, "r_x3": 471.909, "r_y3": 104.81500000000005, "coord_origin": "TOPLEFT"}, "text": "/tildelow", "orig": "/tildelow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.54, "r_y0": 117.01499999999999, "r_x1": 559.807, "r_y1": 117.01499999999999, "r_x2": 559.807, "r_y2": 109.56200000000001, "r_x3": 477.54, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 127.97400000000005, "r_x1": 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in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 350.594, "r_x1": 558.2, "r_y1": 350.594, "r_x2": 558.2, "r_y2": 342.793, "r_x3": 317.955, "r_y3": 342.793, "coord_origin": "TOPLEFT"}, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "orig": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 361.553, "r_x1": 393.16, "r_y1": 361.553, "r_x2": 393.16, "r_y2": 353.752, "r_x3": 317.955, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "assumption that the", "orig": "assumption that the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.842, "r_y0": 361.597, "r_x1": 409.149, "r_y1": 361.597, "r_x2": 409.149, "r_y2": 353.689, "r_x3": 395.842, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 412.333, "r_y0": 361.553, "r_x1": 485.023, "r_y1": 361.553, "r_x2": 485.023, "r_y2": 353.752, "r_x3": 412.333, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "is down-mapped to", "orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.704, "r_y0": 361.597, "r_x1": 502.868, "r_y1": 361.597, "r_x2": 502.868, "r_y2": 353.689, "r_x3": 487.704, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 506.055, "r_y0": 361.553, "r_x1": 559.585, "r_y1": 361.553, "r_x2": 559.585, "r_y2": 353.752, "r_x3": 506.055, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 372.512, "r_x1": 558.2, "r_y1": 372.512, "r_x2": 558.2, "r_y2": 364.711, "r_x3": 317.686, "r_y3": 364.711, "coord_origin": "TOPLEFT"}, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 383.47, "r_x1": 558.369, "r_y1": 383.47, "r_x2": 558.369, "r_y2": 375.669, "r_x3": 317.955, "r_y3": 375.669, "coord_origin": "TOPLEFT"}, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 394.429, "r_x1": 558.2, "r_y1": 394.429, "r_x2": 558.2, "r_y2": 386.628, "r_x3": 317.955, "r_y3": 386.628, "coord_origin": "TOPLEFT"}, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 430.333, "r": 549.861, "b": 439.4, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 445.027, "r": 559.714, "b": 595.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 559.188, "b": 708.532, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. 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293.636, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "77", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 320.508, "r": 131.056, "b": 329.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9555686116218567, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 329.575, "r_x1": 131.056, "r_y1": 329.575, "r_x2": 131.056, "r_y2": 320.508, "r_x3": 53.798, "r_y3": 320.508, "coord_origin": "TOPLEFT"}, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 335.202, "r": 295.558, "b": 529.3050000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 558.142, "r": 295.557, "b": 708.409, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 559.807, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.01499999999999, "r_x1": 455.114, "r_y1": 117.01499999999999, "r_x2": 455.114, "r_y2": 109.56200000000001, "r_x3": 317.955, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "Naive page-wise split will result", "orig": "Naive page-wise split will result", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 459.31, "r_y0": 117.01499999999999, "r_x1": 467.721, "r_y1": 117.01499999999999, "r_x2": 467.721, "r_y2": 109.56200000000001, "r_x3": 459.31, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.909, "r_y0": 116.19299999999998, "r_x1": 477.539, "r_y1": 116.19299999999998, "r_x2": 477.539, "r_y2": 104.81500000000005, "r_x3": 471.909, "r_y3": 104.81500000000005, "coord_origin": "TOPLEFT"}, "text": "/tildelow", "orig": "/tildelow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.54, "r_y0": 117.01499999999999, "r_x1": 559.807, "r_y1": 117.01499999999999, "r_x2": 559.807, "r_y2": 109.56200000000001, "r_x3": 477.54, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 127.97400000000005, "r_x1": 341.375, "r_y1": 127.97400000000005, "r_x2": 341.375, "r_y2": 120.52099999999996, "r_x3": 317.955, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ment.", "orig": "ment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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"end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Title", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 426.524, "t": 285.436, "r": 434.863, "b": 293.237, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "77", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 452.562, "t": 285.436, "r": 460.901, "b": 293.237, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "81", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 358.639, "t": 296.793, "r": 369.605, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "All", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 426.524, "t": 296.793, "r": 434.863, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "72", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 452.562, "t": 296.793, "r": 460.901, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "84", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 478.594, "t": 296.793, "r": 486.933, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "78", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 504.632, "t": 296.793, "r": 512.971, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.834, "r": 559.585, "b": 416.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 350.594, "r_x1": 558.2, "r_y1": 350.594, "r_x2": 558.2, "r_y2": 342.793, "r_x3": 317.955, "r_y3": 342.793, "coord_origin": "TOPLEFT"}, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "orig": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 361.553, "r_x1": 393.16, "r_y1": 361.553, "r_x2": 393.16, "r_y2": 353.752, "r_x3": 317.955, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "assumption that the", "orig": "assumption that the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.842, "r_y0": 361.597, "r_x1": 409.149, "r_y1": 361.597, "r_x2": 409.149, "r_y2": 353.689, "r_x3": 395.842, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 412.333, "r_y0": 361.553, "r_x1": 485.023, "r_y1": 361.553, "r_x2": 485.023, "r_y2": 353.752, "r_x3": 412.333, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "is down-mapped to", "orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.704, "r_y0": 361.597, "r_x1": 502.868, "r_y1": 361.597, "r_x2": 502.868, "r_y2": 353.689, "r_x3": 487.704, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 506.055, "r_y0": 361.553, "r_x1": 559.585, "r_y1": 361.553, "r_x2": 559.585, "r_y2": 353.752, "r_x3": 506.055, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 372.512, "r_x1": 558.2, "r_y1": 372.512, "r_x2": 558.2, "r_y2": 364.711, "r_x3": 317.686, "r_y3": 364.711, "coord_origin": "TOPLEFT"}, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 383.47, "r_x1": 558.369, "r_y1": 383.47, "r_x2": 558.369, "r_y2": 375.669, "r_x3": 317.955, "r_y3": 375.669, "coord_origin": "TOPLEFT"}, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 394.429, "r_x1": 558.2, "r_y1": 394.429, "r_x2": 558.2, "r_y2": 386.628, "r_x3": 317.955, "r_y3": 386.628, "coord_origin": "TOPLEFT"}, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 430.333, "r": 549.861, "b": 439.4, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 445.027, "r": 559.714, "b": 595.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 559.188, "b": 708.532, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.488, "r_x1": 531.077, "r_y1": 708.488, "r_x2": 531.077, "r_y2": 700.687, "r_x3": 317.955, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 533.162, "r_y0": 708.532, "r_x1": 557.256, "r_y1": 708.532, "r_x2": 557.256, "r_y2": 700.624, "r_x3": 533.162, "r_y3": 700.624, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 557.255, "r_y0": 708.488, "r_x1": 559.188, "r_y1": 708.488, "r_x2": 559.188, "r_y2": 700.687, "r_x3": 557.255, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "orig": "mAP@0.5-0.95 scores trained on DocLayNet with different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 295.649, "r_y1": 117.01499999999999, "r_x2": 295.649, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "class label sets. The reduced label sets were obtained by ei-", "orig": "class label sets. The reduced label sets were obtained by ei-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 213.239, "r_y1": 127.97400000000005, "r_x2": 213.239, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ther down-mapping or dropping labels.", "orig": "ther down-mapping or dropping labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. 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293.636, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "77", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 320.508, "r": 131.056, "b": 329.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9555686116218567, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 329.575, "r_x1": 131.056, "r_y1": 329.575, "r_x2": 131.056, "r_y2": 320.508, "r_x3": 53.798, "r_y3": 320.508, "coord_origin": "TOPLEFT"}, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 335.202, "r": 295.558, "b": 529.3050000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 558.142, "r": 295.557, "b": 708.409, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 559.807, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.01499999999999, "r_x1": 455.114, "r_y1": 117.01499999999999, "r_x2": 455.114, "r_y2": 109.56200000000001, "r_x3": 317.955, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "Naive page-wise split will result", "orig": "Naive page-wise split will result", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 459.31, "r_y0": 117.01499999999999, "r_x1": 467.721, "r_y1": 117.01499999999999, "r_x2": 467.721, "r_y2": 109.56200000000001, "r_x3": 459.31, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.909, "r_y0": 116.19299999999998, "r_x1": 477.539, "r_y1": 116.19299999999998, "r_x2": 477.539, "r_y2": 104.81500000000005, "r_x3": 471.909, "r_y3": 104.81500000000005, "coord_origin": "TOPLEFT"}, "text": "/tildelow", "orig": "/tildelow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.54, "r_y0": 117.01499999999999, "r_x1": 559.807, "r_y1": 117.01499999999999, "r_x2": 559.807, "r_y2": 109.56200000000001, "r_x3": 477.54, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 127.97400000000005, "r_x1": 341.375, "r_y1": 127.97400000000005, "r_x2": 341.375, "r_y2": 120.52099999999996, "r_x3": 317.955, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ment.", "orig": "ment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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"row_header": false, "row_section": false}, {"bbox": {"l": 504.632, "t": 296.793, "r": 512.971, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.834, "r": 559.585, "b": 416.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 350.594, "r_x1": 558.2, "r_y1": 350.594, "r_x2": 558.2, "r_y2": 342.793, "r_x3": 317.955, "r_y3": 342.793, "coord_origin": "TOPLEFT"}, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "orig": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 361.553, "r_x1": 393.16, "r_y1": 361.553, "r_x2": 393.16, "r_y2": 353.752, "r_x3": 317.955, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "assumption that the", "orig": "assumption that the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.842, "r_y0": 361.597, "r_x1": 409.149, "r_y1": 361.597, "r_x2": 409.149, "r_y2": 353.689, "r_x3": 395.842, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 412.333, "r_y0": 361.553, "r_x1": 485.023, "r_y1": 361.553, "r_x2": 485.023, "r_y2": 353.752, "r_x3": 412.333, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "is down-mapped to", "orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.704, "r_y0": 361.597, "r_x1": 502.868, "r_y1": 361.597, "r_x2": 502.868, "r_y2": 353.689, "r_x3": 487.704, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 506.055, "r_y0": 361.553, "r_x1": 559.585, "r_y1": 361.553, "r_x2": 559.585, "r_y2": 353.752, "r_x3": 506.055, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 372.512, "r_x1": 558.2, "r_y1": 372.512, "r_x2": 558.2, "r_y2": 364.711, "r_x3": 317.686, "r_y3": 364.711, "coord_origin": "TOPLEFT"}, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 383.47, "r_x1": 558.369, "r_y1": 383.47, "r_x2": 558.369, "r_y2": 375.669, "r_x3": 317.955, "r_y3": 375.669, "coord_origin": "TOPLEFT"}, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 394.429, "r_x1": 558.2, "r_y1": 394.429, "r_x2": 558.2, "r_y2": 386.628, "r_x3": 317.955, "r_y3": 386.628, "coord_origin": "TOPLEFT"}, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 430.333, "r": 549.861, "b": 439.4, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 445.027, "r": 559.714, "b": 595.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 559.188, "b": 708.532, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.488, "r_x1": 531.077, "r_y1": 708.488, "r_x2": 531.077, "r_y2": 700.687, "r_x3": 317.955, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 533.162, "r_y0": 708.532, "r_x1": 557.256, "r_y1": 708.532, "r_x2": 557.256, "r_y2": 700.624, "r_x3": 533.162, "r_y3": 700.624, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 557.255, "r_y0": 708.488, "r_x1": 559.188, "r_y1": 708.488, "r_x2": 559.188, "r_y2": 700.687, "r_x3": 557.255, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.322, "r_y1": 95.09699999999998, "r_x2": 294.322, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 295.649, "r_y1": 106.05600000000004, "r_x2": 295.649, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "orig": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "cLayNet data-sets. By evaluating on common label classes of", "orig": "cLayNet data-sets. By evaluating on common label classes of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "each dataset, we observe that the DocLayNet-trained model", "orig": "each dataset, we observe that the DocLayNet-trained model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 294.044, "r_y1": 138.933, "r_x2": 294.044, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "has much less pronounced variations in performance across", "orig": "has much less pronounced variations in performance across", 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specified in table 3, and", "orig": "or excluded DocLayNet's other labels as specified in table 3, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.07, "r_x1": 117.129, "r_y1": 421.07, "r_x2": 117.129, "r_y2": 413.269, "r_x3": 53.798, "r_y3": 413.269, "coord_origin": "TOPLEFT"}, "text": "also PubLayNet's", "orig": "also PubLayNet's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.362, "r_y0": 421.114, "r_x1": 132.434, "r_y1": 421.114, "r_x2": 132.434, "r_y2": 413.206, "r_x3": 119.362, "r_y3": 413.206, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 135.177, "r_y0": 421.07, "r_x1": 142.544, "r_y1": 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421.07, "r_x2": 294.046, "r_y2": 413.269, "r_x3": 163.881, "r_y3": 413.269, "coord_origin": "TOPLEFT"}, "text": "Note that the different clustering of", "orig": "Note that the different clustering of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.029, "r_x1": 294.046, "r_y1": 432.029, "r_x2": 294.046, "r_y2": 424.228, "r_x3": 53.798, "r_y3": 424.228, "coord_origin": "TOPLEFT"}, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 442.988, "r_x1": 106.207, "r_y1": 442.988, "r_x2": 106.207, "r_y2": 435.187, "r_x3": 53.798, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.448, "r_y0": 443.032, "r_x1": 123.305, "r_y1": 443.032, "r_x2": 123.305, "r_y2": 435.124, "r_x3": 108.448, "r_y3": 435.124, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.305, "r_y0": 442.988, "r_x1": 125.278, "r_y1": 442.988, "r_x2": 125.278, "r_y2": 435.187, "r_x3": 123.305, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 453.946, "r_x1": 294.276, "r_y1": 453.946, "r_x2": 294.276, "r_y2": 446.145, "r_x3": 63.761, "r_y3": 446.145, "coord_origin": "TOPLEFT"}, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 464.905, "r_x1": 62.989, "r_y1": 464.905, "r_x2": 62.989, "r_y2": 457.104, "r_x3": 53.798, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.853, "r_y0": 464.949, "r_x1": 88.947, "r_y1": 464.949, "r_x2": 88.947, "r_y2": 457.041, "r_x3": 64.853, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.02, "r_y0": 464.905, "r_x1": 104.245, 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We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 295.595, "r_x1": 558.435, "r_y1": 295.595, "r_x2": 558.435, "r_y2": 287.794, "r_x3": 327.918, "r_y3": 287.794, "coord_origin": "TOPLEFT"}, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 306.554, "r_x1": 558.2, "r_y1": 306.554, "r_x2": 558.2, "r_y2": 298.753, "r_x3": 317.955, "r_y3": 298.753, "coord_origin": "TOPLEFT"}, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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"TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "orig": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 697.8199999999999, "r_x1": 558.972, "r_y1": 697.8199999999999, "r_x2": 558.972, "r_y2": 691.752, "r_x3": 333.391, "r_y3": 691.752, "coord_origin": "TOPLEFT"}, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "orig": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 705.79, "r_x1": 558.2, "r_y1": 705.79, "r_x2": 558.2, "r_y2": 699.722, "r_x3": 333.391, "r_y3": 699.722, "coord_origin": "TOPLEFT"}, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "orig": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 24, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7984216809272766, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.881, "r_y0": 421.07, "r_x1": 294.046, "r_y1": 421.07, "r_x2": 294.046, "r_y2": 413.269, "r_x3": 163.881, "r_y3": 413.269, "coord_origin": "TOPLEFT"}, "text": "Note that the different clustering of", "orig": "Note that the different clustering of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.029, "r_x1": 294.046, "r_y1": 432.029, "r_x2": 294.046, "r_y2": 424.228, "r_x3": 53.798, "r_y3": 424.228, "coord_origin": "TOPLEFT"}, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 605.999, "r": 156.005, "b": 615.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.529, "t": 620.694, "r": 295.558, "b": 705.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.955, "t": 86.798, "r": 405.73, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 559.714, "b": 186.00599999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 189.164, "r": 559.717, "b": 284.636, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 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"coord_origin": "TOPLEFT"}, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 442.988, "r_x1": 106.207, "r_y1": 442.988, "r_x2": 106.207, "r_y2": 435.187, "r_x3": 53.798, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.448, "r_y0": 443.032, "r_x1": 123.305, "r_y1": 443.032, "r_x2": 123.305, "r_y2": 435.124, "r_x3": 108.448, "r_y3": 435.124, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 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Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 446.145, "r": 295.559, "b": 585.453, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 453.946, "r_x1": 294.276, "r_y1": 453.946, "r_x2": 294.276, "r_y2": 446.145, "r_x3": 63.761, "r_y3": 446.145, "coord_origin": "TOPLEFT"}, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 464.905, "r_x1": 62.989, "r_y1": 464.905, "r_x2": 62.989, "r_y2": 457.104, "r_x3": 53.798, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.853, "r_y0": 464.949, "r_x1": 88.947, "r_y1": 464.949, "r_x2": 88.947, "r_y2": 457.041, "r_x3": 64.853, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.02, "r_y0": 464.905, "r_x1": 104.245, "r_y1": 464.905, "r_x2": 104.245, "r_y2": 457.104, "r_x3": 91.02, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.108, "r_y0": 464.949, "r_x1": 124.781, "r_y1": 464.949, "r_x2": 124.781, "r_y2": 457.041, "r_x3": 106.108, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.855, "r_y0": 464.905, "r_x1": 277.632, "r_y1": 464.905, "r_x2": 277.632, "r_y2": 457.104, "r_x3": 126.855, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 605.999, "r": 156.005, "b": 615.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.529, "t": 620.694, "r": 295.558, "b": 705.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.955, "t": 86.798, "r": 405.73, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 559.714, "b": 186.00599999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 189.164, "r": 559.717, "b": 284.636, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.623, "t": 287.794, "r": 558.435, "b": 317.513, "coord_origin": "TOPLEFT"}, "confidence": 0.9676101207733154, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 295.595, "r_x1": 558.435, "r_y1": 295.595, "r_x2": 558.435, "r_y2": 287.794, "r_x3": 327.918, "r_y3": 287.794, "coord_origin": "TOPLEFT"}, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 306.554, "r_x1": 558.2, "r_y1": 306.554, "r_x2": 558.2, "r_y2": 298.753, "r_x3": 317.955, "r_y3": 298.753, "coord_origin": "TOPLEFT"}, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 317.513, "r_x1": 535.65, "r_y1": 317.513, "r_x2": 535.65, "r_y2": 309.712, "r_x3": 317.623, "r_y3": 309.712, "coord_origin": "TOPLEFT"}, "text": "work will inspire the research community to close that gap.", "orig": "work will inspire the research community to close that gap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.955, "t": 336.03, "r": 387.37, "b": 345.097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 345.097, "r_x1": 387.37, "r_y1": 345.097, "r_x2": 387.37, "r_y2": 336.03, "r_x3": 317.955, "r_y3": 336.03, "coord_origin": "TOPLEFT"}, "text": "REFERENCES", "orig": "REFERENCES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 349.037, "r": 558.201, "b": 371.078, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.198, "r_y0": 355.105, "r_x1": 329.406, "r_y1": 355.105, "r_x2": 329.406, "r_y2": 349.037, "r_x3": 321.198, "r_y3": 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Document Analysis and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 371.078, "r_x1": 365.596, "r_y1": 371.078, "r_x2": 365.596, "r_y2": 364.928, "r_x3": 333.391, "r_y3": 364.928, "coord_origin": "TOPLEFT"}, "text": "Recognition", "orig": "Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.596, "r_y0": 371.045, "r_x1": 367.13, "r_y1": 371.045, "r_x2": 367.13, "r_y2": 364.977, "r_x3": 365.596, "r_y3": 364.977, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.874, "r_y0": 371.045, "r_x1": 434.295, "r_y1": 371.045, "r_x2": 434.295, "r_y2": 364.977, "r_x3": 368.874, "r_y3": 364.977, "coord_origin": "TOPLEFT"}, "text": "pages 1449-1453, 2013.", "orig": "pages 1449-1453, 2013.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013."}, {"label": "list_item", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 321.198, "t": 372.947, "r": 559.38, "b": 402.958, "coord_origin": "TOPLEFT"}, "confidence": 0.9480945467948914, "cells": [{"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.198, "r_y0": 379.015, "r_x1": 329.406, "r_y1": 379.015, "r_x2": 329.406, "r_y2": 372.947, "r_x3": 321.198, "r_y3": 372.947, "coord_origin": "TOPLEFT"}, "text": "[2]", "orig": "[2]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 379.015, "r_x1": 547.031, "r_y1": 379.015, "r_x2": 547.031, "r_y2": 372.947, "r_x3": 333.391, "r_y3": 372.947, "coord_origin": "TOPLEFT"}, "text": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher.", "orig": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 551.818, "r_y0": 379.015, "r_x1": 559.38, "r_y1": 379.015, "r_x2": 559.38, "r_y2": 372.947, "r_x3": 551.818, "r_y3": 372.947, "coord_origin": "TOPLEFT"}, "text": "Ic-", "orig": "Ic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 386.985, "r_x1": 441.477, "r_y1": 386.985, "r_x2": 441.477, "r_y2": 380.917, "r_x3": 333.391, "r_y3": 380.917, "coord_origin": "TOPLEFT"}, "text": "dar2017 competition on recognition", "orig": "dar2017 competition on recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.664, "r_y0": 386.985, "r_x1": 450.454, "r_y1": 386.985, "r_x2": 450.454, "r_y2": 380.917, "r_x3": 444.664, "r_y3": 380.917, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.634, "r_y0": 386.985, "r_x1": 559.38, "r_y1": 386.985, "r_x2": 559.38, "r_y2": 380.917, "r_x3": 453.634, "r_y3": 380.917, "coord_origin": "TOPLEFT"}, "text": "documents with complex layouts -", "orig": "documents with complex layouts -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 394.955, "r_x1": 367.434, "r_y1": 394.955, "r_x2": 367.434, "r_y2": 388.887, "r_x3": 333.391, "r_y3": 388.887, "coord_origin": "TOPLEFT"}, "text": "rdcl2017. In", "orig": "rdcl2017. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.174, "r_y0": 394.988, "r_x1": 558.204, "r_y1": 394.988, "r_x2": 558.204, "r_y2": 388.838, "r_x3": 369.174, "r_y3": 388.838, "coord_origin": "TOPLEFT"}, "text": "2017 14th IAPR International Conference on Document Analysis and", "orig": "2017 14th IAPR International Conference on Document Analysis and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 402.958, "r_x1": 390.876, "r_y1": 402.958, "r_x2": 390.876, "r_y2": 396.808, "r_x3": 333.391, "r_y3": 396.808, "coord_origin": "TOPLEFT"}, "text": "Recognition (ICDAR)", "orig": "Recognition (ICDAR)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.877, "r_y0": 402.925, "r_x1": 392.411, "r_y1": 402.925, "r_x2": 392.411, "r_y2": 396.857, "r_x3": 390.877, "r_y3": 396.857, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 394.155, "r_y0": 402.925, "r_x1": 492.178, "r_y1": 402.925, "r_x2": 492.178, "r_y2": 396.857, "r_x3": 394.155, "r_y3": 396.857, "coord_origin": "TOPLEFT"}, "text": "volume 01, pages 1404-1410, 2017.", "orig": "volume 01, pages 1404-1410, 2017.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. 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IEEE Computer Society, Oct 2017."}, {"label": "list_item", "id": 23, "page_no": 7, "cluster": {"id": 23, "label": "list_item", "bbox": {"l": 317.955, "t": 675.811, "r": 558.972, "b": 705.79, "coord_origin": "TOPLEFT"}, "confidence": 0.889543354511261, "cells": [{"index": 227, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.879, "r_x1": 329.406, "r_y1": 681.879, "r_x2": 329.406, "r_y2": 675.811, "r_x3": 317.955, "r_y3": 675.811, "coord_origin": "TOPLEFT"}, "text": "[13]", "orig": "[13]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 681.879, "r_x1": 558.967, "r_y1": 681.879, "r_x2": 558.967, "r_y2": 675.811, "r_x3": 333.391, "r_y3": 675.811, "coord_origin": "TOPLEFT"}, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "orig": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 229, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.182, "r_y0": 689.849, "r_x1": 558.967, "r_y1": 689.849, "r_x2": 558.967, "r_y2": 683.781, "r_x3": 333.182, "r_y3": 683.781, "coord_origin": "TOPLEFT"}, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "orig": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 697.8199999999999, "r_x1": 558.972, "r_y1": 697.8199999999999, "r_x2": 558.972, "r_y2": 691.752, "r_x3": 333.391, "r_y3": 691.752, "coord_origin": "TOPLEFT"}, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "orig": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 705.79, "r_x1": 558.2, "r_y1": 705.79, "r_x2": 558.2, "r_y2": 699.722, "r_x3": 333.391, "r_y3": 699.722, "coord_origin": "TOPLEFT"}, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "orig": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 149.89200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9824112057685852, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.322, "r_y1": 95.09699999999998, "r_x2": 294.322, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 295.649, "r_y1": 106.05600000000004, "r_x2": 295.649, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "orig": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "cLayNet data-sets. By evaluating on common label classes of", "orig": "cLayNet data-sets. By evaluating on common label classes of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "each dataset, we observe that the DocLayNet-trained model", "orig": "each dataset, we observe that the DocLayNet-trained model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 294.044, "r_y1": 138.933, "r_x2": 294.044, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "has much less pronounced variations in performance across", "orig": "has much less pronounced variations in performance across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 149.89200000000005, "r_x1": 101.159, "r_y1": 149.89200000000005, "r_x2": 101.159, "r_y2": 142.43899999999996, "r_x3": 53.798, "r_y3": 142.43899999999996, "coord_origin": "TOPLEFT"}, "text": "all datasets.", "orig": "all datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. 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167.381, "r_y3": 391.351, "coord_origin": "TOPLEFT"}, "text": "Before training, we either mapped", "orig": "Before training, we either mapped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 410.111, "r_x1": 294.047, "r_y1": 410.111, "r_x2": 294.047, "r_y2": 402.31, "r_x3": 53.798, "r_y3": 402.31, "coord_origin": "TOPLEFT"}, "text": "or excluded DocLayNet's other labels as specified in table 3, and", "orig": "or excluded DocLayNet's other labels as specified in table 3, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.07, "r_x1": 117.129, "r_y1": 421.07, "r_x2": 117.129, "r_y2": 413.269, "r_x3": 53.798, "r_y3": 413.269, "coord_origin": "TOPLEFT"}, "text": "also PubLayNet's", "orig": "also PubLayNet's", "text_direction": "left_to_right", 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"coord_origin": "TOPLEFT"}, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 442.988, "r_x1": 106.207, "r_y1": 442.988, "r_x2": 106.207, "r_y2": 435.187, "r_x3": 53.798, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.448, "r_y0": 443.032, "r_x1": 123.305, "r_y1": 443.032, "r_x2": 123.305, "r_y2": 435.124, "r_x3": 108.448, "r_y3": 435.124, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.305, "r_y0": 442.988, "r_x1": 125.278, "r_y1": 442.988, "r_x2": 125.278, "r_y2": 435.187, "r_x3": 123.305, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 446.145, "r": 295.559, "b": 585.453, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 453.946, "r_x1": 294.276, "r_y1": 453.946, "r_x2": 294.276, "r_y2": 446.145, "r_x3": 63.761, "r_y3": 446.145, "coord_origin": "TOPLEFT"}, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 464.905, "r_x1": 62.989, "r_y1": 464.905, "r_x2": 62.989, "r_y2": 457.104, "r_x3": 53.798, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.853, "r_y0": 464.949, "r_x1": 88.947, "r_y1": 464.949, "r_x2": 88.947, "r_y2": 457.041, "r_x3": 64.853, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.02, "r_y0": 464.905, "r_x1": 104.245, "r_y1": 464.905, "r_x2": 104.245, "r_y2": 457.104, "r_x3": 91.02, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.108, "r_y0": 464.949, "r_x1": 124.781, "r_y1": 464.949, "r_x2": 124.781, "r_y2": 457.041, "r_x3": 106.108, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.855, "r_y0": 464.905, "r_x1": 277.632, "r_y1": 464.905, "r_x2": 277.632, "r_y2": 457.104, "r_x3": 126.855, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 605.999, "r": 156.005, "b": 615.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.529, "t": 620.694, "r": 295.558, "b": 705.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.955, "t": 86.798, "r": 405.73, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 559.714, "b": 186.00599999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 189.164, "r": 559.717, "b": 284.636, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.623, "t": 287.794, "r": 558.435, "b": 317.513, "coord_origin": "TOPLEFT"}, "confidence": 0.9676101207733154, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 295.595, "r_x1": 558.435, "r_y1": 295.595, "r_x2": 558.435, "r_y2": 287.794, "r_x3": 327.918, "r_y3": 287.794, "coord_origin": "TOPLEFT"}, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 306.554, "r_x1": 558.2, "r_y1": 306.554, "r_x2": 558.2, "r_y2": 298.753, "r_x3": 317.955, "r_y3": 298.753, "coord_origin": "TOPLEFT"}, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 317.513, "r_x1": 535.65, "r_y1": 317.513, "r_x2": 535.65, "r_y2": 309.712, "r_x3": 317.623, "r_y3": 309.712, "coord_origin": "TOPLEFT"}, "text": "work will inspire the research community to close that gap.", "orig": "work will inspire the research community to close that gap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.955, "t": 336.03, "r": 387.37, "b": 345.097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 345.097, "r_x1": 387.37, "r_y1": 345.097, "r_x2": 387.37, "r_y2": 336.03, "r_x3": 317.955, "r_y3": 336.03, "coord_origin": "TOPLEFT"}, "text": "REFERENCES", "orig": "REFERENCES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 349.037, "r": 558.201, "b": 371.078, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.198, "r_y0": 355.105, "r_x1": 329.406, "r_y1": 355.105, "r_x2": 329.406, "r_y2": 349.037, "r_x3": 321.198, "r_y3": 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Inference timing results for all experiments -5.1 -We have chosen the PubTabNet data set to perform HPO, since it includes a +order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. +5.1 Hyper Parameter Optimization +We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. - + +# enc-layers# dec-layersLanguageTEDsTEDsTEDsmAPInference +# enc-layers# dec-layersLanguagesimplecomplexall(0.75)time (secs) +66OTSL HTML0.965 0.9690.934 0.9270.955 0.9550.88 0.8572.73 5.39 +44OTSL HTML0.938 0.9520.904 0.9090.927 0.9380.853 0.8431.97 3.77 +24OTSL HTML0.923 0.9450.897 0.9010.915 0.9310.859 0.8341.91 3.81 +42OTSL HTML0.952 0.9440.92 0.9030.942 0.9310.857 0.8241.22 2
TableTable 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.
-Table -5.2 -We picked the model parameter configuration that produced the best prediction -Additionally, +Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. +5.2 Quantitative Results +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables. +Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. 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Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [134.765, 274.65100000000007, 264.408, 283.878], "page": 1, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 174.52600000000007, 480.722, 267.509], "page": 1, "span": [0, 557], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 126.70600000000002, 480.599, 171.86699999999996], "page": 1, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [{"prov": [{"bbox": [139.66741943359375, 322.5054626464844, 475.00927734375, 454.45458984375], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "table", "payload": null, "#-cols": 8, "#-rows": 6, "data": [[{"bbox": [144.592, 341.627, 183.828, 362.952], "spans": [[0, 0], [1, 0]], "text": "# enc-layers", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [192.195, 341.627, 231.425, 362.952], "spans": [[0, 1], [1, 1]], "text": "# dec-layers", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [239.798, 347.106, 278.333, 355.48], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [324.67, 341.627, 348.263, 350.001], "spans": [[0, 3], [0, 4], [0, 5]], "text": "TEDs", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 6], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [324.67, 341.627, 348.263, 350.001], "spans": [[0, 3], [0, 4], [0, 5]], "text": "TEDs", "type": "col_header", "col": 4, "col-header": true, "col-span": [3, 6], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [324.67, 341.627, 348.263, 350.001], "spans": [[0, 3], [0, 4], [0, 5]], "text": "TEDs", "type": "col_header", "col": 5, "col-header": true, "col-span": [3, 6], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [396.271, 341.627, 417.123, 350.001], "spans": [[0, 6]], "text": "mAP", "type": "col_header", "col": 6, "col-header": true, "col-span": [6, 7], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [430.771, 341.627, 467.141, 350.001], "spans": [[0, 7]], "text": "Inference", "type": "col_header", "col": 7, "col-header": true, "col-span": [7, 8], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [144.592, 341.627, 183.828, 362.952], "spans": [[0, 0], [1, 0]], "text": "# enc-layers", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [0, 2]}, {"bbox": [192.195, 341.627, 231.425, 362.952], "spans": [[0, 1], [1, 1]], "text": "# dec-layers", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [0, 2]}, {"bbox": [239.798, 347.106, 278.333, 355.48], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [0, 2]}, {"bbox": [286.686, 354.578, 312.325, 362.952], "spans": [[1, 3]], "text": "simple", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [320.702, 354.578, 353.718, 362.952], "spans": 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[2, 3]}, {"bbox": [396.206, 366.637, 417.198, 389.254], "spans": [[2, 6]], "text": "0.88 0.857", "type": "body", "col": 6, "col-header": false, "col-span": [6, 7], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [439.527, 366.637, 458.388, 389.254], "spans": [[2, 7]], "text": "2.73 5.39", "type": "body", "col": 7, "col-header": false, "col-span": [7, 8], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [161.906, 399.709, 166.515, 408.083], "spans": [[3, 0]], "text": "4", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [209.509, 399.709, 214.118, 408.083], "spans": [[3, 1]], "text": "4", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [245.176, 394.23, 272.94, 415.555], "spans": [[3, 2]], "text": "OTSL HTML", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": 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6]], "text": "0.857 0.824", "type": "body", "col": 6, "col-header": false, "col-span": [6, 7], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [439.527, 445.541, 458.388, 468.158], "spans": [[5, 7]], "text": "1.22 2", "type": "body", "col": 7, "col-header": false, "col-span": [7, 8], "row": 5, "row-header": false, "row-span": [5, 6]}]], "model": null, "bounding-box": null}], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 792.0, "page": 1, "width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.md b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.md index ba224652..2adbfbbd 100644 --- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.md +++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.md @@ -1,15 +1,21 @@ -order to compute the TED score. Inference timing results for all experiments +order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. -## 5.1 +## 5.1 Hyper Parameter Optimization -We have chosen the PubTabNet data set to perform HPO, since it includes a +We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. -Table +Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP | Inference | +|----------------|----------------|------------|-------------|-------------|-------------|-------------|-------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | (0.75) | time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | +## 5.2 Quantitative Results -## 5.2 +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables. -We picked the model parameter configuration that produced the best prediction - -Additionally, \ No newline at end of file +Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json index e13abd7b..309b3c4d 100644 --- a/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json +++ b/tests/data/groundtruth/docling_v1/2305.03393v1-pg9.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"r_y1": 468.158, "r_x2": 417.198, "r_y2": 459.784, "r_x3": 396.206, "r_y3": 459.784, "coord_origin": "TOPLEFT"}, "text": "0.824", "orig": "0.824", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.653, "r_y0": 468.158, "r_x1": 451.262, "r_y1": 468.158, "r_x2": 451.262, "r_y2": 459.784, "r_x3": 446.653, "r_y3": 459.784, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.591, "b": 128.16999999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. 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Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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"confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, 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It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1.doctags.txt b/tests/data/groundtruth/docling_v1/2305.03393v1.doctags.txt index cc7eb513..4bd0ad47 100644 --- a/tests/data/groundtruth/docling_v1/2305.03393v1.doctags.txt +++ b/tests/data/groundtruth/docling_v1/2305.03393v1.doctags.txt @@ -1,128 +1,141 @@ -Optimized Table Tokenization for Table Structure -Maksym Lysak +Optimized Table Tokenization for Table Structure Recognition +Maksym Lysak [0000 - 0002 - 3723 - 6960] , Ahmed Nassar [0000 - 0002 - 9468 - 0822] , Nikolaos Livathinos [0000 - 0001 - 8513 - 3491] , Christoph Auer [0000 - 0001 - 5761 - 0422] , [0000 - 0002 - 8088 - 0823] and Peter Staar IBM Research {mly,ahn,nli,cau,taa}@zurich.ibm.com -Abstract. -Keywords: -1 -Tables are ubiquitous in documents such as scientific papers, patents, reports, -In modern document understanding systems [1,15], table extraction is typi- +Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community. +Keywords: Table Structure Recognition · Data Representation · Transformers · Optimization. +1 Introduction +Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods. +In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of
-Fig. 1. +Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).
-today, -Recently -While the majority of research in TSR is currently focused on the develop- -The main contribution of this paper is the introduction of a new optimised ta- -The paper is structured as follows. In section 2, we give an overview of the -2 -Approaches to formalize the logical structure and layout of tables in electronic -Other work [20] aims at predicting a grid for each table and deciding which cells -Within the -Im2Seq approaches have shown to be well-suited for the TSR task and allow a -3 -All known Im2Seq based models for TSR fundamentally work in similar ways. -ulary and can be interpreted as a table structure. For example, with the HTML +today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]. +Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR. +While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments. +The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML. +The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps. +2 Related Work +Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell. +Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence. +Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. +Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task. +3 Problem Statement +All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab- +ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.
-Fig. 2. +Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.
-Obviously, HTML and other general-purpose markup languages were not de- -Additionally, it would be desirable if the representation would easily allow -In a valid HTML table, the token sequence must describe a 2D grid of table -generation. Implicitly, this also means that Im2Seq models need to learn these -In practice, we observe two major issues with prediction quality when train- -4 -To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, -4.1 -In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines +Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. +Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible. +In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence +generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output. +In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content. +4 Optimised Table Structure Language +To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture. +4.1 Language Definition +In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid. The OTSL vocabulary is comprised of the following tokens: -- - -- - -- - -- - -- - -A notable attribute of OTSL is that it has the capability of achieving lossless +- -"C" cell a new table cell that either has or does not have cell content +- -"L" cell left-looking cell , merging with the left neighbor cell to create a span +- -"U" cell up-looking cell , merging with the upper neighbor cell to create a span +- -"X" cell cross cell , to merge with both left and upper neighbor cells +- -"NL" new-line , switch to the next row. +A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.
-Fig. 3. +Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding
-4.2 +4.2 Language Syntax The OTSL representation follows these syntax rules: -- 1. -- 2. -3. -- 4. -- 5. -- 6. -- The left neighbour of an "X" cell must be either another "X" cell or a "U" -The application of these rules gives OTSL a set of unique properties. First -These characteristics can be easily learned by sequence generator networks, -reduces significantly the column drift seen in the HTML based models (see Fig- -4.3 -The design of OTSL allows to validate a table structure easily on an unfinished -5 -To evaluate the impact of OTSL on prediction accuracy and inference times, we +- 1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell. +- 2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell. +3. Cross cell rule : +- The left neighbour of an "X" cell must be either another "X" cell or a "U" cell, and the upper neighbour of an "X" cell must be either another "X" cell or an "L" cell. +- 4. First row rule : Only "L" cells and "C" cells are allowed in the first row. +- 5. First column rule : Only "U" cells and "C" cells are allowed in the first column. +- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token. +The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a "C"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid. +These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern +reduces significantly the column drift seen in the HTML based models (see Figure 5). +4.3 Error-detection and -mitigation +The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied. +5 Experiments +To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.
-Fig. 4. +Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.
-We rely on standard metrics such as Tree Edit Distance score (TEDs) for -order to compute the TED score. Inference timing results for all experiments -5.1 -We have chosen the PubTabNet data set to perform HPO, since it includes a +We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in +order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. +5.1 Hyper Parameter Optimization +We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. - + +# enc-layers# dec-layersLanguageTEDsTEDsTEDsmAPInference +# enc-layers# dec-layersLanguagesimplecomplexall(0.75)time (secs) +66OTSL HTML0.965 0.9690.934 0.9270.955 0.9550.88 0.8572.73 5.39 +44OTSL HTML0.938 0.9520.904 0.9090.927 0.9380.853 0.8431.97 3.77 +24OTSL HTML0.923 0.9450.897 0.9010.915 0.9310.859 0.8341.91 3.81 +42OTSL HTML0.952 0.9440.92 0.9030.942 0.9310.857 0.8241.22 2
TableTable 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.
-Table -5.2 -We picked the model parameter configuration that produced the best prediction -Additionally, +Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. +5.2 Quantitative Results +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables. +Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. - + +Data setLanguageTEDsTEDsTEDsmAP(0.75)Inference time (secs) +Data setLanguagesimplecomplexallmAP(0.75)Inference time (secs) +PubTabNetOTSL HTML0.965 0.9690.934 0.9270.955 0.9550.88 0.8572.73 5.39 +FinTabNetOTSL HTML0.955 0.9170.961 0.9220.959 0.920.862 0.7221.85 3.26 +PubTables-1MOTSL HTML0.987 0.9830.964 0.9440.977 0.9660.896 0.8891.79 3.26
Table 2.Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).
-Table 2. -5.3 -To illustrate +Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). +5.3 Qualitative Results +To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.
-Fig. 5. +Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. μ
+μ +≥
-Fig. 6. +Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406_003_01.png" PubTabNet.
-6 -We demonstrated that representing tables in HTML for the task of table struc- -First and foremost, given the same network configuration, inference time for -Secondly, OTSL has more inherent structure and a significantly restricted vo- +6 Conclusion +We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits. +First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1). +Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation. References -- 1. -- 2. -- 3. -- 4. -- 5. -- 6. -- 7. -- 8. -- 9. -- 10. -- 11. -- 12. -- 13. -- 14. -- 15. -- 16. -- 17. -- 18. -- 19. -- 20. -- 21. -- 22. -- 23. +- 1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785 +- 2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Fornés, A., Shafait, F. 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In essence, the OTSL defines", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.709, 257.701, 409.311, 266.49800000000005], "page": 6, "span": [0, 57], "__ref_s3_data": null}], "text": "The OTSL vocabulary is comprised of the following tokens:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [140.993, 235.22299999999996, 146.72, 244.02999999999997], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.993, 222.63, 146.72, 231.437], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.993, 198.08100000000002, 146.72, 206.88800000000003], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.993, 173.53300000000002, 146.72, 182.34000000000003], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.993, 160.93899999999996, 146.72, 169.74599999999998], "page": 6, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [149.709, 139.10000000000002, 480.593, 147.89699999999993], "page": 6, "span": [0, 79], "__ref_s3_data": null}], "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [134.765, 477.897, 149.402, 486.704], "page": 7, "span": [0, 3], "__ref_s3_data": null}], "text": "4.2", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 457.955, 363.796, 466.752], "page": 7, "span": [0, 51], "__ref_s3_data": null}], "text": "The OTSL representation follows these syntax rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [138.973, 436.022, 146.72, 444.819], "page": 7, "span": [0, 2], "__ref_s3_data": null}], "text": "- 1.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 412.108, 146.72, 420.905], "page": 7, "span": [0, 2], "__ref_s3_data": null}], "text": "- 2.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 388.195, 146.72, 396.992], "page": 7, "span": [0, 2], "__ref_s3_data": null}], "text": "3.", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [138.973, 340.367, 146.72, 349.164], "page": 7, "span": [0, 2], "__ref_s3_data": null}], "text": "- 4.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 328.409, 146.72, 337.206], "page": 7, "span": [0, 2], "__ref_s3_data": null}], "text": "- 5.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 304.496, 146.72, 313.293], "page": 7, "span": [0, 2], "__ref_s3_data": null}], "text": "- 6.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.701, 376.236, 480.592, 385.033], "page": 7, "span": [0, 74], "__ref_s3_data": null}], "text": "- The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [149.709, 270.61, 480.596, 279.4069999999999], "page": 7, "span": [0, 75], "__ref_s3_data": null}], "text": "The application of these rules gives OTSL a set of unique properties. First", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.709, 139.10000000000002, 480.592, 147.89699999999993], "page": 7, "span": [0, 75], "__ref_s3_data": null}], "text": "These characteristics can be easily learned by sequence generator networks,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 664.269, 480.589, 673.066], "page": 8, "span": [0, 78], "__ref_s3_data": null}], "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 621.636, 149.402, 630.443], "page": 8, "span": [0, 3], "__ref_s3_data": null}], "text": "4.3", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 600.921, 480.596, 609.718], "page": 8, "span": [0, 79], "__ref_s3_data": null}], "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 460.268, 141.489, 470.836], "page": 8, "span": [0, 1], "__ref_s3_data": null}], "text": "5", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 435.953, 480.594, 444.75], "page": 8, "span": [0, 77], "__ref_s3_data": null}], "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [149.709, 163.01, 480.588, 171.80700000000002], "page": 8, "span": [0, 71], "__ref_s3_data": null}], "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 664.269, 480.594, 673.066], "page": 9, "span": [0, 76], "__ref_s3_data": null}], "text": "order to compute the TED score. Inference timing results for all experiments", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 614.007, 149.402, 622.814], "page": 9, "span": [0, 3], "__ref_s3_data": null}], "text": "5.1", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 597.617, 480.592, 606.414], "page": 9, "span": [0, 73], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.765, 509.002, 160.118, 516.928], "page": 9, "span": [0, 5], "__ref_s3_data": null}], "text": "Table", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [134.765, 275.04099999999994, 149.402, 283.848], "page": 9, "span": [0, 3], "__ref_s3_data": null}], "text": "5.2", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 258.65200000000004, 480.591, 267.44900000000007], "page": 9, "span": [0, 77], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.709, 163.01, 205.597, 171.80700000000002], "page": 9, "span": [0, 13], "__ref_s3_data": null}], "text": "Additionally,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [134.765, 668.238, 173.094, 676.164], "page": 10, "span": [0, 8], "__ref_s3_data": null}], "text": "Table 2.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [134.765, 494.278, 149.402, 503.085], "page": 10, "span": [0, 3], "__ref_s3_data": null}], "text": "5.3", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 473.342, 189.59, 482.139], "page": 10, "span": [0, 13], "__ref_s3_data": null}], "text": "To illustrate", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [134.765, 663.883, 141.489, 674.451], "page": 12, "span": [0, 1], "__ref_s3_data": null}], "text": "6", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 636.339, 480.595, 645.136], "page": 12, "span": [0, 77], "__ref_s3_data": null}], "text": "We demonstrated that representing tables in HTML for the task of table struc-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.709, 575.759, 480.593, 584.556], "page": 12, "span": [0, 76], "__ref_s3_data": null}], "text": "First and 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"0e3c057d1d7e6b359d73d4a44597879b2d421097da9aeb18ea581b32666ce740", "model": "default", "page": 11}, {"hash": "ec343c5522af29f238bde237ca655cdc018c5db20fb099c15ce8bc5045ce8593", "model": "default", "page": 12}, {"hash": "4ffa1d69b1366de506ca77c25a021790c3c150791fc830d6f4c85c3846efe6a9", "model": "default", "page": 13}, {"hash": "9fd62e0449eaf680e49767b4c512d8172cd3586480344318dc7e1cb0964b4d18", "model": "default", "page": 14}]}, "main-text": [{"prov": [{"bbox": [134.765, 645.486, 480.597, 676.101], "page": 1, "span": [0, 60], "__ref_s3_data": null}], "text": "Optimized Table Tokenization for Table Structure Recognition", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [139.343, 591.836, 476.013, 621.841], "page": 1, "span": [0, 208], "__ref_s3_data": null}], "text": "Maksym Lysak [0000 - 0002 - 3723 - 6960] , Ahmed Nassar [0000 - 0002 - 9468 - 0822] , Nikolaos Livathinos [0000 - 0001 - 8513 - 3491] , Christoph Auer [0000 - 0001 - 5761 - 0422] , [0000 - 0002 - 8088 - 0823]", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [229.521, 587.619, 298.609, 596.416], "page": 1, "span": [0, 15], "__ref_s3_data": null}], "text": "and Peter Staar", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [279.105, 565.785, 336.251, 577.074], "page": 1, "span": [0, 12], "__ref_s3_data": null}], "text": "IBM Research", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [222.966, 555.722, 392.39, 563.191], "page": 1, "span": [0, 36], "__ref_s3_data": null}], "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [163.111, 326.324, 452.249, 523.914], "page": 1, "span": [0, 1198], "__ref_s3_data": null}], "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [163.111, 293.273, 452.241, 315.521], "page": 1, "span": [0, 90], "__ref_s3_data": null}], "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 259.312, 228.934, 269.88], "page": 1, "span": [0, 14], "__ref_s3_data": null}], "text": "1 Introduction", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 163.18499999999995, 480.596, 243.71299999999997], "page": 1, "span": [0, 500], "__ref_s3_data": null}], "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 127.14499999999998, 480.596, 159.85199999999998], "page": 1, "span": [0, 235], "__ref_s3_data": null}], "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [134.765, 271.11300000000006, 480.592, 339.686], "page": 2, "span": [0, 435], "__ref_s3_data": null}], "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 127.14499999999998, 480.595, 267.44900000000007], "page": 2, "span": [0, 911], "__ref_s3_data": null}], "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 580.583, 480.592, 673.066], "page": 3, "span": [0, 584], "__ref_s3_data": null}], "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 460.77, 480.596, 577.164], "page": 3, "span": [0, 721], "__ref_s3_data": null}], "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 352.913, 480.596, 457.352], "page": 3, "span": [0, 626], "__ref_s3_data": null}], "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 319.344, 236.769, 329.912], "page": 3, "span": [0, 14], "__ref_s3_data": null}], "text": "2 Related Work", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 127.144, 484.121, 303.314], "page": 3, "span": [0, 1161], "__ref_s3_data": null}], "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 532.762, 480.596, 673.066], "page": 4, "span": [0, 939], "__ref_s3_data": null}], "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 305.353, 480.596, 529.343], "page": 4, "span": [0, 1404], "__ref_s3_data": null}], "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 209.45100000000002, 480.594, 301.934], "page": 4, "span": [0, 572], "__ref_s3_data": null}], "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 175.88200000000006, 269.624, 186.45000000000005], "page": 4, "span": [0, 19], "__ref_s3_data": null}], "text": "3 Problem Statement", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 127.144, 480.594, 159.85199999999998], "page": 4, "span": [0, 233], "__ref_s3_data": null}], "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 604.493, 480.594, 673.066], "page": 5, "span": [0, 422], "__ref_s3_data": null}], "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}, {"prov": [{"bbox": [134.765, 259.57899999999995, 480.595, 423.793], "page": 5, "span": [0, 1021], "__ref_s3_data": null}], "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 211.29399999999998, 480.593, 255.957], "page": 5, "span": [0, 313], "__ref_s3_data": null}], "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 127.14499999999998, 480.595, 207.673], "page": 5, "span": [0, 542], "__ref_s3_data": null}], "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 652.314, 480.595, 673.066], "page": 6, "span": [0, 132], "__ref_s3_data": null}], "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 496.258, 480.596, 648.517], "page": 6, "span": [0, 977], "__ref_s3_data": null}], "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 460.801, 372.508, 471.369], "page": 6, "span": [0, 36], "__ref_s3_data": null}], "text": "4 Optimised Table Structure Language", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 350.4, 480.595, 442.883], "page": 6, "span": [0, 563], "__ref_s3_data": null}], "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 317.321, 261.801, 326.128], "page": 6, "span": [0, 23], "__ref_s3_data": null}], "text": "4.1 Language Definition", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 270.294, 480.589, 303.002], "page": 6, "span": [0, 165], "__ref_s3_data": null}], "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [149.709, 257.701, 409.311, 266.49800000000005], "page": 6, "span": [0, 57], "__ref_s3_data": null}], "text": "The OTSL vocabulary is comprised of the following tokens:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [140.993, 235.22299999999996, 460.544, 244.02999999999997], "page": 6, "span": [0, 72], "__ref_s3_data": null}], "text": "- -\"C\" cell a new table cell that either has or does not have cell content", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.993, 210.67499999999995, 480.594, 231.437], "page": 6, "span": [0, 82], "__ref_s3_data": null}], "text": "- -\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.993, 186.12599999999998, 480.588, 206.88800000000003], "page": 6, "span": [0, 81], "__ref_s3_data": null}], "text": "- -\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.993, 173.53300000000002, 454.555, 182.34000000000003], "page": 6, "span": [0, 71], "__ref_s3_data": null}], "text": "- -\"X\" cell cross cell , to merge with both left and upper neighbor cells", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [140.993, 160.93899999999996, 328.617, 169.74599999999998], "page": 6, "span": [0, 40], "__ref_s3_data": null}], "text": "- -\"NL\" new-line , switch to the next row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.765, 127.14499999999998, 480.593, 147.89699999999993], "page": 6, "span": [0, 99], "__ref_s3_data": null}], "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/2"}, {"prov": [{"bbox": [134.765, 477.897, 246.652, 486.704], "page": 7, "span": [0, 19], "__ref_s3_data": null}], "text": "4.2 Language Syntax", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 457.955, 363.796, 466.752], "page": 7, "span": [0, 51], "__ref_s3_data": null}], "text": "The OTSL representation follows these syntax rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [138.973, 424.066, 480.589, 444.829], "page": 7, "span": [0, 108], "__ref_s3_data": null}], "text": "- 1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 400.153, 480.592, 420.915], "page": 7, "span": [0, 106], "__ref_s3_data": null}], "text": "- 2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 388.195, 226.074, 397.002], "page": 7, "span": [0, 20], "__ref_s3_data": null}], "text": "3. Cross cell rule :", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [151.701, 352.326, 480.592, 385.033], "page": 7, "span": [0, 167], "__ref_s3_data": null}], "text": "- The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 340.367, 474.59, 349.174], "page": 7, "span": [0, 78], "__ref_s3_data": null}], "text": "- 4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 316.454, 480.588, 337.216], "page": 7, "span": [0, 84], "__ref_s3_data": null}], "text": "- 5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [138.973, 292.54, 480.595, 313.303], "page": 7, "span": [0, 144], "__ref_s3_data": null}], "text": "- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.765, 151.058, 480.596, 279.4069999999999], "page": 7, "span": [0, 848], "__ref_s3_data": null}], "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 127.14499999999998, 480.593, 147.89699999999993], "page": 7, "span": [0, 153], "__ref_s3_data": null}], "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 652.314, 480.589, 673.066], "page": 8, "span": [0, 84], "__ref_s3_data": null}], "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 621.636, 319.347, 630.443], "page": 8, "span": [0, 35], "__ref_s3_data": null}], "text": "4.3 Error-detection and -mitigation", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 493.324, 480.596, 609.718], "page": 8, "span": [0, 797], "__ref_s3_data": null}], "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 460.268, 229.035, 470.836], "page": 8, "span": [0, 13], "__ref_s3_data": null}], "text": "5 Experiments", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 340.312, 480.595, 444.75], "page": 8, "span": [0, 684], "__ref_s3_data": null}], "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/3"}, {"prov": [{"bbox": [134.765, 127.14499999999998, 480.592, 171.80700000000002], "page": 8, "span": [0, 299], "__ref_s3_data": null}], "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 640.358, 480.596, 673.066], "page": 9, "span": [0, 163], "__ref_s3_data": null}], "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 614.007, 318.448, 622.814], "page": 9, "span": [0, 32], "__ref_s3_data": null}], "text": "5.1 Hyper Parameter Optimization", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 537.841, 480.593, 606.414], "page": 9, "span": [0, 423], "__ref_s3_data": null}], "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/0"}, {"prov": [{"bbox": [134.765, 464.018, 480.595, 519.143], "page": 9, "span": [0, 398], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [134.765, 275.04099999999994, 264.403, 283.848], "page": 9, "span": [0, 24], "__ref_s3_data": null}], "text": "5.2 Quantitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 174.96500000000003, 480.596, 267.44900000000007], "page": 9, "span": [0, 555], "__ref_s3_data": null}], "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 127.14499999999998, 480.596, 171.80700000000002], "page": 9, "span": [0, 289], "__ref_s3_data": null}], "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [134.765, 645.172, 480.594, 678.379], "page": 10, "span": [0, 192], "__ref_s3_data": null}], "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [134.765, 494.278, 257.087, 503.085], "page": 10, "span": [0, 23], "__ref_s3_data": null}], "text": "5.3 Qualitative Results", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 425.522, 480.59, 482.139], "page": 10, "span": [0, 309], "__ref_s3_data": null}], "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/4"}, {"prov": [{"bbox": [227.915, 116.654, 230.1, 126.17399999999998], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u03bc", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [300.581, 98.57100000000003, 302.726, 108.37800000000004], "page": 10, "span": [0, 1], "__ref_s3_data": null}], "text": "\u2265", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/5"}, {"prov": [{"bbox": [134.765, 663.883, 219.255, 674.451], "page": 12, "span": [0, 12], "__ref_s3_data": null}], "text": "6 Conclusion", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [134.765, 588.518, 480.596, 645.136], "page": 12, "span": [0, 330], "__ref_s3_data": null}], "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 468.163, 480.595, 584.556], "page": 12, "span": [0, 724], "__ref_s3_data": null}], "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 323.897, 480.595, 464.201], "page": 12, "span": [0, 926], "__ref_s3_data": null}], "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [134.765, 287.611, 197.686, 298.179], "page": 12, "span": [0, 10], "__ref_s3_data": null}], "text": "References", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [139.371, 228.12799999999993, 480.592, 271.398], "page": 12, "span": [0, 270], "__ref_s3_data": null}], "text": "- 1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.371, 182.59299999999996, 480.592, 226.75900000000001], "page": 12, "span": [0, 301], "__ref_s3_data": null}], "text": "- 2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Forn\u00e9s, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.371, 159.87099999999998, 480.587, 182.11900000000003], "page": 12, "span": [0, 140], "__ref_s3_data": null}], "text": "- 3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.371, 126.19100000000003, 480.588, 159.39699999999993], "page": 12, "span": [0, 204], "__ref_s3_data": null}], "text": "- 4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.371, 641.397, 480.595, 674.604], "page": 13, "span": [0, 203], "__ref_s3_data": null}], "text": "- 5. Kayal, P., Anand, M., Desai, H., Singh, M.: Tables to latex: structure and content extraction from scientific tables. International Journal on Document Analysis and Recognition (IJDAR) pp. 1-10 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.371, 575.621, 480.59, 597.869], "page": 13, "span": [0, 131], "__ref_s3_data": null}], "text": "- 7. Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: A benchmark dataset for table detection and recognition (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.371, 597.55, 480.593, 641.716], "page": 13, "span": [0, 264], "__ref_s3_data": null}], "text": "- 6. Lee, E., Kwon, J., Yang, H., Park, J., Lee, S., Koo, H.I., Cho, N.I.: Table structure recognition based on grid shape graph. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). pp. 18681873. IEEE (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.371, 521.711, 480.595, 575.939], "page": 13, "span": [0, 345], "__ref_s3_data": null}], "text": "- 8. Livathinos, N., Berrospi, C., Lysak, M., Kuropiatnyk, V., Nassar, A., Carvalho, A., Dolfi, M., Auer, C., Dinkla, K., Staar, P.: Robust pdf document conversion using recurrent neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 15137-15145 (May 2021), https://ojs.aaai.org/index.php/ AAAI/article/view/17777", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [139.371, 487.926, 480.594, 521.133], "page": 13, "span": [0, 234], "__ref_s3_data": null}], "text": "- 9. Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4614-4623 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.764, 423.057, 480.594, 488.245], "page": 13, "span": [0, 413], "__ref_s3_data": null}], "text": "- 10. Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.W.J.: Doclaynet: A large human-annotated dataset for document-layout segmentation. In: Zhang, A., Rangwala, H. (eds.) KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. pp. 3743-3751. ACM (2022). https://doi.org/10.1145/3534678.3539043 , https:// doi.org/10.1145/3534678.3539043", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.764, 378.314, 480.593, 422.48], "page": 13, "span": [0, 295], "__ref_s3_data": null}], "text": "- 11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572-573 (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.764, 291.516, 480.594, 334.786], "page": 13, "span": [0, 275], "__ref_s3_data": null}], "text": "- 13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.764, 334.467, 480.595, 378.633], "page": 13, "span": [0, 281], "__ref_s3_data": null}], "text": "- 12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162-1167. IEEE (2017)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.764, 246.77300000000002, 480.593, 290.939], "page": 13, "span": [0, 241], "__ref_s3_data": null}], "text": "- 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.764, 181.904, 480.596, 247.09199999999998], "page": 13, "span": [0, 405], "__ref_s3_data": null}], "text": "- 15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774-782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.764, 159.07899999999995, 480.595, 181.327], "page": 13, "span": [0, 96], "__ref_s3_data": null}], "text": "- 16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.764, 126.19100000000003, 480.591, 159.39800000000002], "page": 13, "span": [0, 195], "__ref_s3_data": null}], "text": "- 17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.765, 641.397, 480.591, 674.604], "page": 14, "span": [0, 223], "__ref_s3_data": null}], "text": "- 18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.765, 598.457, 480.595, 641.727], "page": 14, "span": [0, 269], "__ref_s3_data": null}], "text": "- 19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https://doi.org/10.48550/ARXIV.2105.01848 , https://arxiv.org/abs/2105.01848", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.765, 575.644, 480.594, 597.891], "page": 14, "span": [0, 147], "__ref_s3_data": null}], "text": "- 20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.765, 521.745, 480.593, 575.974], "page": 14, "span": [0, 329], "__ref_s3_data": null}], "text": "- 21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.765, 477.013, 480.596, 521.179], "page": 14, "span": [0, 259], "__ref_s3_data": null}], "text": "- 22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [134.765, 444.137, 480.595, 477.343], "page": 14, "span": [0, 206], "__ref_s3_data": null}], "text": "- 23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)", "type": "paragraph", "payload": null, "name": "List-item", "font": null}], "figures": [{"prov": [{"bbox": [148.45364379882812, 366.1537780761719, 464.3608093261719, 583.6257629394531], "page": 2, "span": [0, 574], "__ref_s3_data": null}], "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [137.41448974609375, 451.7695007324219, 476.5608215332031, 558.4876861572266], "page": 5, "span": [0, 73], "__ref_s3_data": null}], "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [164.65028381347656, 511.6590576171875, 449.5505676269531, 628.2029113769531], "page": 7, "span": [0, 207], "__ref_s3_data": null}], "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [140.70968627929688, 198.32281494140625, 472.73382568359375, 283.9361572265625], "page": 8, "span": [0, 104], "__ref_s3_data": null}], "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [162.67430114746094, 128.78643798828125, 451.70062255859375, 347.37744140625], "page": 10, "span": [0, 270], "__ref_s3_data": null}], "text": "Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). \"PMC2807444_006_00.png\" PubTabNet. \u03bc", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [168.39285278320312, 157.99432373046875, 447.35137939453125, 610.0334930419922], "page": 11, "span": [0, 390], "__ref_s3_data": null}], "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [139.66845703125, 322.5278625488281, 475.00372314453125, 454.4252014160156], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "type": "table", "payload": null, "#-cols": 8, "#-rows": 6, "data": [[{"bbox": [144.592, 339.457, 183.828, 363.697], "spans": [[0, 0], [1, 0]], "text": "# enc-layers", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [192.195, 339.457, 231.431, 363.697], "spans": [[0, 1], [1, 1]], "text": "# dec-layers", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": [239.798, 344.936, 278.318, 356.225], "spans": [[0, 2], [1, 2]], "text": "Language", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 2]}, {"bbox": 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"col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [245.176, 444.662, 272.94, 468.903], "spans": [[5, 2]], "text": "OTSL HTML", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [289.017, 444.662, 310.004, 468.903], "spans": [[5, 3]], "text": "0.952 0.944", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [326.717, 444.662, 347.704, 468.903], "spans": [[5, 4]], "text": "0.92 0.903", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [362.088, 446.877, 386.249, 468.903], "spans": [[5, 5]], "text": "0.942 0.931", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [394.618, 446.877, 418.779, 468.903], "spans": [[5, 6]], "text": 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"page": 12, "width": 612.0}, {"height": 792.0, "page": 13, "width": 612.0}, {"height": 792.0, "page": 14, "width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/2305.03393v1.md b/tests/data/groundtruth/docling_v1/2305.03393v1.md index 60759314..b3e5f45d 100644 --- a/tests/data/groundtruth/docling_v1/2305.03393v1.md +++ b/tests/data/groundtruth/docling_v1/2305.03393v1.md @@ -1,6 +1,6 @@ -## Optimized Table Tokenization for Table Structure +## Optimized Table Tokenization for Table Structure Recognition -Maksym Lysak +Maksym Lysak [0000 - 0002 - 3723 - 6960] , Ahmed Nassar [0000 - 0002 - 9468 - 0822] , Nikolaos Livathinos [0000 - 0001 - 8513 - 3491] , Christoph Auer [0000 - 0001 - 5761 - 0422] , [0000 - 0002 - 8088 - 0823] and Peter Staar @@ -8,194 +8,217 @@ IBM Research {mly,ahn,nli,cau,taa}@zurich.ibm.com -Abstract. +Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community. -Keywords: +Keywords: Table Structure Recognition · Data Representation · Transformers · Optimization. -## 1 +## 1 Introduction -Tables are ubiquitous in documents such as scientific papers, patents, reports, +Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods. -In modern document understanding systems [1,15], table extraction is typi- +In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of -Fig. 1. +Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL). -today, +today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]. -Recently +Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR. -While the majority of research in TSR is currently focused on the develop- +While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments. -The main contribution of this paper is the introduction of a new optimised ta- +The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML. -The paper is structured as follows. In section 2, we give an overview of the +The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps. -## 2 +## 2 Related Work -Approaches to formalize the logical structure and layout of tables in electronic +Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell. -Other work [20] aims at predicting a grid for each table and deciding which cells +Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence. -Within the +Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. -Im2Seq approaches have shown to be well-suited for the TSR task and allow a +Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task. -## 3 +## 3 Problem Statement -All known Im2Seq based models for TSR fundamentally work in similar ways. +All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab- -ulary and can be interpreted as a table structure. For example, with the HTML +ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary. -Fig. 2. +Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet. -Obviously, HTML and other general-purpose markup languages were not de- +Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. -Additionally, it would be desirable if the representation would easily allow +Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible. -In a valid HTML table, the token sequence must describe a 2D grid of table +In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence -generation. Implicitly, this also means that Im2Seq models need to learn these +generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output. -In practice, we observe two major issues with prediction quality when train- +In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content. -## 4 +## 4 Optimised Table Structure Language -To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, +To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture. -## 4.1 +## 4.1 Language Definition -In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines +In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid. The OTSL vocabulary is comprised of the following tokens: -- - +- -"C" cell a new table cell that either has or does not have cell content -A notable attribute of OTSL is that it has the capability of achieving lossless +- -"L" cell left-looking cell , merging with the left neighbor cell to create a span -Fig. 3. +- -"U" cell up-looking cell , merging with the upper neighbor cell to create a span + +- -"X" cell cross cell , to merge with both left and upper neighbor cells + +- -"NL" new-line , switch to the next row. + +A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML. + +Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding -## 4.2 +## 4.2 Language Syntax The OTSL representation follows these syntax rules: -- 1. +- 1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell. -- 2. +- 2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell. -## 3. +## 3. Cross cell rule : -- 4. +- The left neighbour of an "X" cell must be either another "X" cell or a "U" cell, and the upper neighbour of an "X" cell must be either another "X" cell or an "L" cell. -- 5. +- 4. First row rule : Only "L" cells and "C" cells are allowed in the first row. -- 6. +- 5. First column rule : Only "U" cells and "C" cells are allowed in the first column. -- The left neighbour of an "X" cell must be either another "X" cell or a "U" +- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token. -The application of these rules gives OTSL a set of unique properties. First +The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a "C"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid. -These characteristics can be easily learned by sequence generator networks, +These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern -reduces significantly the column drift seen in the HTML based models (see Fig- +reduces significantly the column drift seen in the HTML based models (see Figure 5). -## 4.3 +## 4.3 Error-detection and -mitigation -The design of OTSL allows to validate a table structure easily on an unfinished +The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied. -## 5 +## 5 Experiments -To evaluate the impact of OTSL on prediction accuracy and inference times, we +To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available. -Fig. 4. +Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach. -We rely on standard metrics such as Tree Edit Distance score (TEDs) for +We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in -order to compute the TED score. Inference timing results for all experiments +order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. -## 5.1 +## 5.1 Hyper Parameter Optimization -We have chosen the PubTabNet data set to perform HPO, since it includes a +We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. -Table +Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP | Inference | +|----------------|----------------|------------|-------------|-------------|-------------|-------------|-------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | (0.75) | time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | +## 5.2 Quantitative Results -## 5.2 +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables. -We picked the model parameter configuration that produced the best prediction +Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. -Additionally, +Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). -Table 2. +| Data set | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) | +|--------------|------------|-------------|-------------|-------------|-------------|-------------------------| +| Data set | Language | simple | complex | all | mAP(0.75) | Inference time (secs) | +| PubTabNet | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| FinTabNet | OTSL HTML | 0.955 0.917 | 0.961 0.922 | 0.959 0.92 | 0.862 0.722 | 1.85 3.26 | +| PubTables-1M | OTSL HTML | 0.987 0.983 | 0.964 0.944 | 0.977 0.966 | 0.896 0.889 | 1.79 3.26 | +## 5.3 Qualitative Results +To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes. -## 5.3 - -To illustrate - -Fig. 5. +Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. μ -Fig. 6. +μ + +≥ + +Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406_003_01.png" PubTabNet. -## 6 +## 6 Conclusion -We demonstrated that representing tables in HTML for the task of table struc- +We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits. -First and foremost, given the same network configuration, inference time for +First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1). -Secondly, OTSL has more inherent structure and a significantly restricted vo- +Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. 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Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. The benefits of OTSL are that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 399.923, "r_x1": 169.253, "r_y1": 399.923, "r_x2": 169.253, "r_y2": 388.634, "r_x3": 163.111, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.795, "r_y0": 399.923, "r_x1": 452.244, "r_y1": 399.923, "r_x2": 452.244, "r_y2": 388.634, "r_x3": 172.795, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "orig": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 410.882, "r_x1": 452.249, "r_y1": 410.882, "r_x2": 452.249, "r_y2": 399.593, "r_x3": 163.111, "r_y3": 399.593, "coord_origin": "TOPLEFT"}, "text": "the sequence length to half of HTML on average. Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 297.115, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "inference", "orig": "inference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.158, "r_y0": 421.841, "r_x1": 365.708, "r_y1": 421.841, "r_x2": 365.708, "r_y2": 410.552, "r_x3": 337.158, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "time is", "orig": "time is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.146, "r_y0": 421.841, "r_x1": 396.249, "r_y1": 421.841, "r_x2": 396.249, "r_y2": 410.552, "r_x3": 370.146, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "halved", "orig": "halved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.696, "r_y0": 421.841, "r_x1": 452.241, "r_y1": 421.841, "r_x2": 452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.282, "r_y0": 443.758, "r_x1": 234.527, "r_y1": 443.758, "r_x2": 234.527, "r_y2": 432.469, "r_x3": 204.282, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "correct.", "orig": "correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.588, "r_y0": 443.758, "r_x1": 452.244, "r_y1": 443.758, "r_x2": 452.244, "r_y2": 432.469, "r_x3": 238.588, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "This in turn eliminates most post-processing needs.", "orig": "This in turn eliminates most post-processing needs.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 454.717, "r_x1": 452.246, "r_y1": 454.717, "r_x2": 452.246, "r_y2": 443.428, "r_x3": 163.111, "r_y3": 443.428, "coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 134.765, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 532.688, "r_x1": 228.934, "r_y1": 532.688, "r_x2": 228.934, "r_y2": 522.12, "r_x3": 154.938, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "Introduction", "orig": "Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.0840000000001, "r_x1": 480.594, "r_y1": 557.0840000000001, "r_x2": 480.594, "r_y2": 548.287, "r_x3": 134.765, "r_y3": 548.287, "coord_origin": "TOPLEFT"}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.04, "r_x1": 480.592, "r_y1": 569.04, "r_x2": 480.592, "r_y2": 560.2429999999999, "r_x3": 134.765, "r_y3": 560.2429999999999, "coord_origin": "TOPLEFT"}, "text": "manuals, specification sheets or marketing material. They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. Therefore, accurate table extraction is achieved these days with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.815, "r_x1": 279.327, "r_y1": 628.815, "r_x2": 279.327, "r_y2": 620.018, "r_x3": 134.765, "r_y3": 620.018, "coord_origin": "TOPLEFT"}, "text": "machine-learning based methods.", "orig": "machine-learning based methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.708, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 149.708, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "In modern document understanding systems [1,15], table extraction is typi-", "orig": "In modern document understanding systems [1,15], table extraction is typi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.596, "r_y1": 652.9, "r_x2": 480.596, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "cally a two-step process. 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0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.0840000000001, "r_x1": 480.594, "r_y1": 557.0840000000001, "r_x2": 480.594, "r_y2": 548.287, "r_x3": 134.765, "r_y3": 548.287, "coord_origin": "TOPLEFT"}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.708, "t": 632.148, "r": 480.589, "b": 640.9449999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.708, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 149.708, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "In modern document understanding systems [1,15], table extraction is typi-", "orig": "In modern document understanding systems [1,15], table extraction is typi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typi-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.34, "t": 165.0, "r": 36.34, "b": 555.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8899643421173096, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. 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This example demonstrates many of", "orig": "HTML representation, (D) OTSL representation. 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On the other hand, the problem of table", "orig": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.861, "r_y0": 163.59699999999998, "r_x1": 194.555, "r_y1": 163.59699999999998, "r_x2": 194.555, "r_y2": 154.79999999999995, "r_x3": 173.861, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "as is", "orig": "as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.61, "r_y0": 163.59699999999998, "r_x1": 211.535, "r_y1": 163.59699999999998, "r_x2": 211.535, "r_y2": 154.79999999999995, "r_x3": 199.61, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 215.769, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. In the recent past, a wide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.393, "r_x1": 164.401, "r_y1": 521.393, "r_x2": 164.401, "r_y2": 512.596, "r_x3": 134.765, "r_y3": 512.596, "coord_origin": "TOPLEFT"}, "text": "variety", "orig": "variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.581, "r_y0": 521.393, "r_x1": 177.605, "r_y1": 521.393, "r_x2": 177.605, "r_y2": 512.596, "r_x3": 169.581, "r_y3": 512.596, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.786, "r_y0": 521.393, "r_x1": 223.77, "r_y1": 521.393, "r_x2": 223.77, "r_y2": 512.596, "r_x3": 182.786, "r_y3": 512.596, "coord_origin": 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Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. 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Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. The graph nodes represent the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "orig": "content of each table cell, an embedding vector from the table image, or geomet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.589, "r_y1": 652.9, "r_x2": 480.589, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "orig": "ric coordinates of the table cell. The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 118.93399999999997, "r": 480.592, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 214.836, "r": 480.594, "b": 223.63300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 149.709, "t": 334.648, "r": 480.588, "b": 343.445, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. 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2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 118.93399999999997, "r": 480.592, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While the majority of research in TSR is currently focused on the develop-"}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 214.836, "r": 480.594, "b": 223.63300000000004, "coord_origin": "TOPLEFT"}, 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In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the"}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 141.489, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 480.591, "b": 497.483, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic"}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 118.93399999999997, "r": 480.592, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While the majority of research in TSR is currently focused on the develop-"}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 214.836, "r": 480.594, "b": 223.63300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised ta-"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 149.709, "t": 334.648, "r": 480.588, "b": 343.445, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the"}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 141.489, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 480.591, "b": 497.483, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic"}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. For each decoded table cell (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.299, "r_y0": 319.275, "r_x1": 356.971, "r_y1": 319.275, "r_x2": 356.971, "r_y2": 310.478, "r_x3": 333.299, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.085, "r_y0": 319.275, "r_x1": 363.725, "r_y1": 319.275, "r_x2": 363.725, "r_y2": 310.478, "r_x3": 357.085, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "),", "orig": "),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.64, "r_y0": 319.275, "r_x1": 480.594, "r_y1": 319.275, "r_x2": 480.594, "r_y2": 310.478, "r_x3": 367.64, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "the attention is passed to", "orig": "the attention is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 148.598, "r_y1": 331.23, "r_x2": 148.598, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.277, "r_y0": 331.23, "r_x1": 166.531, "r_y1": 331.23, "r_x2": 166.531, "r_y2": 322.433, "r_x3": 152.277, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.427, "r_y0": 331.23, "r_x1": 202.111, "r_y1": 331.23, "r_x2": 202.111, "r_y2": 322.433, "r_x3": 170.427, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "decoder", "orig": "decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.864, "r_y0": 331.23, "r_x1": 480.587, "r_y1": 331.23, "r_x2": 480.587, "r_y2": 322.433, "r_x3": 206.864, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to predict the content with an embedded OCR approach. The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.7280000000001, "r_x1": 176.662, "r_y1": 534.7280000000001, "r_x2": 176.662, "r_y2": 525.931, "r_x3": 134.765, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "to deliver", "orig": "to deliver", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.896, "r_y0": 534.7280000000001, "r_x1": 246.551, "r_y1": 534.7280000000001, "r_x2": 246.551, "r_y2": 525.931, "r_x3": 180.896, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.785, "r_y0": 534.7280000000001, "r_x1": 480.59, "r_y1": 534.7280000000001, "r_x2": 480.59, "r_y2": 525.931, "r_x3": 250.785, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "prediction accuracy [9]. This motivated the authors", "orig": "prediction accuracy [9]. 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"coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, 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These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 149.709, "t": 262.65700000000004, "r": 199.213, "b": 271.45399999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 149.709, "t": 490.066, "r": 480.594, "b": 498.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 605.55, "r": 141.489, "b": 616.1179999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.591, "b": 640.9449999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.591, "r_y1": 640.9449999999999, "r_x2": 480.591, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.586, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "orig": "ulary and can be interpreted as a table structure. For example, with the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 221.07899999999995, "r": 173.486, "b": 229.005, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 368.207, "r": 480.593, "b": 377.004, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, 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For example, with the HTML", "orig": "ulary and can be interpreted as a table structure. For example, with the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML"}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 221.07899999999995, "r": 173.486, "b": 229.005, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 2."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 368.207, "r": 480.593, "b": 377.004, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not de-"}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 149.709, "t": 536.043, "r": 480.593, "b": 544.84, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow"}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 584.327, "r": 480.593, "b": 593.124, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.586, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "orig": "ulary and can be interpreted as a table structure. For example, with the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML"}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 221.07899999999995, "r": 173.486, "b": 229.005, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 2."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 368.207, "r": 480.593, "b": 377.004, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, 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Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 556.777, "r_x1": 193.206, "r_y1": 556.777, "r_x2": 193.206, "r_y2": 547.98, "r_x3": 151.701, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "\"C\" cell -", "orig": "\"C\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.522, "r_y0": 556.777, "r_x1": 263.466, "r_y1": 556.777, "r_x2": 263.466, "r_y2": 547.98, "r_x3": 196.522, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "a new table cell", "orig": "a new table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.815, "r_y0": 556.777, "r_x1": 460.544, "r_y1": 556.777, "r_x2": 460.544, "r_y2": 547.98, "r_x3": 267.815, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "that either has or does not have cell content", "orig": "that either has or does not have cell content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 569.37, "r_x1": 146.72, "r_y1": 569.37, "r_x2": 146.72, "r_y2": 560.563, "r_x3": 140.993, "r_y3": 560.563, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 569.37, "r_x1": 194.3, "r_y1": 569.37, "r_x2": 194.3, "r_y2": 560.573, "r_x3": 151.701, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": "\"L\" cell -", "orig": "\"L\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 198.659, "r_y0": 569.37, "r_x1": 245.75, "r_y1": 569.37, "r_x2": 245.75, "r_y2": 560.573, "r_x3": 198.659, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": "left-looking", "orig": "left-looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.263, "r_y0": 569.37, "r_x1": 264.518, "r_y1": 569.37, "r_x2": 264.518, "r_y2": 560.573, "r_x3": 250.263, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.518, "r_y0": 569.37, "r_x1": 267.285, "r_y1": 569.37, "r_x2": 267.285, "r_y2": 560.573, "r_x3": 264.518, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 271.638, "r_y0": 569.37, "r_x1": 480.594, "r_y1": 569.37, "r_x2": 480.594, "r_y2": 560.573, "r_x3": 271.638, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": "merging with the left neighbor cell to create a", "orig": "merging with the left neighbor cell to create a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 581.325, "r_x1": 171.676, "r_y1": 581.325, "r_x2": 171.676, "r_y2": 572.528, "r_x3": 151.701, "r_y3": 572.528, "coord_origin": "TOPLEFT"}, "text": "span", "orig": "span", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 593.919, "r_x1": 146.72, "r_y1": 593.919, "r_x2": 146.72, "r_y2": 585.112, "r_x3": 140.993, "r_y3": 585.112, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 593.919, "r_x1": 194.111, "r_y1": 593.919, "r_x2": 194.111, "r_y2": 585.1220000000001, "r_x3": 151.701, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": "\"U\" cell -", "orig": "\"U\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.748, "r_y0": 593.919, "r_x1": 259.895, "r_y1": 593.919, "r_x2": 259.895, "r_y2": 585.1220000000001, "r_x3": 197.748, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": "up-looking cell", "orig": "up-looking cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 259.892, "r_y0": 593.919, "r_x1": 262.659, "r_y1": 593.919, "r_x2": 262.659, "r_y2": 585.1220000000001, "r_x3": 259.892, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.295, "r_y0": 593.919, "r_x1": 480.588, "r_y1": 593.919, "r_x2": 480.588, "r_y2": 585.1220000000001, "r_x3": 266.295, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": "merging with the upper neighbor cell to create a", "orig": "merging with the upper neighbor cell to create a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 605.874, "r_x1": 171.676, "r_y1": 605.874, "r_x2": 171.676, "r_y2": 597.077, "r_x3": 151.701, "r_y3": 597.077, "coord_origin": "TOPLEFT"}, "text": "span", "orig": "span", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 618.467, "r_x1": 193.483, "r_y1": 618.467, "r_x2": 193.483, "r_y2": 609.67, "r_x3": 151.701, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "\"X\" cell -", "orig": "\"X\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.799, "r_y0": 618.467, "r_x1": 236.12, "r_y1": 618.467, "r_x2": 236.12, "r_y2": 609.67, "r_x3": 196.799, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "cross cell", "orig": "cross cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 236.125, "r_y0": 618.467, "r_x1": 238.892, "r_y1": 618.467, "r_x2": 238.892, "r_y2": 609.67, "r_x3": 236.125, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 242.209, "r_y0": 618.467, "r_x1": 454.555, "r_y1": 618.467, "r_x2": 454.555, "r_y2": 609.67, "r_x3": 242.209, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "to merge with both left and upper neighbor cells", "orig": "to merge with both left and upper neighbor cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 631.061, "r_x1": 181.994, "r_y1": 631.061, "r_x2": 181.994, "r_y2": 622.264, "r_x3": 151.701, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "\"NL\" -", "orig": "\"NL\" -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.317, "r_y0": 631.061, "r_x1": 221.462, "r_y1": 631.061, "r_x2": 221.462, "r_y2": 622.264, "r_x3": 185.317, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "new-line", "orig": "new-line", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.461, "r_y0": 631.061, "r_x1": 224.228, "r_y1": 631.061, "r_x2": 224.228, "r_y2": 622.264, "r_x3": 221.461, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.545, "r_y0": 631.061, "r_x1": 328.617, "r_y1": 631.061, "r_x2": 328.617, "r_y2": 622.264, "r_x3": 227.545, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "switch to the next row.", "orig": "switch to the next row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 228.223, "r_y1": 664.855, "r_x2": 228.223, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "conversion to HTML.", "orig": "conversion to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 143.48299999999995, "r": 480.59, "b": 152.27999999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 141.489, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.591, "b": 357.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 149.402, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 497.795, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 146.72, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.993, "t": 560.563, "r": 146.72, "b": 569.37, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 569.37, "r_x1": 146.72, "r_y1": 569.37, "r_x2": 146.72, "r_y2": 560.563, "r_x3": 140.993, "r_y3": 560.563, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.993, "t": 585.112, "r": 146.72, "b": 593.919, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 593.919, "r_x1": 146.72, "r_y1": 593.919, "r_x2": 146.72, "r_y2": 585.112, "r_x3": 140.993, "r_y3": 585.112, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.993, "t": 609.66, "r": 146.72, "b": 618.467, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 146.72, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.593, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 143.48299999999995, "r": 480.59, "b": 152.27999999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when train-"}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 141.489, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.591, "b": 357.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,"}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 149.402, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 497.795, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines"}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 146.72, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.993, "t": 560.563, "r": 146.72, "b": 569.37, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 569.37, "r_x1": 146.72, "r_y1": 569.37, "r_x2": 146.72, "r_y2": 560.563, "r_x3": 140.993, "r_y3": 560.563, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.993, "t": 585.112, "r": 146.72, "b": 593.919, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 593.919, "r_x1": 146.72, "r_y1": 593.919, "r_x2": 146.72, "r_y2": 585.112, "r_x3": 140.993, "r_y3": 585.112, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.993, "t": 609.66, "r": 146.72, "b": 618.467, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 146.72, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.593, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless"}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 143.48299999999995, "r": 480.59, "b": 152.27999999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when train-"}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 141.489, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.591, "b": 357.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,"}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 149.402, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 497.795, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines"}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 146.72, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, 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"cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.993, "t": 585.112, "r": 146.72, "b": 593.919, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 593.919, "r_x1": 146.72, "r_y1": 593.919, "r_x2": 146.72, "r_y2": 585.112, "r_x3": 140.993, "r_y3": 585.112, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.993, "t": 609.66, "r": 146.72, "b": 618.467, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 146.72, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.593, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless"}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.383, "r_y0": 355.978, "r_x1": 260.15, "r_y1": 355.978, "r_x2": 260.15, "r_y2": 347.181, "r_x3": 257.383, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.184, "r_y0": 355.978, "r_x1": 480.589, "r_y1": 355.978, "r_x2": 480.589, "r_y2": 347.181, "r_x3": 264.184, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"L\" cell must be either", "orig": "The left neighbour of an \"L\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 367.934, "r_x1": 283.594, "r_y1": 367.934, "r_x2": 283.594, 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\"U\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 403.805, "r_x1": 146.72, "r_y1": 403.805, "r_x2": 146.72, "r_y2": 395.008, "r_x3": 138.973, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 403.805, "r_x1": 223.304, "r_y1": 403.805, "r_x2": 223.304, "r_y2": 394.998, "r_x3": 151.701, "r_y3": 394.998, "coord_origin": "TOPLEFT"}, "text": "Cross cell rule", "orig": "Cross cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 223.307, "r_y0": 403.805, "r_x1": 226.074, "r_y1": 403.805, "r_x2": 226.074, "r_y2": 395.008, "r_x3": 223.307, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 415.764, "r_x1": 480.592, "r_y1": 415.764, "r_x2": 480.592, "r_y2": 406.967, "r_x3": 151.701, "r_y3": 406.967, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 427.719, "r_x1": 480.592, "r_y1": 427.719, "r_x2": 480.592, "r_y2": 418.922, "r_x3": 151.701, "r_y3": 418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, "r_y2": 442.836, "r_x3": 138.973, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 451.633, "r_x1": 221.323, "r_y1": 451.633, "r_x2": 221.323, "r_y2": 442.826, "r_x3": 151.701, "r_y3": 442.826, "coord_origin": "TOPLEFT"}, "text": "First row rule", "orig": "First row rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.327, "r_y0": 451.633, "r_x1": 224.094, "r_y1": 451.633, "r_x2": 224.094, "r_y2": 442.836, "r_x3": 221.327, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.411, "r_y0": 451.633, "r_x1": 474.59, "r_y1": 451.633, "r_x2": 474.59, "r_y2": 442.836, "r_x3": 227.411, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "orig": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 463.591, "r_x1": 146.72, "r_y1": 463.591, "r_x2": 146.72, "r_y2": 454.794, "r_x3": 138.973, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 463.591, "r_x1": 240.72, "r_y1": 463.591, "r_x2": 240.72, "r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79899999999998, "r": 162.644, "b": 133.72500000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", 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First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.592, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks,"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79899999999998, "r": 162.644, "b": 133.72500000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 3."}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65028381347656, "t": 163.79708862304688, "r": 449.5505676269531, "b": 280.3409423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7868534922599792, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.511, "r_y0": 279.0, "r_x1": 426.599, "r_y1": 279.0, "r_x2": 426.599, "r_y2": 272.88800000000003, "r_x3": 334.511, "r_y3": 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First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.592, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks,"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, "r_y0": 403.867, "r_x1": 480.595, "r_y1": 403.867, "r_x2": 480.595, "r_y2": 395.07, "r_x3": 195.902, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. Secondly we pick the best hyper-parameters", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.822, "r_x1": 172.59, "r_y1": 415.822, "r_x2": 172.59, "r_y2": 407.025, "r_x3": 134.765, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "found in", "orig": "found in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 177.492, "r_y0": 415.822, "r_x1": 191.325, "r_y1": 415.822, "r_x2": 191.325, "r_y2": 407.025, "r_x3": 177.492, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.226, "r_y0": 415.822, "r_x1": 213.463, "r_y1": 415.822, "r_x2": 213.463, "r_y2": 407.025, "r_x3": 196.226, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "first", "orig": "first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 218.364, "r_y0": 415.822, "r_x1": 236.125, "r_y1": 415.822, "r_x2": 236.125, "r_y2": 407.025, "r_x3": 218.364, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "step", "orig": "step", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.027, "r_y0": 415.822, "r_x1": 297.661, "r_y1": 415.822, "r_x2": 297.661, "r_y2": 407.025, "r_x3": 241.027, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "and evaluate", "orig": "and evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 302.563, "r_y0": 415.822, "r_x1": 480.593, "r_y1": 415.822, "r_x2": 480.593, "r_y2": 407.025, "r_x3": 302.563, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "how OTSL impacts the performance of", "orig": "how OTSL impacts the performance of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.778, "r_x1": 377.321, "r_y1": 427.778, "r_x2": 377.321, "r_y2": 418.981, "r_x3": 134.765, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "TableFormer after training on other publicly available", "orig": "TableFormer after training on other publicly available", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.874, "r_y0": 427.778, "r_x1": 421.951, "r_y1": 427.778, "r_x2": 421.951, "r_y2": 418.981, "r_x3": 381.874, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "data sets", "orig": "data sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.504, "r_y0": 427.778, "r_x1": 480.595, "r_y1": 427.778, "r_x2": 480.595, "r_y2": 418.981, "r_x3": 426.504, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "(FinTabNet,", "orig": "(FinTabNet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.733, "r_x1": 480.592, "r_y1": 439.733, "r_x2": 480.592, "r_y2": 430.936, "r_x3": 134.765, "r_y3": 430.936, "coord_origin": "TOPLEFT"}, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 451.688, "r_x1": 479.303, "r_y1": 451.688, "r_x2": 479.303, "r_y2": 442.891, "r_x3": 134.765, "r_y3": 442.891, "coord_origin": "TOPLEFT"}, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "orig": "verted into OTSL format for this purpose, and will be made publicly available.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 484.648, "coord_origin": "TOPLEFT"}, "text": "Fig. 4.", "orig": "Fig. 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.196, "r_y0": 493.722, "r_x1": 480.591, "r_y1": 493.722, "r_x2": 480.591, "r_y2": 482.433, "r_x3": 165.196, "r_y3": 482.433, "coord_origin": "TOPLEFT"}, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "orig": "Architecture sketch of the TableFormer model, which is a representative for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.681, "r_x1": 206.702, "r_y1": 504.681, "r_x2": 206.702, "r_y2": 493.392, "r_x3": 134.765, "r_y3": 493.392, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approach.", "orig": "Im2Seq approach.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.3, "r_y0": 543.091, "r_x1": 149.407, "r_y1": 543.091, "r_x2": 149.407, "r_y2": 540.762, "r_x3": 147.3, "r_y3": 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unfinished"}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 141.489, "b": 331.732, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.25, "r": 480.594, "b": 356.047, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we"}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 484.648, "r": 162.644, "b": 492.574, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 484.648, "coord_origin": "TOPLEFT"}, "text": "Fig. 4.", "orig": "Fig. 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 4."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 149.709, "t": 620.193, "r": 480.588, "b": 628.99, "coord_origin": "TOPLEFT"}, "confidence": 0.951270341873169, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 480.588, "r_y1": 628.99, "r_x2": 480.588, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for"}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.70968627929688, "t": 508.0638427734375, "r": 472.73382568359375, "b": 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91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8631826043128967, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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"r_x1": 264.403, "r_y1": 516.9590000000001, "r_x2": 264.403, "r_y2": 508.152, "r_x3": 160.859, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.348, "r_x1": 480.591, "r_y1": 533.348, "r_x2": 480.591, "r_y2": 524.5509999999999, "r_x3": 134.765, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 164.921, "r_y1": 545.304, "r_x2": 164.921, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378753662109, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8857625722885132, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 149.402, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.592, "b": 194.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 275.072, "r": 160.118, "b": 282.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 282.998, "r_x1": 160.118, "r_y1": 282.998, "r_x2": 160.118, "r_y2": 275.072, "r_x3": 134.765, "r_y3": 275.072, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.152, "r": 149.402, "b": 516.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 516.9590000000001, "r_x1": 149.402, "r_y1": 516.9590000000001, "r_x2": 149.402, "r_y2": 508.152, "r_x3": 134.765, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.591, "b": 533.348, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.348, "r_x1": 480.591, "r_y1": 533.348, "r_x2": 480.591, "r_y2": 524.5509999999999, "r_x3": 134.765, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 620.193, "r": 205.597, "b": 628.99, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"0": 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{"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments"}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 149.402, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.592, "b": 194.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a"}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 275.072, "r": 160.118, "b": 282.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 282.998, "r_x1": 160.118, "r_y1": 282.998, "r_x2": 160.118, "r_y2": 275.072, "r_x3": 134.765, "r_y3": 275.072, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table"}, {"label": "section_header", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.152, "r": 149.402, "b": 516.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 516.9590000000001, "r_x1": 149.402, "r_y1": 516.9590000000001, "r_x2": 149.402, "r_y2": 508.152, "r_x3": 134.765, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.591, "b": 533.348, "coord_origin": "TOPLEFT"}, "confidence": 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115.83600000000001, "r_x3": 134.765, "r_y3": 115.83600000000001, "coord_origin": "TOPLEFT"}, "text": "Table 2.", "orig": "Table 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2."}, {"label": "section_header", "id": 3, "page_no": 9, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 288.915, "r": 149.402, "b": 297.722, "coord_origin": "TOPLEFT"}, "confidence": 0.9546447992324829, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 297.722, "r_x1": 149.402, "r_y1": 297.722, "r_x2": 149.402, "r_y2": 288.915, "r_x3": 134.765, "r_y3": 288.915, "coord_origin": "TOPLEFT"}, "text": "5.3", "orig": "5.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.3"}, {"label": "text", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 309.861, "r": 189.59, "b": 318.658, "coord_origin": "TOPLEFT"}, "confidence": 0.9834085702896118, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 318.658, "r_x1": 189.59, "r_y1": 318.658, "r_x2": 189.59, "r_y2": 309.861, "r_x3": 134.765, "r_y3": 309.861, "coord_origin": "TOPLEFT"}, "text": "To illustrate", "orig": "To illustrate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To illustrate"}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59, "r": 162.644, "b": 405.516, "coord_origin": "TOPLEFT"}, "confidence": 0.9482728838920593, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.516, "r_x1": 162.644, "r_y1": 405.516, "r_x2": 162.644, "r_y2": 397.59, "r_x3": 134.765, "r_y3": 397.59, "coord_origin": "TOPLEFT"}, "text": "Fig. 5.", "orig": "Fig. 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 5."}, {"label": "table", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "table", "bbox": {"l": 143.6376495361328, "t": 156.3477020263672, "r": 470.8485412597656, "b": 263.2624816894531, "coord_origin": "TOPLEFT"}, "confidence": 0.9884640574455261, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "lcel", "lcel", "ched", "ched", "nl", "ucel", "ucel", "ched", "ched", "ched", "ucel", "ucel", "nl", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.67430114746094, "t": 444.62255859375, "r": 451.70062255859375, "b": 663.2135620117188, "coord_origin": "TOPLEFT"}, "confidence": 0.9742383360862732, 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.376, "r_y0": 102.78200000000004, "r_x1": 480.59, "r_y1": 102.78200000000004, "r_x2": 480.59, "r_y2": 91.49300000000005, "r_x3": 471.376, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 6.", "orig": "Fig. 6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.215, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 165.215, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "Visualization of predicted structure and detected bounding boxes on a complex", "orig": "Visualization of predicted structure and detected bounding boxes on a complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 480.588, "r_y1": 145.832, "r_x2": 480.588, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table with many rows. 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.821, "r_y0": 102.78200000000004, "r_x1": 231.72, "r_y1": 102.78200000000004, "r_x2": 231.72, "r_y2": 91.49300000000005, "r_x3": 167.821, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.11699999999996, "r_x1": 141.489, "r_y1": 128.11699999999996, "r_x2": 141.489, "r_y2": 117.54899999999998, "r_x3": 134.765, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 128.11699999999996, "r_x1": 219.255, "r_y1": 128.11699999999996, "r_x2": 219.255, "r_y2": 117.54899999999998, "r_x3": 154.938, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "Conclusion", "orig": "Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 155.66099999999994, "r_x1": 480.595, "r_y1": 155.66099999999994, "r_x2": 480.595, "r_y2": 146.86400000000003, "r_x3": 134.765, "r_y3": 146.86400000000003, "coord_origin": "TOPLEFT"}, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.61599999999999, "r_x1": 152.499, "r_y1": 167.61599999999999, "r_x2": 152.499, "r_y2": 158.81899999999996, "r_x3": 134.765, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "ture", "orig": "ture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.963, "r_y0": 167.61599999999999, "r_x1": 205.131, "r_y1": 167.61599999999999, "r_x2": 205.131, "r_y2": 158.81899999999996, "r_x3": 156.963, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "recognition", "orig": "recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 209.594, "r_y0": 167.61599999999999, "r_x1": 480.595, "r_y1": 167.61599999999999, "r_x2": 480.595, "r_y2": 158.81899999999996, "r_x3": 209.594, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "with Im2Seq models is ill-suited and has serious limitations.", "orig": "with Im2Seq models is ill-suited and has serious limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly used general purpose languages,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.48199999999997, "r_x1": 239.539, "r_y1": 203.48199999999997, "r_x2": 239.539, "r_y2": 194.68499999999995, "r_x3": 134.765, "r_y3": 194.68499999999995, "coord_origin": "TOPLEFT"}, "text": "has several key benefits.", "orig": "has several key benefits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 216.24099999999999, "r_x1": 480.593, "r_y1": 216.24099999999999, "r_x2": 480.593, "r_y2": 207.44399999999996, "r_x3": 149.709, "r_y3": 207.44399999999996, "coord_origin": "TOPLEFT"}, "text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"throughput and responsiveness.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.518, "r_y0": 553.809, "r_x1": 176.341, "r_y1": 553.809, "r_x2": 176.341, "r_y2": 542.52, "r_x3": 151.518, "r_y3": 542.52, "coord_origin": "TOPLEFT"}, "text": "CoRR", "orig": "CoRR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.464, "r_y0": 552.6610000000001, "r_x1": 250.68, "r_y1": 552.6610000000001, "r_x2": 250.68, "r_y2": 544.735, "r_x3": 179.464, "r_y3": 544.735, "coord_origin": "TOPLEFT"}, "text": "abs/2206.00785", "orig": "abs/2206.00785", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.804, "r_y0": 553.809, "r_x1": 281.957, "r_y1": 553.809, "r_x2": 281.957, "r_y2": 542.52, "r_x3": 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Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. The benefits of OTSL are that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 399.923, "r_x1": 169.253, "r_y1": 399.923, "r_x2": 169.253, "r_y2": 388.634, "r_x3": 163.111, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.795, "r_y0": 399.923, "r_x1": 452.244, "r_y1": 399.923, "r_x2": 452.244, "r_y2": 388.634, "r_x3": 172.795, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "orig": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 410.882, "r_x1": 452.249, "r_y1": 410.882, "r_x2": 452.249, "r_y2": 399.593, "r_x3": 163.111, "r_y3": 399.593, "coord_origin": "TOPLEFT"}, "text": "the sequence length to half of HTML on average. Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 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452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, 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"coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 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They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. 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Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. As of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.899, "r": 480.597, "b": 146.514, "coord_origin": "TOPLEFT"}, "confidence": 0.8918349742889404, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.58100000000002, "r_x1": 480.597, "r_y1": 128.58100000000002, "r_x2": 480.597, "r_y2": 115.899, "r_x3": 134.765, "r_y3": 115.899, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for 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Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. The benefits of OTSL are that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 399.923, "r_x1": 169.253, "r_y1": 399.923, "r_x2": 169.253, "r_y2": 388.634, "r_x3": 163.111, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.795, "r_y0": 399.923, "r_x1": 452.244, "r_y1": 399.923, "r_x2": 452.244, "r_y2": 388.634, "r_x3": 172.795, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "orig": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 410.882, "r_x1": 452.249, "r_y1": 410.882, "r_x2": 452.249, "r_y2": 399.593, "r_x3": 163.111, "r_y3": 399.593, "coord_origin": "TOPLEFT"}, "text": "the sequence length to half of HTML on average. Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 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452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, 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"coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.111, "t": 476.479, "r": 452.241, "b": 498.727, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 522.12, "r": 228.934, "b": 532.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 134.765, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 532.688, "r_x1": 228.934, "r_y1": 532.688, "r_x2": 228.934, "r_y2": 522.12, "r_x3": 154.938, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "Introduction", "orig": "Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 548.287, "r": 480.596, "b": 628.815, 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They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. 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Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. As of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 18.34, "t": 165.0, "r": 36.34, "b": 555.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8899643421173096, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.899, "r": 480.597, "b": 146.514, "coord_origin": 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documents is a crucial task in any", "orig": "Extracting tables from documents is a crucial task in any", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 290.334, "r_x1": 452.242, "r_y1": 290.334, "r_x2": 452.242, "r_y2": 279.04499999999996, "r_x3": 163.111, "r_y3": 279.04499999999996, "coord_origin": "TOPLEFT"}, "text": "document conversion pipeline. Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. The benefits of OTSL are that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 399.923, "r_x1": 169.253, "r_y1": 399.923, "r_x2": 169.253, "r_y2": 388.634, "r_x3": 163.111, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.795, "r_y0": 399.923, "r_x1": 452.244, "r_y1": 399.923, "r_x2": 452.244, "r_y2": 388.634, "r_x3": 172.795, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "orig": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 410.882, "r_x1": 452.249, "r_y1": 410.882, "r_x2": 452.249, "r_y2": 399.593, "r_x3": 163.111, "r_y3": 399.593, "coord_origin": "TOPLEFT"}, "text": "the sequence length to half of HTML on average. Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 297.115, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "inference", "orig": "inference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.158, "r_y0": 421.841, "r_x1": 365.708, "r_y1": 421.841, "r_x2": 365.708, "r_y2": 410.552, "r_x3": 337.158, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "time is", "orig": "time is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.146, "r_y0": 421.841, "r_x1": 396.249, "r_y1": 421.841, "r_x2": 396.249, "r_y2": 410.552, "r_x3": 370.146, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "halved", "orig": "halved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.696, "r_y0": 421.841, "r_x1": 452.241, "r_y1": 421.841, "r_x2": 452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.282, "r_y0": 443.758, "r_x1": 234.527, "r_y1": 443.758, "r_x2": 234.527, "r_y2": 432.469, "r_x3": 204.282, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "correct.", "orig": "correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.588, "r_y0": 443.758, "r_x1": 452.244, "r_y1": 443.758, "r_x2": 452.244, "r_y2": 432.469, "r_x3": 238.588, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "This in turn eliminates most post-processing needs.", "orig": "This in turn eliminates most post-processing needs.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 454.717, "r_x1": 452.246, "r_y1": 454.717, "r_x2": 452.246, "r_y2": 443.428, "r_x3": 163.111, "r_y3": 443.428, "coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.111, "t": 476.479, "r": 452.241, "b": 498.727, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 522.12, "r": 228.934, "b": 532.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 134.765, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 532.688, "r_x1": 228.934, "r_y1": 532.688, "r_x2": 228.934, "r_y2": 522.12, "r_x3": 154.938, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "Introduction", "orig": "Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 548.287, "r": 480.596, "b": 628.815, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.0840000000001, "r_x1": 480.594, "r_y1": 557.0840000000001, "r_x2": 480.594, "r_y2": 548.287, "r_x3": 134.765, "r_y3": 548.287, "coord_origin": "TOPLEFT"}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.04, "r_x1": 480.592, "r_y1": 569.04, "r_x2": 480.592, "r_y2": 560.2429999999999, "r_x3": 134.765, "r_y3": 560.2429999999999, "coord_origin": "TOPLEFT"}, "text": "manuals, specification sheets or marketing material. They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. Therefore, accurate table extraction is achieved these days with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.815, "r_x1": 279.327, "r_y1": 628.815, "r_x2": 279.327, "r_y2": 620.018, "r_x3": 134.765, "r_y3": 620.018, "coord_origin": "TOPLEFT"}, "text": "machine-learning based methods.", "orig": "machine-learning based methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.708, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 149.708, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "In modern document understanding systems [1,15], table extraction is typi-", "orig": "In modern document understanding systems [1,15], table extraction is typi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.596, "r_y1": 652.9, "r_x2": 480.596, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. As of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. 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Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. 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Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 297.115, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "inference", "orig": "inference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.158, "r_y0": 421.841, "r_x1": 365.708, "r_y1": 421.841, "r_x2": 365.708, "r_y2": 410.552, "r_x3": 337.158, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "time is", "orig": "time is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.146, "r_y0": 421.841, "r_x1": 396.249, "r_y1": 421.841, "r_x2": 396.249, "r_y2": 410.552, "r_x3": 370.146, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "halved", "orig": "halved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.696, "r_y0": 421.841, "r_x1": 452.241, "r_y1": 421.841, "r_x2": 452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.282, "r_y0": 443.758, "r_x1": 234.527, "r_y1": 443.758, "r_x2": 234.527, "r_y2": 432.469, "r_x3": 204.282, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "correct.", "orig": "correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.588, "r_y0": 443.758, "r_x1": 452.244, "r_y1": 443.758, "r_x2": 452.244, "r_y2": 432.469, "r_x3": 238.588, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "This in turn eliminates most post-processing needs.", "orig": "This in turn eliminates most post-processing needs.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 454.717, "r_x1": 452.246, "r_y1": 454.717, "r_x2": 452.246, "r_y2": 443.428, "r_x3": 163.111, "r_y3": 443.428, "coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.111, "t": 476.479, "r": 452.241, "b": 498.727, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 522.12, "r": 228.934, "b": 532.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 134.765, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 532.688, "r_x1": 228.934, "r_y1": 532.688, "r_x2": 228.934, "r_y2": 522.12, "r_x3": 154.938, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "Introduction", "orig": "Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 548.287, "r": 480.596, "b": 628.815, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.0840000000001, "r_x1": 480.594, "r_y1": 557.0840000000001, "r_x2": 480.594, "r_y2": 548.287, "r_x3": 134.765, "r_y3": 548.287, "coord_origin": "TOPLEFT"}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.04, "r_x1": 480.592, "r_y1": 569.04, "r_x2": 480.592, "r_y2": 560.2429999999999, "r_x3": 134.765, "r_y3": 560.2429999999999, "coord_origin": "TOPLEFT"}, "text": "manuals, specification sheets or marketing material. They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. Therefore, accurate table extraction is achieved these days with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.815, "r_x1": 279.327, "r_y1": 628.815, "r_x2": 279.327, "r_y2": 620.018, "r_x3": 134.765, "r_y3": 620.018, "coord_origin": "TOPLEFT"}, "text": "machine-learning based methods.", "orig": "machine-learning based methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.708, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 149.708, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "In modern document understanding systems [1,15], table extraction is typi-", "orig": "In modern document understanding systems [1,15], table extraction is typi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.596, "r_y1": 652.9, "r_x2": 480.596, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. As of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.34, "t": 165.0, "r": 36.34, "b": 555.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8899643421173096, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. 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On the other hand, the problem of table", "orig": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 124.11900000000003, "r": 480.592, "b": 201.16200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 134.26, "r_x1": 162.644, "r_y1": 134.26, "r_x2": 162.644, "r_y2": 126.33399999999995, "r_x3": 134.765, "r_y3": 126.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 1.", "orig": "Fig. 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.062, "r_y0": 135.40800000000002, "r_x1": 480.591, "r_y1": 135.40800000000002, "r_x2": 480.591, "r_y2": 124.11900000000003, "r_x3": 167.062, "r_y3": 124.11900000000003, "coord_origin": "TOPLEFT"}, "text": "Comparison between HTML and OTSL table structure representation: (A)", "orig": "Comparison between HTML and OTSL table structure representation: (A)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 146.36699999999996, "r_x1": 190.823, "r_y1": 146.36699999999996, "r_x2": 190.823, "r_y2": 135.07799999999997, "r_x3": 134.765, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "table-example", "orig": "table-example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.199, "r_y0": 146.36699999999996, "r_x1": 480.59, "r_y1": 146.36699999999996, "r_x2": 480.59, "r_y2": 135.07799999999997, "r_x3": 195.199, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "with complex row and column headers, including a 2D empty span,", "orig": "with complex row and column headers, including a 2D empty span,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 157.32600000000002, "r_x1": 480.591, "r_y1": 157.32600000000002, "r_x2": 480.591, "r_y2": 146.03700000000003, "r_x3": 134.765, "r_y3": 146.03700000000003, "coord_origin": "TOPLEFT"}, "text": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "orig": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 168.28499999999997, "r_x1": 480.592, "r_y1": 168.28499999999997, "r_x2": 480.592, "r_y2": 156.99599999999998, "r_x3": 134.765, "r_y3": 156.99599999999998, "coord_origin": "TOPLEFT"}, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "orig": "HTML representation, (D) OTSL representation. This example demonstrates many of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.24400000000003, "r_x1": 480.589, "r_y1": 179.24400000000003, "r_x2": 480.589, "r_y2": 167.95500000000004, "r_x3": 134.765, "r_y3": 167.95500000000004, "coord_origin": "TOPLEFT"}, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "orig": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 190.20299999999997, "r_x1": 480.59, "r_y1": 190.20299999999997, "r_x2": 480.59, "r_y2": 178.914, "r_x3": 134.765, "r_y3": 178.914, "coord_origin": "TOPLEFT"}, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure 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On the other hand, the problem of table", "orig": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 124.11900000000003, "r": 480.592, "b": 201.16200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 134.26, "r_x1": 162.644, "r_y1": 134.26, "r_x2": 162.644, "r_y2": 126.33399999999995, "r_x3": 134.765, "r_y3": 126.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 1.", "orig": "Fig. 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.062, "r_y0": 135.40800000000002, "r_x1": 480.591, "r_y1": 135.40800000000002, "r_x2": 480.591, "r_y2": 124.11900000000003, "r_x3": 167.062, "r_y3": 124.11900000000003, "coord_origin": "TOPLEFT"}, "text": "Comparison between HTML and OTSL table structure representation: (A)", "orig": "Comparison between HTML and OTSL table structure representation: (A)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 146.36699999999996, "r_x1": 190.823, "r_y1": 146.36699999999996, "r_x2": 190.823, "r_y2": 135.07799999999997, "r_x3": 134.765, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "table-example", "orig": "table-example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.199, "r_y0": 146.36699999999996, "r_x1": 480.59, "r_y1": 146.36699999999996, "r_x2": 480.59, "r_y2": 135.07799999999997, "r_x3": 195.199, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "with complex row and column headers, including a 2D empty span,", "orig": "with complex row and column headers, including a 2D empty span,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 157.32600000000002, "r_x1": 480.591, "r_y1": 157.32600000000002, "r_x2": 480.591, "r_y2": 146.03700000000003, "r_x3": 134.765, "r_y3": 146.03700000000003, "coord_origin": "TOPLEFT"}, "text": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "orig": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 168.28499999999997, "r_x1": 480.592, "r_y1": 168.28499999999997, "r_x2": 480.592, "r_y2": 156.99599999999998, "r_x3": 134.765, "r_y3": 156.99599999999998, "coord_origin": "TOPLEFT"}, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "orig": "HTML representation, (D) OTSL representation. This example demonstrates many of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.24400000000003, "r_x1": 480.589, "r_y1": 179.24400000000003, "r_x2": 480.589, "r_y2": 167.95500000000004, "r_x3": 134.765, "r_y3": 167.95500000000004, "coord_origin": "TOPLEFT"}, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "orig": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 190.20299999999997, "r_x1": 480.59, "r_y1": 190.20299999999997, "r_x2": 480.59, "r_y2": 178.914, "r_x3": 134.765, "r_y3": 178.914, "coord_origin": "TOPLEFT"}, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "orig": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 201.16200000000003, "r_x1": 460.871, "r_y1": 201.16200000000003, "r_x2": 460.871, "r_y2": 189.87300000000005, "r_x3": 134.765, "r_y3": 189.87300000000005, "coord_origin": "TOPLEFT"}, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "orig": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9688959121704102, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 396.411, "r_y0": 289.478, "r_x1": 446.134, "r_y1": 288.875, "r_x2": 446.134, "r_y2": 280.49, "r_x3": 396.411, "r_y3": 281.093, "coord_origin": "TOPLEFT"}, "text": "C L C L L", "orig": "C L C L L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.748, "r_y0": 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On the other hand, the problem of table", "orig": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 124.11900000000003, "r": 480.592, "b": 201.16200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 134.26, "r_x1": 162.644, "r_y1": 134.26, "r_x2": 162.644, "r_y2": 126.33399999999995, "r_x3": 134.765, "r_y3": 126.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 1.", "orig": "Fig. 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.062, "r_y0": 135.40800000000002, "r_x1": 480.591, "r_y1": 135.40800000000002, "r_x2": 480.591, "r_y2": 124.11900000000003, "r_x3": 167.062, "r_y3": 124.11900000000003, "coord_origin": "TOPLEFT"}, "text": "Comparison between HTML and OTSL table structure representation: (A)", "orig": "Comparison between HTML and OTSL table structure representation: (A)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 146.36699999999996, "r_x1": 190.823, "r_y1": 146.36699999999996, "r_x2": 190.823, "r_y2": 135.07799999999997, "r_x3": 134.765, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "table-example", "orig": "table-example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.199, "r_y0": 146.36699999999996, "r_x1": 480.59, "r_y1": 146.36699999999996, "r_x2": 480.59, "r_y2": 135.07799999999997, "r_x3": 195.199, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "with complex row and column headers, including a 2D empty span,", "orig": "with complex row and column headers, including a 2D empty span,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 157.32600000000002, "r_x1": 480.591, "r_y1": 157.32600000000002, "r_x2": 480.591, "r_y2": 146.03700000000003, "r_x3": 134.765, "r_y3": 146.03700000000003, "coord_origin": "TOPLEFT"}, "text": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "orig": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 168.28499999999997, "r_x1": 480.592, "r_y1": 168.28499999999997, "r_x2": 480.592, "r_y2": 156.99599999999998, "r_x3": 134.765, "r_y3": 156.99599999999998, "coord_origin": "TOPLEFT"}, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "orig": "HTML representation, (D) OTSL representation. This example demonstrates many of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.24400000000003, "r_x1": 480.589, "r_y1": 179.24400000000003, "r_x2": 480.589, "r_y2": 167.95500000000004, "r_x3": 134.765, "r_y3": 167.95500000000004, "coord_origin": "TOPLEFT"}, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "orig": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 190.20299999999997, "r_x1": 480.59, "r_y1": 190.20299999999997, "r_x2": 480.59, "r_y2": 178.914, "r_x3": 134.765, "r_y3": 178.914, "coord_origin": "TOPLEFT"}, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "orig": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 201.16200000000003, "r_x1": 460.871, "r_y1": 201.16200000000003, "r_x2": 460.871, "r_y2": 189.87300000000005, "r_x3": 134.765, "r_y3": 189.87300000000005, "coord_origin": "TOPLEFT"}, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "orig": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9688959121704102, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 396.411, "r_y0": 289.478, "r_x1": 446.134, "r_y1": 288.875, "r_x2": 446.134, "r_y2": 280.49, "r_x3": 396.411, "r_y3": 281.093, "coord_origin": "TOPLEFT"}, "text": "C L C L L", "orig": "C L C L L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.748, "r_y0": 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On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.861, "r_y0": 163.59699999999998, "r_x1": 194.555, "r_y1": 163.59699999999998, "r_x2": 194.555, "r_y2": 154.79999999999995, "r_x3": 173.861, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "as is", "orig": "as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.61, "r_y0": 163.59699999999998, "r_x1": 211.535, "r_y1": 163.59699999999998, "r_x2": 211.535, "r_y2": 154.79999999999995, "r_x3": 199.61, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 215.769, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. 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"coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.355, "r_y0": 533.3489999999999, "r_x1": 480.59, "r_y1": 533.3489999999999, "r_x2": 480.59, "r_y2": 524.552, "r_x3": 442.355, "r_y3": 524.552, "coord_origin": "TOPLEFT"}, "text": "columns,", "orig": "columns,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.59, "r_y1": 545.304, "r_x2": 480.59, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "orig": "learning based approaches fall into three categories: object detection (OD) meth-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 152.252, "r_y1": 569.2139999999999, "r_x2": 152.252, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "ods,", "orig": "ods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.433, "r_y0": 569.2139999999999, "r_x1": 257.459, "r_y1": 569.2139999999999, "r_x2": 257.459, "r_y2": 560.417, "r_x3": 157.433, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "Graph-Neural-Network", "orig": "Graph-Neural-Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.65, "r_y0": 569.2139999999999, "r_x1": 480.591, "r_y1": 569.2139999999999, "r_x2": 480.591, "r_y2": 560.417, "r_x3": 262.65, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "(GNN) methods and Image-to-Markup-Sequence", "orig": "(GNN) methods and Image-to-Markup-Sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 484.121, "r_y1": 581.169, "r_x2": 484.121, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. The graph nodes represent the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "orig": "content of each table cell, an embedding vector from the table image, or geomet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.589, "r_y1": 652.9, "r_x2": 480.589, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "orig": "ric coordinates of the table cell. The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 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134.765, "t": 118.93399999999997, "r": 480.592, "b": 211.41700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": 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154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 214.836, "r": 480.596, "b": 331.23, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 334.648, "r": 480.596, "b": 439.087, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 236.769, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 484.121, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. 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"coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.355, "r_y0": 533.3489999999999, "r_x1": 480.59, "r_y1": 533.3489999999999, "r_x2": 480.59, "r_y2": 524.552, "r_x3": 442.355, "r_y3": 524.552, "coord_origin": "TOPLEFT"}, "text": "columns,", "orig": "columns,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.59, "r_y1": 545.304, "r_x2": 480.59, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "orig": "learning based approaches fall into three categories: object detection (OD) meth-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 152.252, "r_y1": 569.2139999999999, "r_x2": 152.252, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "ods,", "orig": "ods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.433, "r_y0": 569.2139999999999, "r_x1": 257.459, "r_y1": 569.2139999999999, "r_x2": 257.459, "r_y2": 560.417, "r_x3": 157.433, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "Graph-Neural-Network", "orig": "Graph-Neural-Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.65, "r_y0": 569.2139999999999, "r_x1": 480.591, "r_y1": 569.2139999999999, "r_x2": 480.591, "r_y2": 560.417, "r_x3": 262.65, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "(GNN) methods and Image-to-Markup-Sequence", "orig": "(GNN) methods and Image-to-Markup-Sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 484.121, "r_y1": 581.169, "r_x2": 484.121, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. 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The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 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false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.861, "r_y0": 163.59699999999998, "r_x1": 194.555, "r_y1": 163.59699999999998, "r_x2": 194.555, "r_y2": 154.79999999999995, "r_x3": 173.861, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "as is", "orig": "as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.61, "r_y0": 163.59699999999998, "r_x1": 211.535, "r_y1": 163.59699999999998, "r_x2": 211.535, "r_y2": 154.79999999999995, "r_x3": 199.61, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 215.769, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 214.836, "r": 480.596, "b": 331.23, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 334.648, "r": 480.596, "b": 439.087, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 236.769, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 484.121, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. In the recent past, a wide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.393, "r_x1": 164.401, "r_y1": 521.393, "r_x2": 164.401, "r_y2": 512.596, "r_x3": 134.765, "r_y3": 512.596, "coord_origin": "TOPLEFT"}, "text": "variety", "orig": "variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.581, "r_y0": 521.393, "r_x1": 177.605, "r_y1": 521.393, "r_x2": 177.605, "r_y2": 512.596, "r_x3": 169.581, "r_y3": 512.596, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.786, "r_y0": 521.393, "r_x1": 223.77, "r_y1": 521.393, "r_x2": 223.77, "r_y2": 512.596, "r_x3": 182.786, "r_y3": 512.596, "coord_origin": 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"coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.355, "r_y0": 533.3489999999999, "r_x1": 480.59, "r_y1": 533.3489999999999, "r_x2": 480.59, "r_y2": 524.552, "r_x3": 442.355, "r_y3": 524.552, "coord_origin": "TOPLEFT"}, "text": "columns,", "orig": "columns,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.59, "r_y1": 545.304, "r_x2": 480.59, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "orig": "learning based approaches fall into three categories: object detection (OD) meth-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 152.252, "r_y1": 569.2139999999999, "r_x2": 152.252, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "ods,", "orig": "ods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.433, "r_y0": 569.2139999999999, "r_x1": 257.459, "r_y1": 569.2139999999999, "r_x2": 257.459, "r_y2": 560.417, "r_x3": 157.433, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "Graph-Neural-Network", "orig": "Graph-Neural-Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.65, "r_y0": 569.2139999999999, "r_x1": 480.591, "r_y1": 569.2139999999999, "r_x2": 480.591, "r_y2": 560.417, "r_x3": 262.65, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "(GNN) methods and Image-to-Markup-Sequence", "orig": "(GNN) methods and Image-to-Markup-Sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 484.121, "r_y1": 581.169, "r_x2": 484.121, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. The graph nodes represent the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "orig": "content of each table cell, an embedding vector from the table image, or geomet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.589, "r_y1": 652.9, "r_x2": 480.589, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "orig": "ric coordinates of the table cell. The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.592, "b": 211.41700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.861, "r_y0": 163.59699999999998, "r_x1": 194.555, "r_y1": 163.59699999999998, "r_x2": 194.555, "r_y2": 154.79999999999995, "r_x3": 173.861, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "as is", "orig": "as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.61, "r_y0": 163.59699999999998, "r_x1": 211.535, "r_y1": 163.59699999999998, "r_x2": 211.535, "r_y2": 154.79999999999995, "r_x3": 199.61, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 215.769, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 214.836, "r": 480.596, "b": 331.23, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 334.648, "r": 480.596, "b": 439.087, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 236.769, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 484.121, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. 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"coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.355, "r_y0": 533.3489999999999, "r_x1": 480.59, "r_y1": 533.3489999999999, "r_x2": 480.59, "r_y2": 524.552, "r_x3": 442.355, "r_y3": 524.552, "coord_origin": "TOPLEFT"}, "text": "columns,", "orig": "columns,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.59, "r_y1": 545.304, "r_x2": 480.59, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "orig": "learning based approaches fall into three categories: object detection (OD) meth-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 152.252, "r_y1": 569.2139999999999, "r_x2": 152.252, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "ods,", "orig": "ods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.433, "r_y0": 569.2139999999999, "r_x1": 257.459, "r_y1": 569.2139999999999, "r_x2": 257.459, "r_y2": 560.417, "r_x3": 157.433, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "Graph-Neural-Network", "orig": "Graph-Neural-Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.65, "r_y0": 569.2139999999999, "r_x1": 480.591, "r_y1": 569.2139999999999, "r_x2": 480.591, "r_y2": 560.417, "r_x3": 262.65, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "(GNN) methods and Image-to-Markup-Sequence", "orig": "(GNN) methods and Image-to-Markup-Sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 484.121, "r_y1": 581.169, "r_x2": 484.121, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. The graph nodes represent the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "orig": "content of each table cell, an embedding vector from the table image, or geomet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.589, "r_y1": 652.9, "r_x2": 480.589, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "orig": "ric coordinates of the table cell. The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. For each decoded table cell (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.299, "r_y0": 319.275, "r_x1": 356.971, "r_y1": 319.275, "r_x2": 356.971, "r_y2": 310.478, "r_x3": 333.299, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.085, "r_y0": 319.275, "r_x1": 363.725, "r_y1": 319.275, "r_x2": 363.725, "r_y2": 310.478, "r_x3": 357.085, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "),", "orig": "),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.64, "r_y0": 319.275, "r_x1": 480.594, "r_y1": 319.275, "r_x2": 480.594, "r_y2": 310.478, "r_x3": 367.64, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "the attention is passed to", "orig": "the attention is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 148.598, "r_y1": 331.23, "r_x2": 148.598, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.277, "r_y0": 331.23, "r_x1": 166.531, "r_y1": 331.23, "r_x2": 166.531, "r_y2": 322.433, "r_x3": 152.277, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.427, "r_y0": 331.23, "r_x1": 202.111, "r_y1": 331.23, "r_x2": 202.111, "r_y2": 322.433, "r_x3": 170.427, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "decoder", "orig": "decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.864, "r_y0": 331.23, "r_x1": 480.587, "r_y1": 331.23, "r_x2": 480.587, "r_y2": 322.433, "r_x3": 206.864, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to predict the content with an embedded OCR approach. The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.7280000000001, "r_x1": 176.662, "r_y1": 534.7280000000001, "r_x2": 176.662, "r_y2": 525.931, "r_x3": 134.765, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "to deliver", "orig": "to deliver", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.896, "r_y0": 534.7280000000001, "r_x1": 246.551, "r_y1": 534.7280000000001, "r_x2": 246.551, "r_y2": 525.931, "r_x3": 180.896, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.785, "r_y0": 534.7280000000001, "r_x1": 480.59, "r_y1": 534.7280000000001, "r_x2": 480.59, "r_y2": 525.931, "r_x3": 250.785, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "prediction accuracy [9]. This motivated the authors", "orig": "prediction accuracy [9]. 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558.639, "r_x2": 480.59, "r_y2": 549.842, "r_x3": 134.765, "r_y3": 549.842, "coord_origin": "TOPLEFT"}, "text": "be further improved by optimising the table structure representation language.", "orig": "be further improved by optimising the table structure representation language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 570.594, "r_x1": 183.086, "r_y1": 570.594, "r_x2": 183.086, "r_y2": 561.797, "r_x3": 134.765, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "We believe", "orig": "We believe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.977, "r_y0": 570.594, "r_x1": 204.079, "r_y1": 570.594, "r_x2": 204.079, "r_y2": 561.797, "r_x3": 187.977, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "this", "orig": "this", "text_direction": 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"improving", "orig": "improving", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 413.949, "r_y0": 570.594, "r_x1": 441.09, "r_y1": 570.594, "r_x2": 441.09, "r_y2": 561.797, "r_x3": 413.949, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "neural", "orig": "neural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.982, "r_y0": 570.594, "r_x1": 480.589, "r_y1": 570.594, "r_x2": 480.589, "r_y2": 561.797, "r_x3": 445.982, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "network", "orig": "network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 582.549, "r_x1": 249.278, "r_y1": 582.549, "r_x2": 249.278, "r_y2": 573.752, "r_x3": 134.765, "r_y3": 573.752, "coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, 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These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 259.23800000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 262.65700000000004, "r": 480.596, "b": 486.647, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. For each decoded table cell (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.299, "r_y0": 319.275, "r_x1": 356.971, "r_y1": 319.275, "r_x2": 356.971, "r_y2": 310.478, "r_x3": 333.299, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.085, "r_y0": 319.275, "r_x1": 363.725, "r_y1": 319.275, "r_x2": 363.725, "r_y2": 310.478, "r_x3": 357.085, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "),", "orig": "),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.64, "r_y0": 319.275, "r_x1": 480.594, "r_y1": 319.275, "r_x2": 480.594, "r_y2": 310.478, "r_x3": 367.64, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "the attention is passed to", "orig": "the attention is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 148.598, "r_y1": 331.23, "r_x2": 148.598, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.277, "r_y0": 331.23, "r_x1": 166.531, "r_y1": 331.23, "r_x2": 166.531, "r_y2": 322.433, "r_x3": 152.277, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.427, "r_y0": 331.23, "r_x1": 202.111, "r_y1": 331.23, "r_x2": 202.111, "r_y2": 322.433, "r_x3": 170.427, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "decoder", "orig": "decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.864, "r_y0": 331.23, "r_x1": 480.587, "r_y1": 331.23, "r_x2": 480.587, "r_y2": 322.433, "r_x3": 206.864, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to predict the content with an embedded OCR approach. The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 490.066, "r": 480.594, "b": 582.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. 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This motivated the authors", "orig": "prediction accuracy [9]. 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558.639, "r_x2": 480.59, "r_y2": 549.842, "r_x3": 134.765, "r_y3": 549.842, "coord_origin": "TOPLEFT"}, "text": "be further improved by optimising the table structure representation language.", "orig": "be further improved by optimising the table structure representation language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 570.594, "r_x1": 183.086, "r_y1": 570.594, "r_x2": 183.086, "r_y2": 561.797, "r_x3": 134.765, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "We believe", "orig": "We believe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.977, "r_y0": 570.594, "r_x1": 204.079, "r_y1": 570.594, "r_x2": 204.079, "r_y2": 561.797, "r_x3": 187.977, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "this", "orig": "this", "text_direction": 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"coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 605.55, "r": 269.624, "b": 616.1179999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.594, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.591, "r_y1": 640.9449999999999, "r_x2": 480.591, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.587, "r_y1": 652.9, "r_x2": 480.587, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 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These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 259.23800000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 262.65700000000004, "r": 480.596, "b": 486.647, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. For each decoded table cell (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.299, "r_y0": 319.275, "r_x1": 356.971, "r_y1": 319.275, "r_x2": 356.971, "r_y2": 310.478, "r_x3": 333.299, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.085, "r_y0": 319.275, "r_x1": 363.725, "r_y1": 319.275, "r_x2": 363.725, "r_y2": 310.478, "r_x3": 357.085, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "),", "orig": "),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.64, "r_y0": 319.275, "r_x1": 480.594, "r_y1": 319.275, "r_x2": 480.594, "r_y2": 310.478, "r_x3": 367.64, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "the attention is passed to", "orig": "the attention is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 148.598, "r_y1": 331.23, "r_x2": 148.598, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.277, "r_y0": 331.23, "r_x1": 166.531, "r_y1": 331.23, "r_x2": 166.531, "r_y2": 322.433, "r_x3": 152.277, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.427, "r_y0": 331.23, "r_x1": 202.111, "r_y1": 331.23, "r_x2": 202.111, "r_y2": 322.433, "r_x3": 170.427, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "decoder", "orig": "decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.864, "r_y0": 331.23, "r_x1": 480.587, "r_y1": 331.23, "r_x2": 480.587, "r_y2": 322.433, "r_x3": 206.864, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to predict the content with an embedded OCR approach. The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 490.066, "r": 480.594, "b": 582.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.7280000000001, "r_x1": 176.662, "r_y1": 534.7280000000001, "r_x2": 176.662, "r_y2": 525.931, "r_x3": 134.765, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "to deliver", "orig": "to deliver", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.896, "r_y0": 534.7280000000001, "r_x1": 246.551, "r_y1": 534.7280000000001, "r_x2": 246.551, "r_y2": 525.931, "r_x3": 180.896, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.785, "r_y0": 534.7280000000001, "r_x1": 480.59, "r_y1": 534.7280000000001, "r_x2": 480.59, "r_y2": 525.931, "r_x3": 250.785, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "prediction accuracy [9]. This motivated the authors", "orig": "prediction accuracy [9]. This motivated the authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 546.683, "r_x1": 143.619, "r_y1": 546.683, "r_x2": 143.619, "r_y2": 537.886, "r_x3": 134.765, "r_y3": 537.886, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.56, "r_y0": 546.683, "r_x1": 194.815, "r_y1": 546.683, "r_x2": 194.815, "r_y2": 537.886, "r_x3": 148.56, "r_y3": 537.886, "coord_origin": "TOPLEFT"}, "text": "investigate", "orig": "investigate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.756, "r_y0": 546.683, "r_x1": 205.566, "r_y1": 546.683, "r_x2": 205.566, "r_y2": 537.886, "r_x3": 199.756, "r_y3": 537.886, 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558.639, "r_x2": 480.59, "r_y2": 549.842, "r_x3": 134.765, "r_y3": 549.842, "coord_origin": "TOPLEFT"}, "text": "be further improved by optimising the table structure representation language.", "orig": "be further improved by optimising the table structure representation language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 570.594, "r_x1": 183.086, "r_y1": 570.594, "r_x2": 183.086, "r_y2": 561.797, "r_x3": 134.765, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "We believe", "orig": "We believe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.977, "r_y0": 570.594, "r_x1": 204.079, "r_y1": 570.594, "r_x2": 204.079, "r_y2": 561.797, "r_x3": 187.977, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "this", "orig": "this", "text_direction": 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"coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 605.55, "r": 269.624, "b": 616.1179999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.594, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.591, "r_y1": 640.9449999999999, "r_x2": 480.591, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.587, "r_y1": 652.9, "r_x2": 480.587, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "orig": "Given an image of a table, the Im2Seq model predicts the structure of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.594, "r_y1": 664.856, "r_x2": 480.594, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 259.23800000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 262.65700000000004, "r": 480.596, "b": 486.647, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. 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The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 490.066, "r": 480.594, "b": 582.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.7280000000001, "r_x1": 176.662, "r_y1": 534.7280000000001, "r_x2": 176.662, "r_y2": 525.931, "r_x3": 134.765, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "to deliver", "orig": "to deliver", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.896, "r_y0": 534.7280000000001, "r_x1": 246.551, "r_y1": 534.7280000000001, "r_x2": 246.551, "r_y2": 525.931, "r_x3": 180.896, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.785, "r_y0": 534.7280000000001, "r_x1": 480.59, "r_y1": 534.7280000000001, "r_x2": 480.59, "r_y2": 525.931, "r_x3": 250.785, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "prediction accuracy [9]. This motivated the authors", "orig": "prediction accuracy [9]. This motivated the authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 546.683, "r_x1": 143.619, "r_y1": 546.683, "r_x2": 143.619, "r_y2": 537.886, "r_x3": 134.765, "r_y3": 537.886, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.56, "r_y0": 546.683, "r_x1": 194.815, "r_y1": 546.683, "r_x2": 194.815, "r_y2": 537.886, "r_x3": 148.56, "r_y3": 537.886, "coord_origin": "TOPLEFT"}, "text": "investigate", "orig": "investigate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.756, "r_y0": 546.683, "r_x1": 205.566, "r_y1": 546.683, "r_x2": 205.566, "r_y2": 537.886, "r_x3": 199.756, "r_y3": 537.886, 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"coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 605.55, "r": 269.624, "b": 616.1179999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.594, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.591, "r_y1": 640.9449999999999, "r_x2": 480.591, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.587, "r_y1": 652.9, "r_x2": 480.587, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "orig": "Given an image of a table, the Im2Seq model predicts the structure of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.594, "r_y1": 664.856, "r_x2": 480.594, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 187.50699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 218.86400000000003, "r": 469.752, "b": 230.15300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.207, "r": 480.595, "b": 532.421, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 536.043, "r": 480.593, "b": 580.706, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 584.327, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 187.50699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 218.86400000000003, "r": 469.752, "b": 230.15300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.207, "r": 480.595, "b": 532.421, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 536.043, "r": 480.593, "b": 580.706, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 584.327, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 187.50699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 218.86400000000003, "r": 469.752, "b": 230.15300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.207, "r": 480.595, "b": 532.421, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 536.043, "r": 480.593, "b": 580.706, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 584.327, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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neighbor cell to create a", "orig": "merging with the upper neighbor cell to create a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 605.874, "r_x1": 171.676, "r_y1": 605.874, "r_x2": 171.676, "r_y2": 597.077, "r_x3": 151.701, "r_y3": 597.077, "coord_origin": "TOPLEFT"}, "text": "span", "orig": "span", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 618.467, "r_x1": 193.483, "r_y1": 618.467, "r_x2": 193.483, "r_y2": 609.67, 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454.555, "r_y2": 609.67, "r_x3": 242.209, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "to merge with both left and upper neighbor cells", "orig": "to merge with both left and upper neighbor cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 631.061, "r_x1": 181.994, "r_y1": 631.061, "r_x2": 181.994, "r_y2": 622.264, "r_x3": 151.701, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "\"NL\" -", "orig": "\"NL\" -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.317, "r_y0": 631.061, "r_x1": 221.462, "r_y1": 631.061, "r_x2": 221.462, "r_y2": 622.264, "r_x3": 185.317, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "new-line", "orig": "new-line", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.461, "r_y0": 631.061, "r_x1": 224.228, "r_y1": 631.061, "r_x2": 224.228, "r_y2": 622.264, "r_x3": 221.461, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.545, "r_y0": 631.061, "r_x1": 328.617, "r_y1": 631.061, "r_x2": 328.617, "r_y2": 622.264, "r_x3": 227.545, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "switch to the next row.", "orig": "switch to the next row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 228.223, "r_y1": 664.855, "r_x2": 228.223, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "conversion to HTML.", "orig": "conversion to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 143.48299999999995, "r": 480.596, "b": 295.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 372.508, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.595, "b": 441.6, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 261.801, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 521.706, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 460.544, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 556.777, "r_x1": 193.206, "r_y1": 556.777, "r_x2": 193.206, "r_y2": 547.98, "r_x3": 151.701, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "\"C\" cell -", "orig": "\"C\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.522, "r_y0": 556.777, "r_x1": 263.466, "r_y1": 556.777, "r_x2": 263.466, "r_y2": 547.98, "r_x3": 196.522, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "a new table cell", "orig": "a new table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.815, "r_y0": 556.777, "r_x1": 460.544, "r_y1": 556.777, "r_x2": 460.544, "r_y2": 547.98, "r_x3": 267.815, "r_y3": 547.98, "coord_origin": 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"text": "up-looking cell", "orig": "up-looking cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 259.892, "r_y0": 593.919, "r_x1": 262.659, "r_y1": 593.919, "r_x2": 262.659, "r_y2": 585.1220000000001, "r_x3": 259.892, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.295, "r_y0": 593.919, "r_x1": 480.588, "r_y1": 593.919, "r_x2": 480.588, "r_y2": 585.1220000000001, "r_x3": 266.295, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": "merging with the upper neighbor cell to create a", "orig": "merging with the upper neighbor cell to create a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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609.67, "coord_origin": "TOPLEFT"}, "text": "to merge with both left and upper neighbor cells", "orig": "to merge with both left and upper neighbor cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 328.617, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 631.061, "r_x1": 181.994, "r_y1": 631.061, "r_x2": 181.994, "r_y2": 622.264, "r_x3": 151.701, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "\"NL\" -", "orig": "\"NL\" -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.317, "r_y0": 631.061, "r_x1": 221.462, "r_y1": 631.061, "r_x2": 221.462, "r_y2": 622.264, "r_x3": 185.317, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "new-line", "orig": "new-line", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.461, "r_y0": 631.061, "r_x1": 224.228, "r_y1": 631.061, "r_x2": 224.228, "r_y2": 622.264, "r_x3": 221.461, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.545, "r_y0": 631.061, "r_x1": 328.617, "r_y1": 631.061, "r_x2": 328.617, "r_y2": 622.264, "r_x3": 227.545, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "switch to the next row.", "orig": "switch to the next row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 228.223, "r_y1": 664.855, "r_x2": 228.223, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "conversion to HTML.", "orig": "conversion to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 143.48299999999995, "r": 480.596, "b": 295.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 372.508, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.595, "b": 441.6, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 261.801, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 521.706, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 460.544, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 556.777, "r_x1": 193.206, "r_y1": 556.777, "r_x2": 193.206, "r_y2": 547.98, "r_x3": 151.701, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "\"C\" cell -", "orig": "\"C\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.522, "r_y0": 556.777, "r_x1": 263.466, "r_y1": 556.777, "r_x2": 263.466, "r_y2": 547.98, "r_x3": 196.522, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "a new table cell", "orig": "a new table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.815, "r_y0": 556.777, "r_x1": 460.544, "r_y1": 556.777, "r_x2": 460.544, "r_y2": 547.98, "r_x3": 267.815, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "that either has or does not have cell content", "orig": "that either has or does not have cell content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.993, "t": 560.563, "r": 480.594, "b": 581.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 569.37, "r_x1": 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span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.993, "t": 609.66, "r": 454.555, "b": 618.467, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 618.467, "r_x1": 193.483, "r_y1": 618.467, "r_x2": 193.483, "r_y2": 609.67, "r_x3": 151.701, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "\"X\" cell -", "orig": "\"X\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.799, 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"from_ocr": false}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 328.617, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 631.061, "r_x1": 181.994, "r_y1": 631.061, "r_x2": 181.994, "r_y2": 622.264, "r_x3": 151.701, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "\"NL\" -", "orig": "\"NL\" -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.317, "r_y0": 631.061, "r_x1": 221.462, "r_y1": 631.061, "r_x2": 221.462, "r_y2": 622.264, "r_x3": 185.317, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "new-line", "orig": "new-line", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.461, "r_y0": 631.061, "r_x1": 224.228, "r_y1": 631.061, "r_x2": 224.228, "r_y2": 622.264, "r_x3": 221.461, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.545, "r_y0": 631.061, "r_x1": 328.617, "r_y1": 631.061, "r_x2": 328.617, "r_y2": 622.264, "r_x3": 227.545, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "switch to the next row.", "orig": "switch to the next row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 228.223, "r_y1": 664.855, "r_x2": 228.223, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "conversion to HTML.", "orig": "conversion to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 143.48299999999995, "r": 480.596, "b": 295.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 372.508, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.595, "b": 441.6, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 261.801, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 521.706, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 460.544, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 556.777, "r_x1": 193.206, "r_y1": 556.777, "r_x2": 193.206, "r_y2": 547.98, "r_x3": 151.701, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "\"C\" cell -", "orig": "\"C\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.522, "r_y0": 556.777, "r_x1": 263.466, "r_y1": 556.777, "r_x2": 263.466, "r_y2": 547.98, "r_x3": 196.522, "r_y3": 547.98, "coord_origin": 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Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 403.805, "r_x1": 146.72, "r_y1": 403.805, "r_x2": 146.72, "r_y2": 395.008, "r_x3": 138.973, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 403.805, "r_x1": 223.304, "r_y1": 403.805, "r_x2": 223.304, "r_y2": 394.998, "r_x3": 151.701, "r_y3": 394.998, "coord_origin": "TOPLEFT"}, "text": "Cross cell rule", "orig": "Cross cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 223.307, "r_y0": 403.805, "r_x1": 226.074, "r_y1": 403.805, "r_x2": 226.074, "r_y2": 395.008, "r_x3": 223.307, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 415.764, "r_x1": 480.592, "r_y1": 415.764, "r_x2": 480.592, "r_y2": 406.967, "r_x3": 151.701, "r_y3": 406.967, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 427.719, "r_x1": 480.592, "r_y1": 427.719, "r_x2": 480.592, "r_y2": 418.922, "r_x3": 151.701, "r_y3": 418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, "r_y2": 442.836, "r_x3": 138.973, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 451.633, "r_x1": 221.323, "r_y1": 451.633, "r_x2": 221.323, "r_y2": 442.826, "r_x3": 151.701, "r_y3": 442.826, "coord_origin": "TOPLEFT"}, "text": "First row rule", "orig": "First row rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.327, "r_y0": 451.633, "r_x1": 224.094, "r_y1": 451.633, "r_x2": 224.094, "r_y2": 442.836, "r_x3": 221.327, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.411, "r_y0": 451.633, "r_x1": 474.59, "r_y1": 451.633, "r_x2": 474.59, "r_y2": 442.836, "r_x3": 227.411, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "orig": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 463.591, "r_x1": 146.72, "r_y1": 463.591, "r_x2": 146.72, "r_y2": 454.794, "r_x3": 138.973, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 463.591, "r_x1": 240.72, "r_y1": 463.591, "r_x2": 240.72, "r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.587, "b": 156.79100000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", "text_direction": 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160.859, "r_y3": 305.296, "coord_origin": "TOPLEFT"}, "text": "Language Syntax", "orig": "Language Syntax", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.248, "r": 363.796, "b": 334.045, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 334.045, "r_x1": 363.796, "r_y1": 334.045, "r_x2": 363.796, "r_y2": 325.248, "r_x3": 134.765, "r_y3": 325.248, "coord_origin": "TOPLEFT"}, "text": "The OTSL representation follows these syntax rules:", "orig": "The OTSL representation follows these syntax rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 138.973, "t": 347.171, "r": 480.589, "b": 367.934, "coord_origin": "TOPLEFT"}, "confidence": 0.9588112235069275, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 355.978, "r_x1": 146.72, "r_y1": 355.978, "r_x2": 146.72, "r_y2": 347.181, "r_x3": 138.973, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 355.978, "r_x1": 257.379, "r_y1": 355.978, "r_x2": 257.379, "r_y2": 347.171, "r_x3": 151.701, "r_y3": 347.171, "coord_origin": "TOPLEFT"}, "text": "Left-looking cell rule", "orig": "Left-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.383, "r_y0": 355.978, "r_x1": 260.15, "r_y1": 355.978, "r_x2": 260.15, "r_y2": 347.181, "r_x3": 257.383, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, 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418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 138.973, "t": 442.826, "r": 474.59, "b": 451.633, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, 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"r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.973, "t": 478.697, "r": 480.595, "b": 499.46, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 512.5930000000001, "r": 480.596, "b": 640.942, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.587, "b": 156.79100000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.276, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 166.276, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "orig": "OTSL description of table structure: A - table example; B - graphical repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 171.412, "r_y1": 145.832, "r_x2": 171.412, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "sentation", "orig": "sentation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.546, "r_y0": 145.832, "r_x1": 182.968, "r_y1": 145.832, "r_x2": 182.968, "r_y2": 134.543, "r_x3": 175.546, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.093, "r_y0": 145.832, "r_x1": 207.058, "r_y1": 145.832, "r_x2": 207.058, "r_y2": 134.543, "r_x3": 187.093, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table", "orig": "table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.192, "r_y0": 145.832, "r_x1": 250.188, "r_y1": 145.832, "r_x2": 250.188, "r_y2": 134.543, "r_x3": 211.192, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "structure;", "orig": "structure;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 254.312, "r_y0": 145.832, "r_x1": 480.587, "r_y1": 145.832, "r_x2": 480.587, "r_y2": 134.543, "r_x3": 254.312, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "C - mapping structure on a grid; D - OTSL structure", "orig": "C - mapping structure on a grid; D - OTSL structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 156.79100000000005, "r_x1": 306.116, "r_y1": 156.79100000000005, "r_x2": 306.116, "r_y2": 145.50199999999995, "r_x3": 134.765, "r_y3": 145.50199999999995, "coord_origin": "TOPLEFT"}, "text": "encoding; E - explanation on cell encoding", "orig": "encoding; E - explanation on cell encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65028381347656, "t": 163.79708862304688, "r": 449.5505676269531, "b": 280.3409423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7868534922599792, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 374.493, "r_y0": 177.88099999999997, "r_x1": 428.861, "r_y1": 177.22199999999998, "r_x2": 428.861, "r_y2": 168.05399999999997, "r_x3": 374.493, "r_y3": 168.71299999999997, "coord_origin": "TOPLEFT"}, "text": "C L C L L", "orig": "C L C L L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 373.769, "r_y0": 202.21299999999997, "r_x1": 448.17, "r_y1": 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359.562, "r_y3": 168.37900000000002, "coord_origin": "TOPLEFT"}, "text": "D", "orig": "D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 169.747, "t": 243.31100000000004, "r": 175.271, "b": 250.95100000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.747, "r_y0": 250.95100000000002, "r_x1": 175.271, "r_y1": 250.95100000000002, "r_x2": 175.271, "r_y2": 243.31100000000004, "r_x3": 169.747, "r_y3": 243.31100000000004, "coord_origin": "TOPLEFT"}, "text": "E", "orig": "E", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.296, "r": 246.652, "b": 314.103, "coord_origin": "TOPLEFT"}, "confidence": 0.9233023524284363, "cells": [{"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 314.103, "r_x1": 149.402, "r_y1": 314.103, "r_x2": 149.402, "r_y2": 305.296, "r_x3": 134.765, "r_y3": 305.296, "coord_origin": "TOPLEFT"}, "text": "4.2", "orig": "4.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 314.103, "r_x1": 246.652, "r_y1": 314.103, "r_x2": 246.652, "r_y2": 305.296, "r_x3": 160.859, "r_y3": 305.296, "coord_origin": "TOPLEFT"}, "text": "Language Syntax", "orig": "Language Syntax", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.248, "r": 363.796, "b": 334.045, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 334.045, "r_x1": 363.796, "r_y1": 334.045, "r_x2": 363.796, "r_y2": 325.248, "r_x3": 134.765, "r_y3": 325.248, "coord_origin": "TOPLEFT"}, "text": "The OTSL representation follows these syntax rules:", "orig": "The OTSL representation follows these syntax rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.973, "t": 347.171, "r": 480.589, "b": 367.934, "coord_origin": "TOPLEFT"}, "confidence": 0.9588112235069275, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 355.978, "r_x1": 146.72, "r_y1": 355.978, "r_x2": 146.72, "r_y2": 347.181, "r_x3": 138.973, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 355.978, "r_x1": 257.379, "r_y1": 355.978, "r_x2": 257.379, "r_y2": 347.171, "r_x3": 151.701, "r_y3": 347.171, "coord_origin": "TOPLEFT"}, "text": "Left-looking cell rule", "orig": "Left-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.383, "r_y0": 355.978, "r_x1": 260.15, "r_y1": 355.978, "r_x2": 260.15, "r_y2": 347.181, "r_x3": 257.383, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.184, "r_y0": 355.978, "r_x1": 480.589, "r_y1": 355.978, "r_x2": 480.589, "r_y2": 347.181, "r_x3": 264.184, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"L\" cell must be either", "orig": "The left neighbour of an \"L\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 367.934, "r_x1": 283.594, "r_y1": 367.934, "r_x2": 283.594, "r_y2": 359.137, "r_x3": 151.701, "r_y3": 359.137, "coord_origin": "TOPLEFT"}, "text": "another \"L\" cell or a \"C\" cell.", "orig": "another \"L\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.973, "t": 371.085, "r": 480.592, "b": 391.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 379.892, "r_x1": 146.72, "r_y1": 379.892, "r_x2": 146.72, "r_y2": 371.095, "r_x3": 138.973, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 379.892, "r_x1": 252.112, "r_y1": 379.892, "r_x2": 252.112, "r_y2": 371.085, "r_x3": 151.701, "r_y3": 371.085, "coord_origin": "TOPLEFT"}, "text": "Up-looking cell rule", "orig": "Up-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.112, "r_y0": 379.892, "r_x1": 254.879, "r_y1": 379.892, "r_x2": 254.879, "r_y2": 371.095, "r_x3": 252.112, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.764, "r_y0": 379.892, "r_x1": 480.592, "r_y1": 379.892, "r_x2": 480.592, "r_y2": 371.095, "r_x3": 258.764, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": "The upper neighbour of a \"U\" cell must be either", "orig": "The upper neighbour of a \"U\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 391.847, "r_x1": 284.839, "r_y1": 391.847, "r_x2": 284.839, "r_y2": 383.05, "r_x3": 151.701, "r_y3": 383.05, "coord_origin": "TOPLEFT"}, "text": "another \"U\" cell or a \"C\" cell.", "orig": "another \"U\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.973, "t": 394.998, "r": 226.074, "b": 403.805, "coord_origin": "TOPLEFT"}, "confidence": 0.6506174206733704, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 403.805, "r_x1": 146.72, "r_y1": 403.805, "r_x2": 146.72, "r_y2": 395.008, "r_x3": 138.973, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 403.805, "r_x1": 223.304, "r_y1": 403.805, "r_x2": 223.304, "r_y2": 394.998, "r_x3": 151.701, "r_y3": 394.998, "coord_origin": "TOPLEFT"}, "text": "Cross cell rule", "orig": "Cross cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 223.307, "r_y0": 403.805, "r_x1": 226.074, "r_y1": 403.805, "r_x2": 226.074, "r_y2": 395.008, "r_x3": 223.307, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.701, "t": 406.967, "r": 480.592, "b": 439.674, "coord_origin": "TOPLEFT"}, "confidence": 0.7247239947319031, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 415.764, "r_x1": 480.592, "r_y1": 415.764, "r_x2": 480.592, "r_y2": 406.967, "r_x3": 151.701, "r_y3": 406.967, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 427.719, "r_x1": 480.592, "r_y1": 427.719, "r_x2": 480.592, "r_y2": 418.922, "r_x3": 151.701, "r_y3": 418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.973, "t": 442.826, "r": 474.59, "b": 451.633, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, "r_y2": 442.836, "r_x3": 138.973, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 451.633, "r_x1": 221.323, "r_y1": 451.633, "r_x2": 221.323, "r_y2": 442.826, "r_x3": 151.701, "r_y3": 442.826, "coord_origin": "TOPLEFT"}, "text": "First row rule", "orig": "First row rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.327, "r_y0": 451.633, "r_x1": 224.094, "r_y1": 451.633, "r_x2": 224.094, "r_y2": 442.836, "r_x3": 221.327, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.411, "r_y0": 451.633, "r_x1": 474.59, "r_y1": 451.633, "r_x2": 474.59, "r_y2": 442.836, "r_x3": 227.411, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "orig": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.973, "t": 454.784, "r": 480.588, "b": 475.546, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 463.591, "r_x1": 146.72, "r_y1": 463.591, "r_x2": 146.72, "r_y2": 454.794, "r_x3": 138.973, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 463.591, "r_x1": 240.72, "r_y1": 463.591, "r_x2": 240.72, "r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.973, "t": 478.697, "r": 480.595, "b": 499.46, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 512.5930000000001, "r": 480.596, "b": 640.942, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.587, "b": 156.79100000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.276, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 166.276, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "orig": "OTSL description of table structure: A - table example; B - graphical repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 171.412, "r_y1": 145.832, "r_x2": 171.412, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "sentation", "orig": "sentation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.546, "r_y0": 145.832, "r_x1": 182.968, "r_y1": 145.832, "r_x2": 182.968, "r_y2": 134.543, "r_x3": 175.546, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.093, "r_y0": 145.832, "r_x1": 207.058, "r_y1": 145.832, "r_x2": 207.058, "r_y2": 134.543, "r_x3": 187.093, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table", "orig": "table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.192, "r_y0": 145.832, "r_x1": 250.188, "r_y1": 145.832, "r_x2": 250.188, "r_y2": 134.543, "r_x3": 211.192, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "structure;", "orig": "structure;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 254.312, "r_y0": 145.832, "r_x1": 480.587, "r_y1": 145.832, "r_x2": 480.587, "r_y2": 134.543, "r_x3": 254.312, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "C - mapping structure on a grid; D - OTSL structure", "orig": "C - mapping structure on a grid; D - OTSL structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 156.79100000000005, "r_x1": 306.116, "r_y1": 156.79100000000005, "r_x2": 306.116, "r_y2": 145.50199999999995, "r_x3": 134.765, "r_y3": 145.50199999999995, "coord_origin": "TOPLEFT"}, "text": "encoding; E - explanation on cell encoding", "orig": "encoding; E - explanation on cell encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65028381347656, "t": 163.79708862304688, "r": 449.5505676269531, "b": 280.3409423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7868534922599792, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 374.493, "r_y0": 177.88099999999997, "r_x1": 428.861, "r_y1": 177.22199999999998, "r_x2": 428.861, "r_y2": 168.05399999999997, "r_x3": 374.493, "r_y3": 168.71299999999997, "coord_origin": "TOPLEFT"}, "text": "C L C L L", "orig": "C L C L L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 373.769, "r_y0": 202.21299999999997, "r_x1": 448.17, "r_y1": 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"coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 355.978, "r_x1": 257.379, "r_y1": 355.978, "r_x2": 257.379, "r_y2": 347.171, "r_x3": 151.701, "r_y3": 347.171, "coord_origin": "TOPLEFT"}, "text": "Left-looking cell rule", "orig": "Left-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.383, "r_y0": 355.978, "r_x1": 260.15, "r_y1": 355.978, "r_x2": 260.15, "r_y2": 347.181, "r_x3": 257.383, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.184, "r_y0": 355.978, "r_x1": 480.589, "r_y1": 355.978, "r_x2": 480.589, "r_y2": 347.181, "r_x3": 264.184, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"L\" cell must be either", "orig": "The left neighbour of an \"L\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 367.934, "r_x1": 283.594, "r_y1": 367.934, "r_x2": 283.594, "r_y2": 359.137, "r_x3": 151.701, "r_y3": 359.137, "coord_origin": "TOPLEFT"}, "text": "another \"L\" cell or a \"C\" cell.", "orig": "another \"L\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.973, "t": 371.085, "r": 480.592, "b": 391.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 379.892, "r_x1": 146.72, "r_y1": 379.892, "r_x2": 146.72, "r_y2": 371.095, "r_x3": 138.973, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 379.892, "r_x1": 252.112, "r_y1": 379.892, "r_x2": 252.112, "r_y2": 371.085, "r_x3": 151.701, "r_y3": 371.085, "coord_origin": "TOPLEFT"}, "text": "Up-looking cell rule", "orig": "Up-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.112, "r_y0": 379.892, "r_x1": 254.879, "r_y1": 379.892, "r_x2": 254.879, "r_y2": 371.095, "r_x3": 252.112, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.764, "r_y0": 379.892, "r_x1": 480.592, "r_y1": 379.892, "r_x2": 480.592, "r_y2": 371.095, "r_x3": 258.764, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": "The upper neighbour of a \"U\" cell must be either", "orig": "The upper neighbour of a \"U\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 391.847, "r_x1": 284.839, "r_y1": 391.847, "r_x2": 284.839, "r_y2": 383.05, "r_x3": 151.701, "r_y3": 383.05, "coord_origin": "TOPLEFT"}, "text": "another \"U\" cell or a \"C\" cell.", "orig": "another \"U\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.973, "t": 394.998, "r": 226.074, "b": 403.805, "coord_origin": "TOPLEFT"}, "confidence": 0.6506174206733704, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 403.805, "r_x1": 146.72, "r_y1": 403.805, "r_x2": 146.72, "r_y2": 395.008, "r_x3": 138.973, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 403.805, "r_x1": 223.304, "r_y1": 403.805, "r_x2": 223.304, "r_y2": 394.998, "r_x3": 151.701, "r_y3": 394.998, "coord_origin": "TOPLEFT"}, "text": "Cross cell rule", "orig": "Cross cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 223.307, "r_y0": 403.805, "r_x1": 226.074, "r_y1": 403.805, "r_x2": 226.074, "r_y2": 395.008, "r_x3": 223.307, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.701, "t": 406.967, "r": 480.592, "b": 439.674, "coord_origin": "TOPLEFT"}, "confidence": 0.7247239947319031, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 415.764, "r_x1": 480.592, "r_y1": 415.764, "r_x2": 480.592, "r_y2": 406.967, "r_x3": 151.701, "r_y3": 406.967, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 427.719, "r_x1": 480.592, "r_y1": 427.719, "r_x2": 480.592, "r_y2": 418.922, "r_x3": 151.701, "r_y3": 418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.973, "t": 442.826, "r": 474.59, "b": 451.633, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, "r_y2": 442.836, "r_x3": 138.973, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 451.633, "r_x1": 221.323, "r_y1": 451.633, "r_x2": 221.323, "r_y2": 442.826, "r_x3": 151.701, "r_y3": 442.826, "coord_origin": "TOPLEFT"}, "text": "First row rule", "orig": "First row rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.327, "r_y0": 451.633, "r_x1": 224.094, "r_y1": 451.633, "r_x2": 224.094, "r_y2": 442.836, "r_x3": 221.327, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.411, "r_y0": 451.633, "r_x1": 474.59, "r_y1": 451.633, "r_x2": 474.59, "r_y2": 442.836, "r_x3": 227.411, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "orig": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.973, "t": 454.784, "r": 480.588, "b": 475.546, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 463.591, "r_x1": 146.72, "r_y1": 463.591, "r_x2": 146.72, "r_y2": 454.794, "r_x3": 138.973, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 463.591, "r_x1": 240.72, "r_y1": 463.591, "r_x2": 240.72, "r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.973, "t": 478.697, "r": 480.595, "b": 499.46, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 512.5930000000001, "r": 480.596, "b": 640.942, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, 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Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. 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The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.589, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55700000000002, "r": 319.347, "b": 170.36400000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28200000000004, "r": 480.596, "b": 298.676, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.035, "b": 331.732, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.25, "r": 480.595, "b": 451.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, "r_y0": 403.867, "r_x1": 480.595, "r_y1": 403.867, "r_x2": 480.595, "r_y2": 395.07, "r_x3": 195.902, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. Secondly we pick the best hyper-parameters", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.822, "r_x1": 172.59, "r_y1": 415.822, "r_x2": 172.59, "r_y2": 407.025, "r_x3": 134.765, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "found in", "orig": "found in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 177.492, "r_y0": 415.822, "r_x1": 191.325, "r_y1": 415.822, "r_x2": 191.325, "r_y2": 407.025, "r_x3": 177.492, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.226, "r_y0": 415.822, "r_x1": 213.463, "r_y1": 415.822, "r_x2": 213.463, "r_y2": 407.025, "r_x3": 196.226, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "first", "orig": "first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 218.364, "r_y0": 415.822, "r_x1": 236.125, "r_y1": 415.822, "r_x2": 236.125, "r_y2": 407.025, "r_x3": 218.364, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "step", "orig": "step", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.027, "r_y0": 415.822, "r_x1": 297.661, "r_y1": 415.822, "r_x2": 297.661, "r_y2": 407.025, "r_x3": 241.027, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "and evaluate", "orig": "and evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 302.563, "r_y0": 415.822, "r_x1": 480.593, "r_y1": 415.822, "r_x2": 480.593, "r_y2": 407.025, "r_x3": 302.563, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "how OTSL impacts the performance of", "orig": "how OTSL impacts the performance of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.778, "r_x1": 377.321, "r_y1": 427.778, "r_x2": 377.321, "r_y2": 418.981, "r_x3": 134.765, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "TableFormer after training on other publicly available", "orig": "TableFormer after training on other publicly available", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.874, "r_y0": 427.778, "r_x1": 421.951, "r_y1": 427.778, "r_x2": 421.951, "r_y2": 418.981, "r_x3": 381.874, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "data sets", "orig": "data sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.504, "r_y0": 427.778, "r_x1": 480.595, "r_y1": 427.778, "r_x2": 480.595, "r_y2": 418.981, "r_x3": 426.504, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "(FinTabNet,", "orig": "(FinTabNet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.733, "r_x1": 480.592, "r_y1": 439.733, "r_x2": 480.592, "r_y2": 430.936, "r_x3": 134.765, "r_y3": 430.936, "coord_origin": "TOPLEFT"}, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 451.688, "r_x1": 479.303, "r_y1": 451.688, "r_x2": 479.303, "r_y2": 442.891, "r_x3": 134.765, "r_y3": 442.891, "coord_origin": "TOPLEFT"}, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "orig": "verted into OTSL format for this purpose, and will be made publicly available.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 482.433, "r": 480.591, "b": 504.681, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 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"rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "orig": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "orig": "section Over Union (IOU) threshold for the bounding-box predictions of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 155.846, "r_y1": 664.855, "r_x2": 155.846, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.442, "r_y0": 664.855, "r_x1": 480.59, "r_y1": 664.855, "r_x2": 480.59, "r_y2": 656.058, "r_x3": 159.442, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "The predicted OTSL structures were converted back to HTML format in", "orig": "The predicted OTSL structures were converted back to HTML format in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8301323056221008, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8631826043128967, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.589, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55700000000002, "r": 319.347, "b": 170.36400000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28200000000004, "r": 480.596, "b": 298.676, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.035, "b": 331.732, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.25, "r": 480.595, "b": 451.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, "r_y0": 403.867, "r_x1": 480.595, "r_y1": 403.867, "r_x2": 480.595, "r_y2": 395.07, "r_x3": 195.902, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. Secondly we pick the best hyper-parameters", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.822, "r_x1": 172.59, "r_y1": 415.822, "r_x2": 172.59, "r_y2": 407.025, "r_x3": 134.765, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "found in", "orig": "found in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 177.492, "r_y0": 415.822, "r_x1": 191.325, "r_y1": 415.822, "r_x2": 191.325, "r_y2": 407.025, "r_x3": 177.492, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.226, "r_y0": 415.822, "r_x1": 213.463, "r_y1": 415.822, "r_x2": 213.463, "r_y2": 407.025, "r_x3": 196.226, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "first", "orig": "first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 218.364, "r_y0": 415.822, "r_x1": 236.125, "r_y1": 415.822, "r_x2": 236.125, "r_y2": 407.025, "r_x3": 218.364, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "step", "orig": "step", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.027, "r_y0": 415.822, "r_x1": 297.661, "r_y1": 415.822, "r_x2": 297.661, "r_y2": 407.025, "r_x3": 241.027, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "and evaluate", "orig": "and evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 302.563, "r_y0": 415.822, "r_x1": 480.593, "r_y1": 415.822, "r_x2": 480.593, "r_y2": 407.025, "r_x3": 302.563, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "how OTSL impacts the performance of", "orig": "how OTSL impacts the performance of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.778, "r_x1": 377.321, "r_y1": 427.778, "r_x2": 377.321, "r_y2": 418.981, "r_x3": 134.765, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "TableFormer after training on other publicly available", "orig": "TableFormer after training on other publicly available", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.874, "r_y0": 427.778, "r_x1": 421.951, "r_y1": 427.778, "r_x2": 421.951, "r_y2": 418.981, "r_x3": 381.874, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "data sets", "orig": "data sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.504, "r_y0": 427.778, "r_x1": 480.595, "r_y1": 427.778, "r_x2": 480.595, "r_y2": 418.981, "r_x3": 426.504, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "(FinTabNet,", "orig": "(FinTabNet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.733, "r_x1": 480.592, "r_y1": 439.733, "r_x2": 480.592, "r_y2": 430.936, "r_x3": 134.765, "r_y3": 430.936, "coord_origin": "TOPLEFT"}, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 451.688, "r_x1": 479.303, "r_y1": 451.688, "r_x2": 479.303, "r_y2": 442.891, "r_x3": 134.765, "r_y3": 442.891, "coord_origin": "TOPLEFT"}, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "orig": "verted into OTSL format for this purpose, and will be made publicly available.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 482.433, "r": 480.591, "b": 504.681, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 484.648, "coord_origin": "TOPLEFT"}, "text": "Fig. 4.", "orig": "Fig. 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.196, "r_y0": 493.722, "r_x1": 480.591, "r_y1": 493.722, "r_x2": 480.591, "r_y2": 482.433, "r_x3": 165.196, "r_y3": 482.433, "coord_origin": "TOPLEFT"}, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "orig": "Architecture sketch of the TableFormer model, which is a representative for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.681, "r_x1": 206.702, "r_y1": 504.681, "r_x2": 206.702, "r_y2": 493.392, "r_x3": 134.765, "r_y3": 493.392, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approach.", "orig": "Im2Seq approach.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.70968627929688, "t": 508.0638427734375, "r": 472.73382568359375, "b": 593.6771850585938, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.3, "r_y0": 543.091, "r_x1": 149.407, "r_y1": 543.091, "r_x2": 149.407, "r_y2": 540.762, "r_x3": 147.3, "r_y3": 540.762, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.809, "r_y0": 543.091, "r_x1": 155.721, "r_y1": 543.091, "r_x2": 155.721, "r_y2": 540.762, "r_x3": 150.809, "r_y3": 540.762, "coord_origin": "TOPLEFT"}, "text": "Item", "orig": "Item", "text_direction": "left_to_right", 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"r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "orig": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "orig": "section Over Union (IOU) threshold for the bounding-box predictions of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 155.846, "r_y1": 664.855, "r_x2": 155.846, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.442, "r_y0": 664.855, "r_x1": 480.59, "r_y1": 664.855, "r_x2": 480.59, "r_y2": 656.058, "r_x3": 159.442, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "The predicted OTSL structures were converted back to HTML format in", "orig": "The predicted OTSL structures were converted back to HTML format in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.589, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55700000000002, "r": 319.347, "b": 170.36400000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28200000000004, "r": 480.596, "b": 298.676, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.035, "b": 331.732, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.25, "r": 480.595, "b": 451.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, "r_y0": 403.867, "r_x1": 480.595, "r_y1": 403.867, "r_x2": 480.595, "r_y2": 395.07, "r_x3": 195.902, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. Secondly we pick the best hyper-parameters", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.822, "r_x1": 172.59, "r_y1": 415.822, "r_x2": 172.59, "r_y2": 407.025, "r_x3": 134.765, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "found in", "orig": "found in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 177.492, "r_y0": 415.822, "r_x1": 191.325, "r_y1": 415.822, "r_x2": 191.325, "r_y2": 407.025, "r_x3": 177.492, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.226, "r_y0": 415.822, "r_x1": 213.463, "r_y1": 415.822, "r_x2": 213.463, "r_y2": 407.025, "r_x3": 196.226, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "first", "orig": "first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 218.364, "r_y0": 415.822, "r_x1": 236.125, "r_y1": 415.822, "r_x2": 236.125, "r_y2": 407.025, "r_x3": 218.364, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "step", "orig": "step", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.027, "r_y0": 415.822, "r_x1": 297.661, "r_y1": 415.822, "r_x2": 297.661, "r_y2": 407.025, "r_x3": 241.027, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "and evaluate", "orig": "and evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 302.563, "r_y0": 415.822, "r_x1": 480.593, "r_y1": 415.822, "r_x2": 480.593, "r_y2": 407.025, "r_x3": 302.563, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "how OTSL impacts the performance of", "orig": "how OTSL impacts the performance of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.778, "r_x1": 377.321, "r_y1": 427.778, "r_x2": 377.321, "r_y2": 418.981, "r_x3": 134.765, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "TableFormer after training on other publicly available", "orig": "TableFormer after training on other publicly available", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.874, "r_y0": 427.778, "r_x1": 421.951, "r_y1": 427.778, "r_x2": 421.951, "r_y2": 418.981, "r_x3": 381.874, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "data sets", "orig": "data sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.504, "r_y0": 427.778, "r_x1": 480.595, "r_y1": 427.778, "r_x2": 480.595, "r_y2": 418.981, "r_x3": 426.504, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "(FinTabNet,", "orig": "(FinTabNet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.733, "r_x1": 480.592, "r_y1": 439.733, "r_x2": 480.592, "r_y2": 430.936, "r_x3": 134.765, "r_y3": 430.936, "coord_origin": "TOPLEFT"}, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 451.688, "r_x1": 479.303, "r_y1": 451.688, "r_x2": 479.303, "r_y2": 442.891, "r_x3": 134.765, "r_y3": 442.891, "coord_origin": "TOPLEFT"}, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "orig": "verted into OTSL format for this purpose, and will be made publicly available.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 482.433, "r": 480.591, "b": 504.681, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 484.648, "coord_origin": "TOPLEFT"}, "text": "Fig. 4.", "orig": "Fig. 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.196, "r_y0": 493.722, "r_x1": 480.591, "r_y1": 493.722, "r_x2": 480.591, "r_y2": 482.433, "r_x3": 165.196, "r_y3": 482.433, "coord_origin": "TOPLEFT"}, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "orig": "Architecture sketch of the TableFormer model, which is a representative for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.681, "r_x1": 206.702, "r_y1": 504.681, "r_x2": 206.702, "r_y2": 493.392, "r_x3": 134.765, "r_y3": 493.392, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approach.", "orig": "Im2Seq approach.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.70968627929688, "t": 508.0638427734375, "r": 472.73382568359375, "b": 593.6771850585938, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.3, "r_y0": 543.091, "r_x1": 149.407, "r_y1": 543.091, "r_x2": 149.407, "r_y2": 540.762, "r_x3": 147.3, "r_y3": 540.762, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.809, "r_y0": 543.091, "r_x1": 155.721, "r_y1": 543.091, "r_x2": 155.721, "r_y2": 540.762, "r_x3": 150.809, "r_y3": 540.762, "coord_origin": "TOPLEFT"}, "text": "Item", "orig": "Item", "text_direction": "left_to_right", 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"r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "orig": "section Over Union (IOU) threshold for the bounding-box predictions of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 155.846, "r_y1": 664.855, "r_x2": 155.846, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.442, "r_y0": 664.855, "r_x1": 480.59, "r_y1": 664.855, "r_x2": 480.59, "r_y2": 656.058, "r_x3": 159.442, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "The predicted OTSL structures were converted back to HTML format in", "orig": "The predicted OTSL structures were converted back to HTML format in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "headers": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8301323056221008, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8631826043128967, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378753662109, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8857625722885132, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 151.64200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 318.448, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.593, "b": 254.159, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 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"r_x2": 300.652, "r_y2": 272.85699999999997, "r_x3": 275.957, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 284.146, "r_x1": 321.763, "r_y1": 284.146, "r_x2": 321.763, "r_y2": 272.85699999999997, "r_x3": 306.92, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.03, "r_y0": 284.146, "r_x1": 419.24, "r_y1": 284.146, "r_x2": 419.24, "r_y2": 272.85699999999997, "r_x3": 328.03, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 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"confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.105, "r_x1": 480.595, "r_y1": 295.105, "r_x2": 480.595, "r_y2": 283.816, "r_x3": 134.765, "r_y3": 283.816, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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"text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 164.921, "r_y1": 545.304, "r_x2": 164.921, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.193, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 318.448, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.593, "b": 254.159, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 245.36199999999997, "r_x3": 149.149, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 306.064, "r_x1": 480.595, "r_y1": 306.064, "r_x2": 480.595, "r_y2": 294.775, "r_x3": 134.765, "r_y3": 294.775, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 317.023, "r_x1": 163.504, "r_y1": 317.023, "r_x2": 163.504, "r_y2": 305.734, "r_x3": 134.765, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 317.023, "r_x1": 443.264, "r_y1": 317.023, "r_x2": 443.264, "r_y2": 305.734, "r_x3": 167.817, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 317.023, "r_x1": 480.595, "r_y1": 317.023, "r_x2": 480.595, "r_y2": 305.734, "r_x3": 447.577, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.982, "r_x1": 480.595, "r_y1": 327.982, "r_x2": 480.595, "r_y2": 316.693, "r_x3": 134.765, "r_y3": 316.693, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.746, "r_x1": 168.048, "r_y1": 350.746, "r_x2": 168.048, "r_y2": 339.457, "r_x3": 160.37, "r_y3": 339.457, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 363.697, "r_x1": 183.828, "r_y1": 363.697, "r_x2": 183.828, "r_y2": 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264.403, "b": 516.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 516.9590000000001, "r_x1": 149.402, "r_y1": 516.9590000000001, "r_x2": 149.402, "r_y2": 508.152, "r_x3": 134.765, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 516.9590000000001, "r_x1": 264.403, "r_y1": 516.9590000000001, "r_x2": 264.403, "r_y2": 508.152, "r_x3": 160.859, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.596, "b": 617.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.348, "r_x1": 480.591, "r_y1": 533.348, "r_x2": 480.591, "r_y2": 524.5509999999999, "r_x3": 134.765, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 164.921, "r_y1": 545.304, "r_x2": 164.921, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.193, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 151.64200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 318.448, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.593, "b": 254.159, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 245.36199999999997, "r_x3": 149.149, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 272.85699999999997, "r": 480.595, "b": 327.982, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 282.998, "r_x1": 160.118, "r_y1": 282.998, "r_x2": 160.118, "r_y2": 275.072, "r_x3": 134.765, "r_y3": 275.072, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.345, "r_y0": 282.998, "r_x1": 175.595, "r_y1": 282.998, "r_x2": 175.595, "r_y2": 275.072, "r_x3": 167.345, "r_y3": 275.072, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 284.146, "r_x1": 255.736, "r_y1": 284.146, "r_x2": 255.736, "r_y2": 272.85699999999997, "r_x3": 188.133, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.013, "r_y0": 284.146, "r_x1": 269.69, "r_y1": 284.146, "r_x2": 269.69, "r_y2": 272.85699999999997, "r_x3": 262.013, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.957, "r_y0": 284.146, "r_x1": 300.652, "r_y1": 284.146, "r_x2": 300.652, "r_y2": 272.85699999999997, "r_x3": 275.957, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 284.146, "r_x1": 321.763, "r_y1": 284.146, "r_x2": 321.763, "r_y2": 272.85699999999997, "r_x3": 306.92, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.03, "r_y0": 284.146, "r_x1": 419.24, "r_y1": 284.146, "r_x2": 419.24, "r_y2": 272.85699999999997, "r_x3": 328.03, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.508, "r_y0": 284.146, "r_x1": 435.233, "r_y1": 284.146, "r_x2": 435.233, "r_y2": 272.85699999999997, "r_x3": 425.508, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.509, "r_y0": 284.146, "r_x1": 454.309, "r_y1": 284.146, "r_x2": 454.309, "r_y2": 272.85699999999997, "r_x3": 441.509, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.576, "r_y0": 284.146, "r_x1": 480.594, "r_y1": 284.146, "r_x2": 480.594, "r_y2": 272.85699999999997, "r_x3": 460.576, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.105, "r_x1": 480.595, "r_y1": 295.105, "r_x2": 480.595, "r_y2": 283.816, "r_x3": 134.765, "r_y3": 283.816, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 306.064, "r_x1": 480.595, "r_y1": 306.064, "r_x2": 480.595, "r_y2": 294.775, "r_x3": 134.765, "r_y3": 294.775, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 317.023, "r_x1": 163.504, "r_y1": 317.023, "r_x2": 163.504, "r_y2": 305.734, "r_x3": 134.765, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 317.023, "r_x1": 443.264, "r_y1": 317.023, "r_x2": 443.264, "r_y2": 305.734, "r_x3": 167.817, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 317.023, "r_x1": 480.595, "r_y1": 317.023, "r_x2": 480.595, "r_y2": 305.734, "r_x3": 447.577, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.982, "r_x1": 480.595, "r_y1": 327.982, "r_x2": 480.595, "r_y2": 316.693, "r_x3": 134.765, "r_y3": 316.693, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.746, "r_x1": 168.048, "r_y1": 350.746, "r_x2": 168.048, "r_y2": 339.457, "r_x3": 160.37, "r_y3": 339.457, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 363.697, "r_x1": 183.828, "r_y1": 363.697, "r_x2": 183.828, "r_y2": 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264.403, "b": 516.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 516.9590000000001, "r_x1": 149.402, "r_y1": 516.9590000000001, "r_x2": 149.402, "r_y2": 508.152, "r_x3": 134.765, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 516.9590000000001, "r_x1": 264.403, "r_y1": 516.9590000000001, "r_x2": 264.403, "r_y2": 508.152, "r_x3": 160.859, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.596, "b": 617.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.348, "r_x1": 480.591, "r_y1": 533.348, "r_x2": 480.591, "r_y2": 524.5509999999999, "r_x3": 134.765, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 164.921, "r_y1": 545.304, "r_x2": 164.921, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.193, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378753662109, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8857625722885132, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 143.979, "r_y1": 102.78200000000004, "r_x2": 143.979, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "10", "orig": "10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.821, "r_y0": 102.78200000000004, "r_x1": 231.72, "r_y1": 102.78200000000004, "r_x2": 231.72, "r_y2": 91.49300000000005, "r_x3": 167.821, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. 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In", "orig": "demonstrates less overlap and more accurate bounding boxes with OTSL. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 342.568, "r_x1": 480.587, "r_y1": 342.568, "r_x2": 480.587, "r_y2": 333.771, "r_x3": 134.765, "r_y3": 333.771, "coord_origin": "TOPLEFT"}, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "orig": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 354.523, "r_x1": 480.59, "r_y1": 354.523, "r_x2": 480.59, "r_y2": 345.726, "r_x3": 134.765, "r_y3": 345.726, "coord_origin": "TOPLEFT"}, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "orig": "ken sequences, resulting in even more precise structure prediction and bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 366.478, "r_x1": 161.657, "r_y1": 366.478, "r_x2": 161.657, "r_y2": 357.681, "r_x3": 134.765, "r_y3": 357.681, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. 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"confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 578.748, "r_x1": 180.174, "r_y1": 555.783, "r_x2": 172.391, "r_y2": 555.777, "r_x3": 172.374, "r_y3": 578.742, "coord_origin": "TOPLEFT"}, "text": "HTML", "orig": "HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9301655888557434, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.376, "t": 91.49300000000005, "r": 480.59, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9007201790809631, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.376, "r_y0": 102.78200000000004, "r_x1": 480.59, "r_y1": 102.78200000000004, "r_x2": 480.59, "r_y2": 91.49300000000005, "r_x3": 471.376, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "11"}, {"label": "caption", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.588, "b": 178.70899999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8657060861587524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 6.", "orig": "Fig. 6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.215, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 165.215, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "Visualization of predicted structure and detected bounding boxes on a complex", "orig": "Visualization of predicted structure and detected bounding boxes on a complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 480.588, "r_y1": 145.832, "r_x2": 480.588, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "orig": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 156.79100000000005, "r_x1": 480.588, "r_y1": 156.79100000000005, "r_x2": 480.588, "r_y2": 145.50199999999995, "r_x3": 134.765, "r_y3": 145.50199999999995, "coord_origin": "TOPLEFT"}, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "orig": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.75, "r_x1": 480.588, "r_y1": 167.75, "r_x2": 480.588, "r_y2": 156.461, "r_x3": 134.765, "r_y3": 156.461, "coord_origin": "TOPLEFT"}, "text": "didn't complete the HTML sequence correctly and displayed a lot more of drift and", "orig": "didn't complete the HTML sequence correctly and displayed a lot more of drift and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.70899999999995, "r_x1": 415.845, "r_y1": 178.70899999999995, "r_x2": 415.845, "r_y2": 167.41999999999996, "r_x3": 134.765, "r_y3": 167.41999999999996, "coord_origin": "TOPLEFT"}, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "orig": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39285278320312, "t": 181.9665069580078, "r": 447.35137939453125, "b": 634.0056762695312, "coord_origin": "TOPLEFT"}, "confidence": 0.7612658739089966, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.505, "r_y0": 320.335, "r_x1": 177.596, "r_y1": 320.335, "r_x2": 177.596, "r_y2": 312.552, "r_x3": 171.505, "r_y3": 312.552, "coord_origin": "TOPLEFT"}, "text": "B", "orig": "B", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.058, "r_y0": 500.537, "r_x1": 177.149, "r_y1": 500.537, "r_x2": 177.149, "r_y2": 492.754, "r_x3": 171.058, "r_y3": 492.754, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 633.395, "r_x1": 374.963, "r_y1": 633.395, "r_x2": 374.963, "r_y2": 627.558, "r_x3": 283.047, "r_y3": 627.558, "coord_origin": "TOPLEFT"}, "text": "Incorrect end of HTML sequence", "orig": "Incorrect end of HTML sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 623.271, "r_x1": 398.06, "r_y1": 623.271, "r_x2": 398.06, "r_y2": 617.434, "r_x3": 283.047, "r_y3": 617.434, "coord_origin": "TOPLEFT"}, "text": "Horizontally merged cells are not present", "orig": "Horizontally merged cells are not present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.642, "r_y0": 471.511, 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cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.834, "r_y0": 194.32600000000002, "r_x1": 253.613, "r_y1": 194.32600000000002, "r_x2": 253.613, "r_y2": 186.962, "r_x3": 247.834, "r_y3": 186.962, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.19, "r_y0": 613.719, "r_x1": 381.547, "r_y1": 613.719, "r_x2": 381.547, "r_y2": 607.8820000000001, "r_x3": 292.19, "r_y3": 607.8820000000001, "coord_origin": "TOPLEFT"}, "text": "Bounding box drifting at the end", "orig": "Bounding box drifting at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 403.401, "r_x1": 180.173, "r_y1": 381.368, "r_x2": 172.39, "r_y2": 381.363, "r_x3": 172.374, "r_y3": 403.395, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 578.748, "r_x1": 180.174, "r_y1": 555.783, "r_x2": 172.391, "r_y2": 555.777, "r_x3": 172.374, "r_y3": 578.742, "coord_origin": "TOPLEFT"}, "text": "HTML", "orig": "HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 18, "label": "text", "bbox": {"l": 171.505, "t": 312.552, "r": 177.596, "b": 320.335, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.505, "r_y0": 320.335, "r_x1": 177.596, "r_y1": 320.335, "r_x2": 177.596, "r_y2": 312.552, "r_x3": 171.505, "r_y3": 312.552, "coord_origin": "TOPLEFT"}, "text": "B", "orig": "B", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 171.058, "t": 492.754, "r": 177.149, "b": 500.537, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.058, "r_y0": 500.537, "r_x1": 177.149, "r_y1": 500.537, "r_x2": 177.149, "r_y2": 492.754, "r_x3": 171.058, "r_y3": 492.754, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 283.047, "t": 627.558, "r": 374.963, "b": 633.395, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 633.395, "r_x1": 374.963, "r_y1": 633.395, "r_x2": 374.963, "r_y2": 627.558, "r_x3": 283.047, "r_y3": 627.558, "coord_origin": "TOPLEFT"}, "text": "Incorrect end of HTML sequence", "orig": "Incorrect end of HTML sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 283.047, "t": 617.434, "r": 398.06, "b": 623.271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 623.271, "r_x1": 398.06, "r_y1": 623.271, "r_x2": 398.06, "r_y2": 617.434, "r_x3": 283.047, "r_y3": 617.434, "coord_origin": "TOPLEFT"}, "text": "Horizontally merged cells are not present", "orig": "Horizontally merged cells are not present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 293.642, "t": 465.674, "r": 437.508, "b": 471.511, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.642, "r_y0": 471.511, "r_x1": 437.508, "r_y1": 471.511, "r_x2": 437.508, "r_y2": 465.674, "r_x3": 293.642, "r_y3": 465.674, "coord_origin": "TOPLEFT"}, "text": "Repeating pattern is well represented in predictions", "orig": "Repeating pattern is well represented in predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 181.891, "t": 288.436, "r": 239.235, "b": 294.273, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.891, "r_y0": 294.273, "r_x1": 239.235, "r_y1": 294.273, "r_x2": 239.235, "r_y2": 288.436, "r_x3": 181.891, "r_y3": 288.436, "coord_origin": "TOPLEFT"}, "text": "Repeating pattern of", "orig": "Repeating pattern of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 181.891, "t": 294.97, "r": 251.529, "b": 300.807, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.891, "r_y0": 300.807, "r_x1": 251.529, "r_y1": 300.807, "r_x2": 251.529, "r_y2": 294.97, "r_x3": 181.891, "r_y3": 294.97, "coord_origin": "TOPLEFT"}, "text": "horizontally merged cells", "orig": "horizontally merged cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 247.834, "t": 186.962, "r": 253.613, "b": 194.32600000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.834, "r_y0": 194.32600000000002, "r_x1": 253.613, "r_y1": 194.32600000000002, "r_x2": 253.613, "r_y2": 186.962, "r_x3": 247.834, "r_y3": 186.962, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 292.19, "t": 607.8820000000001, "r": 381.547, "b": 613.719, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.19, "r_y0": 613.719, "r_x1": 381.547, "r_y1": 613.719, "r_x2": 381.547, "r_y2": 607.8820000000001, "r_x3": 292.19, "r_y3": 607.8820000000001, "coord_origin": "TOPLEFT"}, "text": "Bounding box drifting at the end", "orig": "Bounding box drifting at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 172.374, "t": 381.363, "r": 180.173, "b": 403.401, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 403.401, "r_x1": 180.173, "r_y1": 381.368, "r_x2": 172.39, "r_y2": 381.363, "r_x3": 172.374, "r_y3": 403.395, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 172.374, "t": 555.777, "r": 180.174, "b": 578.748, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 578.748, "r_x1": 180.174, "r_y1": 555.783, "r_x2": 172.391, "r_y2": 555.777, "r_x3": 172.374, "r_y3": 578.742, "coord_origin": "TOPLEFT"}, "text": "HTML", "orig": "HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.588, "b": 178.70899999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8657060861587524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 6.", "orig": "Fig. 6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.215, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 165.215, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "Visualization of predicted structure and detected bounding boxes on a complex", "orig": "Visualization of predicted structure and detected bounding boxes on a complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 480.588, "r_y1": 145.832, "r_x2": 480.588, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "orig": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 156.79100000000005, "r_x1": 480.588, "r_y1": 156.79100000000005, "r_x2": 480.588, "r_y2": 145.50199999999995, "r_x3": 134.765, "r_y3": 145.50199999999995, "coord_origin": "TOPLEFT"}, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "orig": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.75, "r_x1": 480.588, "r_y1": 167.75, "r_x2": 480.588, "r_y2": 156.461, "r_x3": 134.765, "r_y3": 156.461, "coord_origin": "TOPLEFT"}, "text": "didn't complete the HTML sequence correctly and displayed a lot more of drift and", "orig": "didn't complete the HTML sequence correctly and displayed a lot more of drift and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.70899999999995, "r_x1": 415.845, "r_y1": 178.70899999999995, "r_x2": 415.845, "r_y2": 167.41999999999996, "r_x3": 134.765, "r_y3": 167.41999999999996, "coord_origin": "TOPLEFT"}, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "orig": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39285278320312, "t": 181.9665069580078, "r": 447.35137939453125, "b": 634.0056762695312, "coord_origin": "TOPLEFT"}, "confidence": 0.7612658739089966, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.505, "r_y0": 320.335, "r_x1": 177.596, "r_y1": 320.335, "r_x2": 177.596, "r_y2": 312.552, "r_x3": 171.505, "r_y3": 312.552, "coord_origin": "TOPLEFT"}, "text": "B", "orig": "B", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.058, "r_y0": 500.537, "r_x1": 177.149, "r_y1": 500.537, "r_x2": 177.149, "r_y2": 492.754, "r_x3": 171.058, "r_y3": 492.754, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 633.395, "r_x1": 374.963, "r_y1": 633.395, "r_x2": 374.963, "r_y2": 627.558, "r_x3": 283.047, "r_y3": 627.558, "coord_origin": "TOPLEFT"}, "text": "Incorrect end of HTML sequence", "orig": "Incorrect end of HTML sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 623.271, "r_x1": 398.06, "r_y1": 623.271, "r_x2": 398.06, "r_y2": 617.434, "r_x3": 283.047, "r_y3": 617.434, "coord_origin": "TOPLEFT"}, "text": "Horizontally merged cells are not present", "orig": "Horizontally merged cells are not present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.642, "r_y0": 471.511, 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.11699999999996, "r_x1": 141.489, "r_y1": 128.11699999999996, "r_x2": 141.489, "r_y2": 117.54899999999998, "r_x3": 134.765, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 128.11699999999996, "r_x1": 219.255, "r_y1": 128.11699999999996, "r_x2": 219.255, "r_y2": 117.54899999999998, "r_x3": 154.938, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "Conclusion", "orig": "Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 155.66099999999994, "r_x1": 480.595, "r_y1": 155.66099999999994, "r_x2": 480.595, "r_y2": 146.86400000000003, "r_x3": 134.765, "r_y3": 146.86400000000003, "coord_origin": "TOPLEFT"}, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.61599999999999, "r_x1": 152.499, "r_y1": 167.61599999999999, "r_x2": 152.499, "r_y2": 158.81899999999996, "r_x3": 134.765, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "ture", "orig": "ture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.963, "r_y0": 167.61599999999999, "r_x1": 205.131, "r_y1": 167.61599999999999, "r_x2": 205.131, "r_y2": 158.81899999999996, "r_x3": 156.963, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "recognition", "orig": "recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 209.594, "r_y0": 167.61599999999999, "r_x1": 480.595, "r_y1": 167.61599999999999, "r_x2": 480.595, "r_y2": 158.81899999999996, "r_x3": 209.594, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "with Im2Seq models is ill-suited and has serious limitations.", "orig": "with Im2Seq models is ill-suited and has serious limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly used general purpose languages,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.48199999999997, "r_x1": 239.539, "r_y1": 203.48199999999997, "r_x2": 239.539, "r_y2": 194.68499999999995, "r_x3": 134.765, "r_y3": 194.68499999999995, "coord_origin": "TOPLEFT"}, "text": "has several key benefits.", "orig": "has several key benefits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 216.24099999999999, "r_x1": 480.593, "r_y1": 216.24099999999999, "r_x2": 480.593, "r_y2": 207.44399999999996, "r_x3": 149.709, "r_y3": 207.44399999999996, "coord_origin": "TOPLEFT"}, "text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.607, "r_y0": 531.8910000000001, "r_x1": 277.21, "r_y1": 531.8910000000001, "r_x2": 277.21, "r_y2": 520.602, "r_x3": 220.607, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "M., Carvalho,", "orig": "M., Carvalho,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.594, "r_y0": 531.8910000000001, "r_x1": 293.621, "r_y1": 531.8910000000001, "r_x2": 293.621, "r_y2": 520.602, "r_x3": 281.594, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "A.,", "orig": "A.,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.996, "r_y0": 531.8910000000001, "r_x1": 379.518, "r_y1": 531.8910000000001, "r_x2": 379.518, "r_y2": 520.602, "r_x3": 297.996, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "Ramis, C.B., Staar,", "orig": "Ramis, C.B., Staar,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.894, "r_y0": 531.8910000000001, "r_x1": 413.839, "r_y1": 531.8910000000001, "r_x2": 413.839, "r_y2": 520.602, "r_x3": 383.894, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "P.W.J.:", "orig": "P.W.J.:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 418.214, "r_y0": 531.8910000000001, "r_x1": 459.065, "r_y1": 531.8910000000001, "r_x2": 459.065, "r_y2": 520.602, "r_x3": 418.214, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "Delivering", "orig": "Delivering", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 463.441, "r_y0": 531.8910000000001, "r_x1": 480.592, "r_y1": 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limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly 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"text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.799, "r": 480.595, "b": 468.103, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.821, "r": 197.686, "b": 504.389, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 520.602, "r": 480.592, "b": 563.8720000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.371, "r_y0": 531.8910000000001, "r_x1": 146.537, "r_y1": 531.8910000000001, "r_x2": 146.537, "r_y2": 520.602, "r_x3": 139.371, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.522, "r_y0": 531.8910000000001, "r_x1": 189.958, "r_y1": 531.8910000000001, "r_x2": 189.958, "r_y2": 520.602, "r_x3": 151.522, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "Auer, C.,", "orig": "Auer, C.,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.342, "r_y0": 531.8910000000001, "r_x1": 216.222, "r_y1": 531.8910000000001, "r_x2": 216.222, "r_y2": 520.602, "r_x3": 194.342, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "Dolfi,", "orig": "Dolfi,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.607, "r_y0": 531.8910000000001, "r_x1": 277.21, "r_y1": 531.8910000000001, "r_x2": 277.21, "r_y2": 520.602, "r_x3": 220.607, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "M., Carvalho,", "orig": "M., Carvalho,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.594, "r_y0": 531.8910000000001, "r_x1": 293.621, "r_y1": 531.8910000000001, "r_x2": 293.621, "r_y2": 520.602, "r_x3": 281.594, "r_y3": 520.602, 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Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54899999999998, "r": 219.255, "b": 128.11699999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.11699999999996, "r_x1": 141.489, "r_y1": 128.11699999999996, "r_x2": 141.489, "r_y2": 117.54899999999998, "r_x3": 134.765, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 128.11699999999996, "r_x1": 219.255, "r_y1": 128.11699999999996, "r_x2": 219.255, "r_y2": 117.54899999999998, "r_x3": 154.938, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "Conclusion", "orig": "Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86400000000003, "r": 480.596, "b": 203.48199999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 155.66099999999994, "r_x1": 480.595, "r_y1": 155.66099999999994, "r_x2": 480.595, "r_y2": 146.86400000000003, "r_x3": 134.765, "r_y3": 146.86400000000003, "coord_origin": "TOPLEFT"}, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.61599999999999, "r_x1": 152.499, "r_y1": 167.61599999999999, "r_x2": 152.499, "r_y2": 158.81899999999996, "r_x3": 134.765, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "ture", "orig": "ture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.963, "r_y0": 167.61599999999999, "r_x1": 205.131, "r_y1": 167.61599999999999, "r_x2": 205.131, "r_y2": 158.81899999999996, "r_x3": 156.963, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "recognition", "orig": "recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 209.594, "r_y0": 167.61599999999999, "r_x1": 480.595, "r_y1": 167.61599999999999, "r_x2": 480.595, "r_y2": 158.81899999999996, "r_x3": 209.594, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "with Im2Seq models is ill-suited and has serious limitations.", "orig": "with Im2Seq models is ill-suited and has serious limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly used general purpose languages,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.48199999999997, "r_x1": 239.539, "r_y1": 203.48199999999997, "r_x2": 239.539, "r_y2": 194.68499999999995, "r_x3": 134.765, "r_y3": 194.68499999999995, "coord_origin": "TOPLEFT"}, "text": "has several key benefits.", "orig": "has several key benefits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44399999999996, "r": 480.595, "b": 323.837, "coord_origin": "TOPLEFT"}, "confidence": 0.9873476028442383, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 216.24099999999999, "r_x1": 480.593, "r_y1": 216.24099999999999, "r_x2": 480.593, "r_y2": 207.44399999999996, "r_x3": 149.709, "r_y3": 207.44399999999996, "coord_origin": "TOPLEFT"}, "text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.799, "r": 480.595, "b": 468.103, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.821, "r": 197.686, "b": 504.389, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 520.602, "r": 480.592, "b": 563.8720000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"index": 39, 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Analysis and", "orig": "2019 International Conference on Document Analysis and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.518, "r_y0": 665.809, "r_x1": 350.111, "r_y1": 665.809, "r_x2": 350.111, "r_y2": 654.52, "r_x3": 151.518, "r_y3": 654.52, "coord_origin": "TOPLEFT"}, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "orig": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54899999999998, "r": 219.255, "b": 128.11699999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.11699999999996, "r_x1": 141.489, "r_y1": 128.11699999999996, "r_x2": 141.489, "r_y2": 117.54899999999998, "r_x3": 134.765, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 128.11699999999996, "r_x1": 219.255, "r_y1": 128.11699999999996, "r_x2": 219.255, "r_y2": 117.54899999999998, "r_x3": 154.938, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "Conclusion", "orig": "Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86400000000003, "r": 480.596, "b": 203.48199999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 155.66099999999994, "r_x1": 480.595, "r_y1": 155.66099999999994, "r_x2": 480.595, "r_y2": 146.86400000000003, "r_x3": 134.765, "r_y3": 146.86400000000003, "coord_origin": "TOPLEFT"}, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.61599999999999, "r_x1": 152.499, "r_y1": 167.61599999999999, "r_x2": 152.499, "r_y2": 158.81899999999996, "r_x3": 134.765, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "ture", "orig": "ture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.963, "r_y0": 167.61599999999999, "r_x1": 205.131, "r_y1": 167.61599999999999, "r_x2": 205.131, "r_y2": 158.81899999999996, "r_x3": 156.963, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "recognition", "orig": "recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 209.594, "r_y0": 167.61599999999999, "r_x1": 480.595, "r_y1": 167.61599999999999, "r_x2": 480.595, "r_y2": 158.81899999999996, "r_x3": 209.594, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "with Im2Seq models is ill-suited and has serious limitations.", "orig": "with Im2Seq models is ill-suited and has serious limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly used general purpose languages,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.48199999999997, "r_x1": 239.539, "r_y1": 203.48199999999997, "r_x2": 239.539, "r_y2": 194.68499999999995, "r_x3": 134.765, "r_y3": 194.68499999999995, "coord_origin": "TOPLEFT"}, "text": "has several key benefits.", "orig": "has several key benefits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44399999999996, "r": 480.595, "b": 323.837, "coord_origin": "TOPLEFT"}, "confidence": 0.9873476028442383, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 216.24099999999999, "r_x1": 480.593, "r_y1": 216.24099999999999, "r_x2": 480.593, "r_y2": 207.44399999999996, "r_x3": 149.709, "r_y3": 207.44399999999996, "coord_origin": "TOPLEFT"}, "text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.799, "r": 480.595, "b": 468.103, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.821, "r": 197.686, "b": 504.389, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 520.602, "r": 480.592, "b": 563.8720000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"index": 39, 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Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.371, "t": 565.241, "r": 480.592, "b": 609.407, "coord_origin": "TOPLEFT"}, "confidence": 0.9730570316314697, "cells": [{"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.371, "r_y0": 576.53, "r_x1": 146.537, "r_y1": 576.53, "r_x2": 146.537, "r_y2": 565.241, "r_x3": 139.371, "r_y3": 565.241, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.522, "r_y0": 576.53, "r_x1": 480.586, "r_y1": 576.53, "r_x2": 480.586, "r_y2": 565.241, "r_x3": 151.522, "r_y3": 565.241, "coord_origin": "TOPLEFT"}, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "orig": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.518, "r_y0": 587.489, "r_x1": 480.59, "r_y1": 587.489, "r_x2": 480.59, "r_y2": 576.2, "r_x3": 151.518, "r_y3": 576.2, "coord_origin": "TOPLEFT"}, "text": "in the wild using transformer and identity matrix-based augmentation. 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IEEE (2019)"}], "headers": [{"label": "page_header", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 143.979, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7012730240821838, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 143.979, "r_y1": 102.78200000000004, "r_x2": 143.979, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "14", "orig": "14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "14"}, {"label": "page_header", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 167.821, "t": 91.49300000000005, "r": 231.72, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7889755964279175, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.821, "r_y0": 102.78200000000004, "r_x1": 231.72, "r_y1": 102.78200000000004, "r_x2": 231.72, "r_y2": 91.49300000000005, "r_x3": 167.821, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/amt_handbook_sample.doctags.txt b/tests/data/groundtruth/docling_v1/amt_handbook_sample.doctags.txt index da6b7fb5..1099835e 100644 --- a/tests/data/groundtruth/docling_v1/amt_handbook_sample.doctags.txt +++ b/tests/data/groundtruth/docling_v1/amt_handbook_sample.doctags.txt @@ -1,22 +1,23 @@ -pulleys, provided the inner race of the bearing is clamped -The two general types of self-locking nuts currently in use +pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws. +The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type. Boots Self-Locking Nut -nut is of one piece, all-metal -The spring keeps the locking and load-carrying sections such -The spring, through the medium of the locking section, exerts -Boots self-locking nuts are made with three different spring +nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut. +The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly. +The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency. +Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is
-Figure 7-26. +Figure 7-26. Self-locking nuts.
-the most common ranges in size for No. 6 up to 1 4 inch, the +the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. +. Stainless Steel Self-Locking Nut -The stainless steel self-locking nut may be spun on and off +The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened. Elastic Stop Nut -The elastic stop nut is a standard nut with the height +The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This
-Figure 7-27. +Figure 7-27. Stainless steel self-locking nut.
\ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/amt_handbook_sample.json b/tests/data/groundtruth/docling_v1/amt_handbook_sample.json index 06cc0749..f5d39858 100644 --- a/tests/data/groundtruth/docling_v1/amt_handbook_sample.json +++ b/tests/data/groundtruth/docling_v1/amt_handbook_sample.json @@ -1 +1 @@ -{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "amt_handbook_sample.pdf", "filename-prov": null, "document-hash": "4ba7cdbd9ce8155d692d8f477f88bb3ec1acc2a463cf1e0209d1e624e58ebce9", "#-pages": 1, "collection-name": null, "description": null, "page-hashes": [{"hash": "f31706a847734c62e1e41f9f792c756283d1d4955552c1cc7f5e23c351bdd7cb", "model": "default", "page": 1}]}, "main-text": [{"prov": [{"bbox": [71.992, 719.501, 309.122, 727.966], "page": 1, "span": [0, 58], "__ref_s3_data": null}], "text": "pulleys, provided the inner race of the bearing is clamped", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 657.001, 309.033, 665.466], "page": 1, "span": [0, 59], "__ref_s3_data": null}], "text": "The two general types of self-locking nuts currently in use", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 569.5, 167.272, 577.876], "page": 1, "span": [0, 22], "__ref_s3_data": null}], "text": "Boots Self-Locking Nut", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [174.212, 555.001, 311.548, 563.466], "page": 1, "span": [0, 35], "__ref_s3_data": null}], "text": "nut is of one piece, all-metal", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 467.501, 309.03, 475.966], "page": 1, "span": [0, 60], "__ref_s3_data": null}], "text": "The spring keeps the locking and load-carrying sections such", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 380.001, 309.042, 388.466], "page": 1, "span": [0, 61], "__ref_s3_data": null}], "text": "The spring, through the medium of the locking section, exerts", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 267.501, 311.502, 275.966], "page": 1, "span": [0, 60], "__ref_s3_data": null}], "text": "Boots self-locking nuts are made with three different spring", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [320.992, 719.501, 560.508, 727.966], "page": 1, 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null}], "text": "The elastic stop nut is a standard nut with the height", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}], "figures": [{"prov": [{"bbox": [70.59269714355469, 79.6090087890625, 309.863037109375, 242.77777099609375], "page": 1, "span": [0, 12], "__ref_s3_data": null}], "text": "Figure 7-26.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [320.4467468261719, 81.689208984375, 558.8576049804688, 352.359375], "page": 1, "span": [0, 12], "__ref_s3_data": null}], "text": "Figure 7-27.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 774.0, "page": 1, "width": 594.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file +{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "amt_handbook_sample.pdf", "filename-prov": null, "document-hash": "4ba7cdbd9ce8155d692d8f477f88bb3ec1acc2a463cf1e0209d1e624e58ebce9", "#-pages": 1, "collection-name": null, "description": null, "page-hashes": [{"hash": "f31706a847734c62e1e41f9f792c756283d1d4955552c1cc7f5e23c351bdd7cb", "model": "default", "page": 1}]}, "main-text": [{"prov": [{"bbox": [71.992, 682.001, 311.412, 727.966], "page": 1, "span": [0, 244], "__ref_s3_data": null}], "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 594.501, 311.445, 665.466], "page": 1, "span": [0, 382], "__ref_s3_data": null}], "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 569.5, 167.272, 577.876], "page": 1, "span": [0, 22], "__ref_s3_data": null}], "text": "Boots Self-Locking Nut", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [71.992, 492.501, 311.548, 563.466], "page": 1, "span": [0, 326], "__ref_s3_data": null}], "text": "nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 405.001, 311.519, 475.966], "page": 1, "span": [0, 332], "__ref_s3_data": null}], "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 292.501, 311.492, 388.466], "page": 1, "span": [0, 477], "__ref_s3_data": null}], "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [71.992, 255.00099999999998, 311.502, 275.966], "page": 1, "span": [0, 122], "__ref_s3_data": null}], "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/0"}, {"prov": [{"bbox": [320.992, 657.001, 560.517, 727.966], "page": 1, "span": [0, 370], "__ref_s3_data": null}], "text": "the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.995, 644.501, 325.995, 652.966], "page": 1, "span": [0, 1], "__ref_s3_data": null}], "text": ".", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.995, 632.0, 450.995, 640.376], "page": 1, "span": [0, 32], "__ref_s3_data": null}], "text": "Stainless Steel Self-Locking Nut", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [320.995, 417.501, 560.513, 625.966], "page": 1, "span": [0, 1024], "__ref_s3_data": null}], "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [320.995, 392.5, 388.505, 400.876], "page": 1, "span": [0, 16], "__ref_s3_data": null}], "text": "Elastic Stop Nut", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [320.995, 365.501, 558.104, 386.466], "page": 1, "span": [0, 124], "__ref_s3_data": null}], "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/1"}], "figures": [{"prov": [{"bbox": [70.59269714355469, 79.6090087890625, 309.863037109375, 242.77777099609375], "page": 1, "span": [0, 31], "__ref_s3_data": null}], "text": "Figure 7-26. Self-locking nuts.", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [320.4467468261719, 81.689208984375, 558.8576049804688, 352.359375], "page": 1, "span": [0, 46], "__ref_s3_data": null}], "text": "Figure 7-27. Stainless steel self-locking nut.", "type": "figure", "payload": null, "bounding-box": null}], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 774.0, "page": 1, "width": 594.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/amt_handbook_sample.md b/tests/data/groundtruth/docling_v1/amt_handbook_sample.md index d099e2af..272be1db 100644 --- a/tests/data/groundtruth/docling_v1/amt_handbook_sample.md +++ b/tests/data/groundtruth/docling_v1/amt_handbook_sample.md @@ -1,29 +1,31 @@ -pulleys, provided the inner race of the bearing is clamped +pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws. -The two general types of self-locking nuts currently in use +The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type. ## Boots Self-Locking Nut -nut is of one piece, all-metal +nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut. -The spring keeps the locking and load-carrying sections such +The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly. -The spring, through the medium of the locking section, exerts +The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency. -Boots self-locking nuts are made with three different spring +Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is -Figure 7-26. +Figure 7-26. Self-locking nuts. -the most common ranges in size for No. 6 up to 1 4 inch, the +the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. + +. ## Stainless Steel Self-Locking Nut -The stainless steel self-locking nut may be spun on and off +The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened. ## Elastic Stop Nut -The elastic stop nut is a standard nut with the height +The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This -Figure 7-27. +Figure 7-27. Stainless steel self-locking nut. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/amt_handbook_sample.pages.json b/tests/data/groundtruth/docling_v1/amt_handbook_sample.pages.json index 441e7a19..bd74e40e 100644 --- a/tests/data/groundtruth/docling_v1/amt_handbook_sample.pages.json +++ b/tests/data/groundtruth/docling_v1/amt_handbook_sample.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 594.0, "height": 774.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 116.902, "r_y0": 711.999, "r_x1": 119.402, "r_y1": 711.999, "r_x2": 119.402, "r_y2": 703.534, "r_x3": 116.902, "r_y3": 703.534, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.902, "r_y0": 709.979, "r_x1": 368.402, "r_y1": 709.979, "r_x2": 368.402, "r_y2": 701.514, "r_x3": 365.902, "r_y3": 701.514, 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supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.136, "r_y0": 682.01, "r_x1": 531.167, "r_y1": 682.01, "r_x2": 531.167, "r_y2": 675.255, "r_x3": 479.136, "r_y3": 675.255, "coord_origin": "TOPLEFT"}, "text": "Tightened nut ", "orig": "Tightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.37, "r_y0": 541.167, "r_x1": 535.234, "r_y1": 541.167, "r_x2": 535.234, "r_y2": 534.412, "r_x3": 474.37, "r_y3": 534.412, "coord_origin": "TOPLEFT"}, "text": "Untightened nut ", "orig": "Untightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.756, "r_y0": 441.029, "r_x1": 487.084, "r_y1": 441.029, "r_x2": 487.084, "r_y2": 434.221, "r_x3": 456.756, "r_y3": 434.221, "coord_origin": "TOPLEFT"}, "text": "Nut case ", "orig": "Nut case ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 434.623, "r_y0": 586.855, "r_x1": 497.472, "r_y1": 586.855, "r_x2": 497.472, "r_y2": 580.047, "r_x3": 434.623, "r_y3": 580.047, "coord_origin": "TOPLEFT"}, "text": "Threaded nut core ", "orig": "Threaded nut core ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.551, "r_y0": 562.3530000000001, "r_x1": 507.686, "r_y1": 562.3530000000001, "r_x2": 507.686, "r_y2": 555.545, "r_x3": 448.551, "r_y3": 555.545, "coord_origin": "TOPLEFT"}, "text": "Locking shoulder ", "orig": "Locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 424.784, "r_y0": 673.144, "r_x1": 452.103, "r_y1": 673.144, "r_x2": 452.103, "r_y2": 666.336, "r_x3": 424.784, "r_y3": 666.336, "coord_origin": "TOPLEFT"}, "text": "Keyway ", "orig": "Keyway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.416, "r_y0": 596.797, "r_x1": 161.319, "r_y1": 596.797, "r_x2": 161.319, "r_y2": 589.989, "r_x3": 102.416, "r_y3": 589.989, "coord_origin": "TOPLEFT"}, "text": "Boots aircraft nut ", "orig": "Boots aircraft nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.686, "r_y0": 688.342, "r_x1": 129.774, "r_y1": 688.342, "r_x2": 129.774, "r_y2": 681.534, "r_x3": 91.686, "r_y3": 681.534, "coord_origin": "TOPLEFT"}, "text": "Flexloc nut ", "orig": "Flexloc nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 162.481, "r_y0": 688.342, "r_x1": 207.856, "r_y1": 688.342, "r_x2": 207.856, "r_y2": 681.534, "r_x3": 162.481, "r_y3": 681.534, "coord_origin": "TOPLEFT"}, "text": "Fiber locknut ", "orig": "Fiber locknut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.314, "r_y0": 688.342, "r_x1": 289.561, "r_y1": 688.342, "r_x2": 289.561, "r_y2": 681.534, "r_x3": 237.314, "r_y3": 681.534, "coord_origin": "TOPLEFT"}, "text": "Elastic stop nut ", "orig": "Elastic stop nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.933, "r_y0": 596.797, "r_x1": 277.797, "r_y1": 596.797, "r_x2": 277.797, "r_y2": 589.989, "r_x3": 216.933, "r_y3": 589.989, "coord_origin": "TOPLEFT"}, "text": "Elastic anchor nut ", "orig": "Elastic anchor nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 72.0, "r_y0": 712.285, "r_x1": 119.128, "r_y1": 712.285, "r_x2": 119.128, "r_y2": 704.41, "r_x3": 72.0, "r_y3": 704.41, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26. ", "orig": "Figure 7-26. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.402, "r_y0": 711.816, "r_x1": 184.148, "r_y1": 711.816, "r_x2": 184.148, "r_y2": 704.277, "r_x3": 119.402, "r_y3": 704.277, "coord_origin": "TOPLEFT"}, "text": "Self-locking nuts. ", "orig": "Self-locking nuts. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.0, "r_y0": 710.265, "r_x1": 368.128, "r_y1": 710.265, "r_x2": 368.128, "r_y2": 702.39, "r_x3": 321.0, "r_y3": 702.39, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27. ", "orig": "Figure 7-27. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.402, "r_y0": 709.796, "r_x1": 481.649, "r_y1": 709.796, "r_x2": 481.649, "r_y2": 702.2570000000001, "r_x3": 368.402, "r_y3": 702.2570000000001, "coord_origin": "TOPLEFT"}, "text": "Stainless steel self-locking nut. ", "orig": "Stainless steel self-locking nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.985, "r_y0": 739.604, "r_x1": 560.775, "r_y1": 739.604, "r_x2": 560.775, "r_y2": 730.1, "r_x3": 537.985, "r_y3": 730.1, "coord_origin": "TOPLEFT"}, "text": "7-45 ", "orig": "7-45 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "text", "bbox": {"l": 71.992, "t": 46.03399999999999, "r": 309.122, "b": 54.499000000000024, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", 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"rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 558.072, "r_y0": 408.499, "r_x1": 560.572, "r_y1": 408.499, "r_x2": 560.572, "r_y2": 400.034, "r_x3": 558.072, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 66.99900000000002, "r_x1": 309.002, "r_y1": 66.99900000000002, "r_x2": 309.002, "r_y2": 58.53399999999999, "r_x3": 71.992, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "to the supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.136, "r_y0": 682.01, "r_x1": 531.167, "r_y1": 682.01, "r_x2": 531.167, "r_y2": 675.255, "r_x3": 479.136, "r_y3": 675.255, "coord_origin": "TOPLEFT"}, "text": "Tightened nut ", "orig": "Tightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.37, "r_y0": 541.167, "r_x1": 535.234, "r_y1": 541.167, "r_x2": 535.234, "r_y2": 534.412, "r_x3": 474.37, "r_y3": 534.412, "coord_origin": "TOPLEFT"}, "text": "Untightened nut ", "orig": "Untightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.756, "r_y0": 441.029, "r_x1": 487.084, "r_y1": 441.029, "r_x2": 487.084, "r_y2": 434.221, "r_x3": 456.756, "r_y3": 434.221, "coord_origin": "TOPLEFT"}, "text": "Nut case ", "orig": "Nut case ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 434.623, "r_y0": 586.855, "r_x1": 497.472, "r_y1": 586.855, "r_x2": 497.472, "r_y2": 580.047, "r_x3": 434.623, "r_y3": 580.047, "coord_origin": "TOPLEFT"}, "text": "Threaded nut core ", "orig": "Threaded nut core ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.551, "r_y0": 562.3530000000001, "r_x1": 507.686, "r_y1": 562.3530000000001, "r_x2": 507.686, "r_y2": 555.545, "r_x3": 448.551, "r_y3": 555.545, "coord_origin": "TOPLEFT"}, "text": "Locking shoulder ", "orig": "Locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 424.784, "r_y0": 673.144, "r_x1": 452.103, "r_y1": 673.144, "r_x2": 452.103, "r_y2": 666.336, "r_x3": 424.784, "r_y3": 666.336, "coord_origin": "TOPLEFT"}, "text": "Keyway ", "orig": "Keyway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.416, "r_y0": 596.797, "r_x1": 161.319, "r_y1": 596.797, "r_x2": 161.319, "r_y2": 589.989, "r_x3": 102.416, "r_y3": 589.989, "coord_origin": "TOPLEFT"}, "text": "Boots aircraft nut ", "orig": "Boots aircraft nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.686, "r_y0": 688.342, "r_x1": 129.774, "r_y1": 688.342, "r_x2": 129.774, "r_y2": 681.534, "r_x3": 91.686, "r_y3": 681.534, "coord_origin": "TOPLEFT"}, "text": "Flexloc nut ", "orig": "Flexloc nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 162.481, "r_y0": 688.342, "r_x1": 207.856, "r_y1": 688.342, "r_x2": 207.856, "r_y2": 681.534, "r_x3": 162.481, "r_y3": 681.534, "coord_origin": "TOPLEFT"}, "text": "Fiber locknut ", "orig": "Fiber locknut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.314, "r_y0": 688.342, "r_x1": 289.561, "r_y1": 688.342, "r_x2": 289.561, "r_y2": 681.534, "r_x3": 237.314, "r_y3": 681.534, "coord_origin": "TOPLEFT"}, "text": "Elastic stop nut ", "orig": "Elastic stop nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.933, "r_y0": 596.797, "r_x1": 277.797, "r_y1": 596.797, "r_x2": 277.797, "r_y2": 589.989, "r_x3": 216.933, "r_y3": 589.989, "coord_origin": "TOPLEFT"}, "text": "Elastic anchor nut ", "orig": "Elastic anchor nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 72.0, "r_y0": 712.285, "r_x1": 119.128, "r_y1": 712.285, "r_x2": 119.128, "r_y2": 704.41, "r_x3": 72.0, "r_y3": 704.41, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26. ", "orig": "Figure 7-26. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.402, "r_y0": 711.816, "r_x1": 184.148, "r_y1": 711.816, "r_x2": 184.148, "r_y2": 704.277, "r_x3": 119.402, "r_y3": 704.277, "coord_origin": "TOPLEFT"}, "text": "Self-locking nuts. ", "orig": "Self-locking nuts. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.0, "r_y0": 710.265, "r_x1": 368.128, "r_y1": 710.265, "r_x2": 368.128, "r_y2": 702.39, "r_x3": 321.0, "r_y3": 702.39, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27. ", "orig": "Figure 7-27. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.402, "r_y0": 709.796, "r_x1": 481.649, "r_y1": 709.796, "r_x2": 481.649, "r_y2": 702.2570000000001, "r_x3": 368.402, "r_y3": 702.2570000000001, "coord_origin": "TOPLEFT"}, "text": "Stainless steel self-locking nut. ", "orig": "Stainless steel self-locking nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.985, "r_y0": 739.604, "r_x1": 560.775, "r_y1": 739.604, "r_x2": 560.775, "r_y2": 730.1, "r_x3": 537.985, "r_y3": 730.1, "coord_origin": "TOPLEFT"}, "text": "7-45 ", "orig": "7-45 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 6, "label": "text", "bbox": {"l": 71.992, "t": 210.534, "r": 311.548, "b": 281.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.212, "r_y0": 218.99900000000002, "r_x1": 311.548, "r_y1": 218.99900000000002, "r_x2": 311.548, "r_y2": 210.534, "r_x3": 174.212, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": " nut is of one piece, all-metal ", "orig": " nut is of one piece, all-metal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 71.992, "t": 46.03399999999999, "r": 311.412, "b": 91.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 66.99900000000002, "r_x1": 309.002, "r_y1": 66.99900000000002, "r_x2": 309.002, "r_y2": 58.53399999999999, "r_x3": 71.992, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "to the supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 71.992, "t": 108.53399999999999, "r": 311.445, "b": 179.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 71.992, "t": 196.12400000000002, "r": 167.272, "b": 204.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 71.992, "t": 298.034, "r": 311.519, "b": 368.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 71.992, "t": 385.534, "r": 311.492, "b": 481.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 71.992, "t": 498.034, "r": 311.502, "b": 518.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 320.992, "t": 46.03399999999999, "r": 560.517, "b": 116.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 320.995, "t": 121.03399999999999, "r": 325.995, "b": 129.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 320.995, "t": 133.62400000000002, "r": 450.995, "b": 142.0, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 320.995, "t": 148.034, "r": 560.513, "b": 356.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 320.995, "t": 373.124, "r": 388.505, "b": 381.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 320.995, "t": 387.534, "r": 558.104, "b": 408.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.136, "r_y0": 682.01, "r_x1": 531.167, "r_y1": 682.01, "r_x2": 531.167, "r_y2": 675.255, "r_x3": 479.136, "r_y3": 675.255, "coord_origin": "TOPLEFT"}, "text": "Tightened nut ", "orig": "Tightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.37, "r_y0": 541.167, "r_x1": 535.234, "r_y1": 541.167, "r_x2": 535.234, "r_y2": 534.412, "r_x3": 474.37, "r_y3": 534.412, "coord_origin": "TOPLEFT"}, "text": "Untightened nut ", "orig": "Untightened nut ", "text_direction": "left_to_right", "confidence": 1.0, 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", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.402, "r_y0": 709.796, "r_x1": 481.649, "r_y1": 709.796, "r_x2": 481.649, "r_y2": 702.2570000000001, "r_x3": 368.402, "r_y3": 702.2570000000001, "coord_origin": "TOPLEFT"}, "text": "Stainless steel self-locking nut. ", "orig": "Stainless steel self-locking nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 537.985, "t": 730.1, "r": 560.775, "b": 739.604, "coord_origin": "TOPLEFT"}, "confidence": 0.9368568658828735, "cells": [{"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.985, "r_y0": 739.604, "r_x1": 560.775, "r_y1": 739.604, "r_x2": 560.775, "r_y2": 730.1, "r_x3": 537.985, "r_y3": 730.1, "coord_origin": "TOPLEFT"}, "text": "7-45 ", "orig": "7-45 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "text", "bbox": {"l": 71.992, "t": 210.534, "r": 311.548, "b": 281.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.212, "r_y0": 218.99900000000002, "r_x1": 311.548, "r_y1": 218.99900000000002, "r_x2": 311.548, "r_y2": 210.534, "r_x3": 174.212, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": " nut is of one piece, all-metal ", "orig": " nut is of one piece, all-metal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"label": "text", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "text", "bbox": {"l": 71.992, "t": 46.03399999999999, "r": 311.412, "b": 91.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 66.99900000000002, "r_x1": 309.002, "r_y1": 66.99900000000002, "r_x2": 309.002, "r_y2": 58.53399999999999, "r_x3": 71.992, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "to the supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 71.992, "t": 108.53399999999999, "r": 311.445, "b": 179.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 71.992, "t": 196.12400000000002, "r": 167.272, "b": 204.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Boots Self-Locking Nut"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 71.992, "t": 298.034, "r": 311.519, "b": 368.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 71.992, "t": 385.534, "r": 311.492, "b": 481.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"label": "text", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "text", "bbox": {"l": 71.992, "t": 498.034, "r": 311.502, "b": 518.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"label": "text", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "text", "bbox": {"l": 320.992, "t": 46.03399999999999, "r": 560.517, "b": 116.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"label": "text", "id": 19, "page_no": 0, "cluster": {"id": 19, "label": "text", "bbox": {"l": 320.995, "t": 121.03399999999999, "r": 325.995, "b": 129.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "."}, {"label": "section_header", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 320.995, "t": 133.62400000000002, "r": 450.995, "b": 142.0, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Stainless Steel Self-Locking Nut"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.995, "t": 148.034, "r": 560.513, "b": 356.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"label": "section_header", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 320.995, "t": 373.124, "r": 388.505, "b": 381.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Elastic Stop Nut"}, {"label": "text", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "text", "bbox": {"l": 320.995, "t": 387.534, "r": 558.104, "b": 408.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.136, "r_y0": 682.01, "r_x1": 531.167, "r_y1": 682.01, "r_x2": 531.167, "r_y2": 675.255, "r_x3": 479.136, "r_y3": 675.255, "coord_origin": "TOPLEFT"}, "text": "Tightened nut ", "orig": "Tightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.37, "r_y0": 541.167, "r_x1": 535.234, "r_y1": 541.167, "r_x2": 535.234, "r_y2": 534.412, "r_x3": 474.37, "r_y3": 534.412, "coord_origin": "TOPLEFT"}, "text": "Untightened nut ", "orig": "Untightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": 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", "orig": "Figure 7-26. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.402, "r_y0": 711.816, "r_x1": 184.148, "r_y1": 711.816, "r_x2": 184.148, "r_y2": 704.277, "r_x3": 119.402, "r_y3": 704.277, "coord_origin": "TOPLEFT"}, "text": "Self-locking nuts. ", "orig": "Self-locking nuts. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7-26. Self-locking nuts."}, {"label": "caption", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 321.0, "t": 702.2570000000001, "r": 481.649, "b": 710.265, "coord_origin": "TOPLEFT"}, "confidence": 0.9497622847557068, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.0, "r_y0": 710.265, "r_x1": 368.128, "r_y1": 710.265, "r_x2": 368.128, "r_y2": 702.39, "r_x3": 321.0, "r_y3": 702.39, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27. ", "orig": "Figure 7-27. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.402, "r_y0": 709.796, "r_x1": 481.649, "r_y1": 709.796, "r_x2": 481.649, "r_y2": 702.2570000000001, "r_x3": 368.402, "r_y3": 702.2570000000001, "coord_origin": "TOPLEFT"}, "text": "Stainless steel self-locking nut. ", "orig": "Stainless steel self-locking nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7-27. Stainless steel self-locking nut."}, {"label": "page_footer", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 537.985, "t": 730.1, "r": 560.775, "b": 739.604, "coord_origin": "TOPLEFT"}, "confidence": 0.9368568658828735, "cells": [{"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.985, "r_y0": 739.604, "r_x1": 560.775, "r_y1": 739.604, "r_x2": 560.775, "r_y2": 730.1, "r_x3": 537.985, "r_y3": 730.1, "coord_origin": "TOPLEFT"}, "text": "7-45 ", "orig": "7-45 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7-45"}], "body": [{"label": "text", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "text", "bbox": {"l": 71.992, "t": 210.534, "r": 311.548, "b": 281.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.212, "r_y0": 218.99900000000002, "r_x1": 311.548, "r_y1": 218.99900000000002, "r_x2": 311.548, "r_y2": 210.534, "r_x3": 174.212, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": " nut is of one piece, all-metal ", "orig": " nut is of one piece, all-metal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"label": "text", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "text", "bbox": {"l": 71.992, "t": 46.03399999999999, "r": 311.412, "b": 91.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 66.99900000000002, "r_x1": 309.002, "r_y1": 66.99900000000002, "r_x2": 309.002, "r_y2": 58.53399999999999, "r_x3": 71.992, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "to the supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 71.992, "t": 108.53399999999999, "r": 311.445, "b": 179.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 71.992, "t": 196.12400000000002, "r": 167.272, "b": 204.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Boots Self-Locking Nut"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 71.992, "t": 298.034, "r": 311.519, "b": 368.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 71.992, "t": 385.534, "r": 311.492, "b": 481.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"label": "text", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "text", "bbox": {"l": 71.992, "t": 498.034, "r": 311.502, "b": 518.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"label": "text", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "text", "bbox": {"l": 320.992, "t": 46.03399999999999, "r": 560.517, "b": 116.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"label": "text", "id": 19, "page_no": 0, "cluster": {"id": 19, "label": "text", "bbox": {"l": 320.995, "t": 121.03399999999999, "r": 325.995, "b": 129.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "."}, {"label": "section_header", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 320.995, "t": 133.62400000000002, "r": 450.995, "b": 142.0, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Stainless Steel Self-Locking Nut"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.995, "t": 148.034, "r": 560.513, "b": 356.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"label": "section_header", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 320.995, "t": 373.124, "r": 388.505, "b": 381.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Elastic Stop Nut"}, {"label": "text", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "text", "bbox": {"l": 320.995, "t": 387.534, "r": 558.104, "b": 408.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.136, "r_y0": 682.01, "r_x1": 531.167, "r_y1": 682.01, "r_x2": 531.167, "r_y2": 675.255, "r_x3": 479.136, "r_y3": 675.255, "coord_origin": "TOPLEFT"}, "text": "Tightened nut ", "orig": "Tightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.37, "r_y0": 541.167, "r_x1": 535.234, "r_y1": 541.167, "r_x2": 535.234, "r_y2": 534.412, "r_x3": 474.37, "r_y3": 534.412, "coord_origin": "TOPLEFT"}, "text": "Untightened nut ", "orig": "Untightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, +function add(a, b) { return a + b; } console.log(add(3, 5)); +Listing 1: Simple JavaScript Program +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, Formula -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir- -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. 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Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 792.0, "page": 1, "width": 612.0}, {"height": 841.89, "page": 2, "width": 595.276}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/code_and_formula.md b/tests/data/groundtruth/docling_v1/code_and_formula.md index d0708a9b..9a5dcee5 100644 --- a/tests/data/groundtruth/docling_v1/code_and_formula.md +++ b/tests/data/groundtruth/docling_v1/code_and_formula.md @@ -1,23 +1,23 @@ ## JavaScript Code Example -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir- +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, -function +function add(a, b) { return a + b; } console.log(add(3, 5)); -Listing 1: +Listing 1: Simple JavaScript Program -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, ## Formula -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir- +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie \ No newline at end of file +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.608, "r_x1": 477.482, "r_y1": 499.608, "r_x2": 477.482, "r_y2": 490.902, "r_x3": 133.768, "r_y3": 490.902, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.608, "r_x1": 477.482, "r_y1": 499.608, "r_x2": 477.482, "r_y2": 490.902, "r_x3": 133.768, "r_y3": 490.902, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.608, "r_x1": 477.482, "r_y1": 499.608, "r_x2": 477.482, "r_y2": 490.902, "r_x3": 133.768, "r_y3": 490.902, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 511.564, "r_x1": 387.354, "r_y1": 511.564, "r_x2": 387.354, "r_y2": 502.858, "r_x3": 133.768, "r_y3": 502.858, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 511.564, "r_x1": 477.478, "r_y1": 511.564, "r_x2": 477.478, "r_y2": 502.858, "r_x3": 393.571, "r_y3": 502.858, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 523.519, "r_x1": 477.48, "r_y1": 523.519, "r_x2": 477.48, "r_y2": 514.813, "r_x3": 133.768, "r_y3": 514.813, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 535.4739999999999, "r_x1": 407.17, "r_y1": 535.4739999999999, "r_x2": 407.17, "r_y2": 526.768, "r_x3": 133.768, "r_y3": 526.768, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 535.4739999999999, "r_x1": 477.475, "r_y1": 535.4739999999999, "r_x2": 477.475, "r_y2": 526.768, "r_x3": 414.054, "r_y3": 526.768, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 547.429, "r_x1": 323.816, "r_y1": 547.429, "r_x2": 323.816, "r_y2": 538.723, "r_x3": 133.768, "r_y3": 538.723, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 547.429, "r_x1": 371.404, "r_y1": 547.429, "r_x2": 371.404, "r_y2": 538.723, "r_x3": 330.72, "r_y3": 538.723, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 547.429, "r_x1": 477.476, "r_y1": 547.429, "r_x2": 477.476, "r_y2": 538.723, "r_x3": 375.548, "r_y3": 538.723, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 559.384, "r_x1": 351.485, "r_y1": 559.384, "r_x2": 351.485, "r_y2": 550.678, "r_x3": 133.768, "r_y3": 550.678, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.608, "r_x1": 477.482, "r_y1": 499.608, "r_x2": 477.482, "r_y2": 490.902, "r_x3": 133.768, "r_y3": 490.902, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 242.17399999999998, "r_x1": 477.478, "r_y1": 242.17399999999998, "r_x2": 477.478, "r_y2": 233.46799999999996, "r_x3": 393.571, "r_y3": 233.46799999999996, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 254.12900000000002, "r_x1": 477.48, "r_y1": 254.12900000000002, "r_x2": 477.48, "r_y2": 245.423, "r_x3": 133.768, "r_y3": 245.423, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 266.08399999999995, "r_x1": 407.17, "r_y1": 266.08399999999995, "r_x2": 407.17, "r_y2": 257.37800000000004, "r_x3": 133.768, "r_y3": 257.37800000000004, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 266.08399999999995, "r_x1": 477.475, "r_y1": 266.08399999999995, "r_x2": 477.475, "r_y2": 257.37800000000004, "r_x3": 414.054, "r_y3": 257.37800000000004, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 278.03999999999996, "r_x1": 323.816, "r_y1": 278.03999999999996, "r_x2": 323.816, "r_y2": 269.33399999999995, "r_x3": 133.768, "r_y3": 269.33399999999995, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 278.03999999999996, "r_x1": 371.404, "r_y1": 278.03999999999996, "r_x2": 371.404, "r_y2": 269.33399999999995, "r_x3": 330.72, "r_y3": 269.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 278.03999999999996, "r_x1": 477.476, "r_y1": 278.03999999999996, "r_x2": 477.476, "r_y2": 269.33399999999995, "r_x3": 375.548, "r_y3": 269.33399999999995, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 289.995, "r_x1": 351.485, "r_y1": 289.995, "r_x2": 351.485, "r_y2": 281.289, "r_x3": 133.768, "r_y3": 281.289, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.768, "t": 293.244, "r": 477.482, "b": 349.77099999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9853153228759766, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 301.94999999999993, "r_x1": 477.475, "r_y1": 301.94999999999993, "r_x2": 477.475, "r_y2": 293.244, "r_x3": 148.712, "r_y3": 293.244, "coord_origin": "TOPLEFT"}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 313.905, "r_x1": 477.482, "r_y1": 313.905, "r_x2": 477.482, "r_y2": 305.19899999999996, "r_x3": 133.768, "r_y3": 305.19899999999996, "coord_origin": "TOPLEFT"}, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "orig": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 325.86, "r_x1": 477.479, "r_y1": 325.86, "r_x2": 477.479, "r_y2": 317.154, "r_x3": 133.768, "r_y3": 317.154, "coord_origin": "TOPLEFT"}, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "orig": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 337.815, "r_x1": 230.812, "r_y1": 337.815, "r_x2": 230.812, "r_y2": 329.10900000000004, "r_x3": 133.768, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "te feugait nulla facilisi.", "orig": "te feugait nulla facilisi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.976, "r_y0": 337.815, "r_x1": 477.481, "r_y1": 337.815, "r_x2": 477.481, "r_y2": 329.10900000000004, "r_x3": 234.976, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "orig": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 349.77099999999996, "r_x1": 326.216, "r_y1": 349.77099999999996, "r_x2": 326.216, "r_y2": 341.065, "r_x3": 133.768, "r_y3": 341.065, "coord_origin": "TOPLEFT"}, "text": "sed diam nonummy nibh euismod tincidunt.", "orig": "sed diam nonummy nibh euismod tincidunt.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.554, "t": 362.902, "r": 330.697, "b": 373.681, "coord_origin": "TOPLEFT"}, "confidence": 0.8727474808692932, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.554, "r_y0": 373.681, "r_x1": 285.82, "r_y1": 373.681, "r_x2": 285.82, "r_y2": 364.97499999999997, "r_x3": 280.554, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 285.82, "r_y0": 368.996, "r_x1": 289.792, "r_y1": 368.996, "r_x2": 289.792, "r_y2": 362.902, "r_x3": 285.82, "r_y3": 362.902, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.504, "r_y0": 373.681, "r_x1": 330.697, "r_y1": 373.681, "r_x2": 330.697, "r_y2": 364.97499999999997, "r_x3": 292.504, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "+8 = 12", "orig": "+8 = 12", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a 2 +8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 382.90799999999996, "r": 477.482, "b": 523.121, "coord_origin": "TOPLEFT"}, "confidence": 0.9879696369171143, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 391.614, "r_x1": 477.478, "r_y1": 391.614, "r_x2": 477.478, "r_y2": 382.90799999999996, "r_x3": 148.712, "r_y3": 382.90799999999996, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 403.56899999999996, "r_x1": 432.467, "r_y1": 403.56899999999996, "r_x2": 432.467, "r_y2": 394.863, "r_x3": 133.768, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 403.56899999999996, "r_x1": 477.479, "r_y1": 403.56899999999996, "r_x2": 477.479, "r_y2": 394.863, "r_x3": 437.398, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 415.524, "r_x1": 174.725, "r_y1": 415.524, "r_x2": 174.725, "r_y2": 406.818, "r_x3": 133.768, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 415.524, "r_x1": 431.98, "r_y1": 415.524, "r_x2": 431.98, "r_y2": 406.818, "r_x3": 180.075, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 415.524, "r_x1": 477.476, "r_y1": 415.524, "r_x2": 477.476, "r_y2": 406.818, "r_x3": 437.319, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 427.479, "r_x1": 477.476, "r_y1": 427.479, "r_x2": 477.476, "r_y2": 418.77299999999997, "r_x3": 133.768, "r_y3": 418.77299999999997, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 475.3, "r_x1": 387.354, "r_y1": 475.3, "r_x2": 387.354, "r_y2": 466.594, "r_x3": 133.768, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 475.3, "r_x1": 477.478, "r_y1": 475.3, "r_x2": 477.478, "r_y2": 466.594, "r_x3": 393.571, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 487.255, "r_x1": 477.48, "r_y1": 487.255, "r_x2": 477.48, "r_y2": 478.549, "r_x3": 133.768, "r_y3": 478.549, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.21, "r_x1": 407.16, "r_y1": 499.21, "r_x2": 407.16, "r_y2": 490.50399999999996, "r_x3": 133.768, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 499.21, "r_x1": 477.475, "r_y1": 499.21, "r_x2": 477.475, "r_y2": 490.50399999999996, "r_x3": 414.054, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 511.16499999999996, "r_x1": 323.816, "r_y1": 511.16499999999996, "r_x2": 323.816, "r_y2": 502.459, "r_x3": 133.768, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 511.16499999999996, "r_x1": 371.404, "r_y1": 511.16499999999996, "r_x2": 371.404, "r_y2": 502.459, "r_x3": 330.72, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 511.16499999999996, "r_x1": 477.476, "r_y1": 511.16499999999996, "r_x2": 477.476, "r_y2": 502.459, "r_x3": 375.548, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 523.121, "r_x1": 351.485, "r_y1": 523.121, "r_x2": 351.485, "r_y2": 514.415, "r_x3": 133.768, "r_y3": 514.415, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.768, "t": 293.244, "r": 477.482, "b": 349.77099999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9853153228759766, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 301.94999999999993, "r_x1": 477.475, "r_y1": 301.94999999999993, "r_x2": 477.475, "r_y2": 293.244, "r_x3": 148.712, "r_y3": 293.244, "coord_origin": "TOPLEFT"}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 313.905, "r_x1": 477.482, "r_y1": 313.905, "r_x2": 477.482, "r_y2": 305.19899999999996, "r_x3": 133.768, "r_y3": 305.19899999999996, "coord_origin": "TOPLEFT"}, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "orig": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 325.86, "r_x1": 477.479, "r_y1": 325.86, "r_x2": 477.479, "r_y2": 317.154, "r_x3": 133.768, "r_y3": 317.154, "coord_origin": "TOPLEFT"}, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "orig": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 337.815, "r_x1": 230.812, "r_y1": 337.815, "r_x2": 230.812, "r_y2": 329.10900000000004, "r_x3": 133.768, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "te feugait nulla facilisi.", "orig": "te feugait nulla facilisi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.976, "r_y0": 337.815, "r_x1": 477.481, "r_y1": 337.815, "r_x2": 477.481, "r_y2": 329.10900000000004, "r_x3": 234.976, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "orig": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 349.77099999999996, "r_x1": 326.216, "r_y1": 349.77099999999996, "r_x2": 326.216, "r_y2": 341.065, "r_x3": 133.768, "r_y3": 341.065, "coord_origin": "TOPLEFT"}, "text": "sed diam nonummy nibh euismod tincidunt.", "orig": "sed diam nonummy nibh euismod tincidunt.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.554, "t": 362.902, "r": 330.697, "b": 373.681, "coord_origin": "TOPLEFT"}, "confidence": 0.8727474808692932, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.554, "r_y0": 373.681, "r_x1": 285.82, "r_y1": 373.681, "r_x2": 285.82, "r_y2": 364.97499999999997, "r_x3": 280.554, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 285.82, "r_y0": 368.996, "r_x1": 289.792, "r_y1": 368.996, "r_x2": 289.792, "r_y2": 362.902, "r_x3": 285.82, "r_y3": 362.902, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.504, "r_y0": 373.681, "r_x1": 330.697, "r_y1": 373.681, "r_x2": 330.697, "r_y2": 364.97499999999997, "r_x3": 292.504, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "+8 = 12", "orig": "+8 = 12", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a 2 +8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 382.90799999999996, "r": 477.482, "b": 523.121, "coord_origin": "TOPLEFT"}, "confidence": 0.9879696369171143, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 391.614, "r_x1": 477.478, "r_y1": 391.614, "r_x2": 477.478, "r_y2": 382.90799999999996, "r_x3": 148.712, "r_y3": 382.90799999999996, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 403.56899999999996, "r_x1": 432.467, "r_y1": 403.56899999999996, "r_x2": 432.467, "r_y2": 394.863, "r_x3": 133.768, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 403.56899999999996, "r_x1": 477.479, "r_y1": 403.56899999999996, "r_x2": 477.479, "r_y2": 394.863, "r_x3": 437.398, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 415.524, "r_x1": 174.725, "r_y1": 415.524, "r_x2": 174.725, "r_y2": 406.818, "r_x3": 133.768, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 415.524, "r_x1": 431.98, "r_y1": 415.524, "r_x2": 431.98, "r_y2": 406.818, "r_x3": 180.075, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 415.524, "r_x1": 477.476, "r_y1": 415.524, "r_x2": 477.476, "r_y2": 406.818, "r_x3": 437.319, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 427.479, "r_x1": 477.476, "r_y1": 427.479, "r_x2": 477.476, "r_y2": 418.77299999999997, "r_x3": 133.768, "r_y3": 418.77299999999997, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 439.43399999999997, "r_x1": 186.966, "r_y1": 439.43399999999997, "r_x2": 186.966, "r_y2": 430.728, "r_x3": 133.768, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "ipsum dolor", "orig": "ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.938, "r_y0": 439.43399999999997, "r_x1": 202.509, "r_y1": 439.43399999999997, "r_x2": 202.509, "r_y2": 430.728, "r_x3": 191.938, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sit", "orig": "sit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 207.47, "r_y0": 439.43399999999997, "r_x1": 231.823, "r_y1": 439.43399999999997, "r_x2": 231.823, "r_y2": 430.728, "r_x3": 207.47, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "amet,", "orig": "amet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.203, "r_y0": 439.43399999999997, "r_x1": 282.117, "r_y1": 439.43399999999997, "r_x2": 282.117, "r_y2": 430.728, "r_x3": 237.203, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "consetetur", "orig": "consetetur", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 287.078, "r_y0": 439.43399999999997, "r_x1": 331.468, "r_y1": 439.43399999999997, "r_x2": 331.468, "r_y2": 430.728, "r_x3": 287.078, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sadipscing", "orig": "sadipscing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.439, "r_y0": 439.43399999999997, "r_x1": 356.946, "r_y1": 439.43399999999997, "r_x2": 356.946, "r_y2": 430.728, "r_x3": 336.439, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "elitr,", "orig": "elitr,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 362.316, "r_y0": 439.43399999999997, "r_x1": 376.208, "r_y1": 439.43399999999997, "r_x2": 376.208, "r_y2": 430.728, "r_x3": 362.316, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sed", "orig": "sed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.179, "r_y0": 439.43399999999997, "r_x1": 477.482, "r_y1": 439.43399999999997, "r_x2": 477.482, "r_y2": 430.728, "r_x3": 381.179, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "diam nonumy eirmod", "orig": "diam nonumy eirmod", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 451.39, "r_x1": 477.481, "r_y1": 451.39, "r_x2": 477.481, "r_y2": 442.68399999999997, "r_x3": 133.768, "r_y3": 442.68399999999997, "coord_origin": "TOPLEFT"}, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 475.3, "r_x1": 387.354, "r_y1": 475.3, "r_x2": 387.354, "r_y2": 466.594, "r_x3": 133.768, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 475.3, "r_x1": 477.478, "r_y1": 475.3, "r_x2": 477.478, "r_y2": 466.594, "r_x3": 393.571, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 487.255, "r_x1": 477.48, "r_y1": 487.255, "r_x2": 477.48, "r_y2": 478.549, "r_x3": 133.768, "r_y3": 478.549, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.21, "r_x1": 407.16, "r_y1": 499.21, "r_x2": 407.16, "r_y2": 490.50399999999996, "r_x3": 133.768, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 499.21, "r_x1": 477.475, "r_y1": 499.21, "r_x2": 477.475, "r_y2": 490.50399999999996, "r_x3": 414.054, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 511.16499999999996, "r_x1": 323.816, "r_y1": 511.16499999999996, "r_x2": 323.816, "r_y2": 502.459, "r_x3": 133.768, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 511.16499999999996, "r_x1": 371.404, "r_y1": 511.16499999999996, "r_x2": 371.404, "r_y2": 502.459, "r_x3": 330.72, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 511.16499999999996, "r_x1": 477.476, "r_y1": 511.16499999999996, "r_x2": 477.476, "r_y2": 502.459, "r_x3": 375.548, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 523.121, "r_x1": 351.485, "r_y1": 523.121, "r_x2": 351.485, "r_y2": 514.415, "r_x3": 133.768, "r_y3": 514.415, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.768, "t": 526.37, "r": 477.484, "b": 594.852, "coord_origin": "TOPLEFT"}, "confidence": 0.9845718145370483, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 535.076, "r_x1": 477.475, "r_y1": 535.076, "r_x2": 477.475, "r_y2": 526.37, "r_x3": 148.712, "r_y3": 526.37, "coord_origin": "TOPLEFT"}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 547.031, "r_x1": 477.482, "r_y1": 547.031, "r_x2": 477.482, "r_y2": 538.325, "r_x3": 133.768, "r_y3": 538.325, "coord_origin": "TOPLEFT"}, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "orig": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 558.986, "r_x1": 477.479, "r_y1": 558.986, "r_x2": 477.479, "r_y2": 550.28, "r_x3": 133.768, "r_y3": 550.28, "coord_origin": "TOPLEFT"}, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "orig": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 570.941, "r_x1": 230.812, "r_y1": 570.941, "r_x2": 230.812, "r_y2": 562.235, "r_x3": 133.768, "r_y3": 562.235, "coord_origin": "TOPLEFT"}, "text": "te feugait nulla facilisi.", "orig": "te feugait nulla facilisi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.976, "r_y0": 570.941, "r_x1": 477.481, "r_y1": 570.941, "r_x2": 477.481, "r_y2": 562.235, "r_x3": 234.976, "r_y3": 562.235, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "orig": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 582.896, "r_x1": 477.484, "r_y1": 582.896, "r_x2": 477.484, "r_y2": 574.19, "r_x3": 133.768, "r_y3": 574.19, "coord_origin": "TOPLEFT"}, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "orig": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 594.852, "r_x1": 173.064, "r_y1": 594.852, "r_x2": 173.064, "r_y2": 586.146, "r_x3": 133.768, "r_y3": 586.146, "coord_origin": "TOPLEFT"}, "text": "volutpat.", "orig": "volutpat.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 133.768, "t": 598.101, "r": 477.484, "b": 666.583, "coord_origin": "TOPLEFT"}, "confidence": 0.98720782995224, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 606.807, "r_x1": 477.475, "r_y1": 606.807, "r_x2": 477.475, "r_y2": 598.101, "r_x3": 148.712, "r_y3": 598.101, "coord_origin": "TOPLEFT"}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 618.762, "r_x1": 477.482, "r_y1": 618.762, "r_x2": 477.482, "r_y2": 610.056, "r_x3": 133.768, "r_y3": 610.056, "coord_origin": "TOPLEFT"}, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "orig": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 630.717, "r_x1": 477.479, "r_y1": 630.717, "r_x2": 477.479, "r_y2": 622.011, "r_x3": 133.768, "r_y3": 622.011, "coord_origin": "TOPLEFT"}, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "orig": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 642.672, "r_x1": 230.812, "r_y1": 642.672, "r_x2": 230.812, "r_y2": 633.966, "r_x3": 133.768, "r_y3": 633.966, "coord_origin": "TOPLEFT"}, "text": "te feugait nulla facilisi.", "orig": "te feugait nulla facilisi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.976, "r_y0": 642.672, "r_x1": 477.481, "r_y1": 642.672, "r_x2": 477.481, "r_y2": 633.966, "r_x3": 234.976, "r_y3": 633.966, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "orig": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 654.6279999999999, "r_x1": 477.484, "r_y1": 654.6279999999999, "r_x2": 477.484, "r_y2": 645.922, "r_x3": 133.768, "r_y3": 645.922, "coord_origin": "TOPLEFT"}, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "orig": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 666.583, "r_x1": 173.064, "r_y1": 666.583, "r_x2": 173.064, "r_y2": 657.877, "r_x3": 133.768, "r_y3": 657.877, "coord_origin": "TOPLEFT"}, "text": "volutpat.", "orig": "volutpat.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}], "headers": [{"label": "page_footer", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 303.133, "t": 695.832, "r": 308.114, "b": 704.538, "coord_origin": "TOPLEFT"}, "confidence": 0.8504500389099121, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.133, "r_y0": 704.538, "r_x1": 308.114, "r_y1": 704.538, "r_x2": 308.114, "r_y2": 695.832, "r_x3": 303.133, "r_y3": 695.832, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt b/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt index 8f0b2a8e..f6ac7324 100644 --- a/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt +++ b/tests/data/groundtruth/docling_v1/picture_classification.doctags.txt @@ -1,15 +1,15 @@ Figures Example -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir- +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
-Figure 1: +Figure 1: This is an example image.
-Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.
-Figure 2: +Figure 2: This is an example image.
-Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Figure 1: +Figure 1: This is an example image. -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. -Figure 2: - \ No newline at end of file +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. + +Figure 2: This is an example image. + + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 244.26300000000003, "r_x1": 477.475, "r_y1": 244.26300000000003, "r_x2": 477.475, "r_y2": 235.55700000000002, "r_x3": 414.054, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 256.21799999999996, "r_x1": 323.816, "r_y1": 256.21799999999996, "r_x2": 323.816, "r_y2": 247.51199999999994, "r_x3": 133.768, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 256.21799999999996, "r_x1": 371.404, "r_y1": 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 626.829, "r_x1": 477.482, "r_y1": 626.829, "r_x2": 477.482, "r_y2": 618.123, "r_x3": 133.768, "r_y3": 618.123, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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"Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 573.816, "r_x1": 432.467, "r_y1": 573.816, "r_x2": 432.467, "r_y2": 565.11, "r_x3": 133.768, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 573.816, "r_x1": 477.479, "r_y1": 573.816, "r_x2": 477.479, "r_y2": 565.11, "r_x3": 437.398, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "caption", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 226.891, "t": 529.245, "r": 384.355, "b": 537.951, "coord_origin": "TOPLEFT"}, "confidence": 0.9477447271347046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.891, "r_y0": 537.951, "r_x1": 266.075, "r_y1": 537.951, "r_x2": 266.075, "r_y2": 529.245, "r_x3": 226.891, "r_y3": 529.245, "coord_origin": "TOPLEFT"}, "text": "Figure 1:", "orig": "Figure 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.508, "r_y0": 537.951, "r_x1": 384.355, "r_y1": 537.951, "r_x2": 384.355, "r_y2": 529.245, "r_x3": 270.508, "r_y3": 529.245, "coord_origin": "TOPLEFT"}, "text": "This is an example image.", "orig": "This is an example image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 1: This is an example image."}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.768, "t": 553.155, "r": 477.482, "b": 669.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9862836599349976, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 561.861, "r_x1": 477.478, "r_y1": 561.861, "r_x2": 477.478, "r_y2": 553.155, "r_x3": 148.712, "r_y3": 553.155, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 573.816, "r_x1": 432.467, "r_y1": 573.816, "r_x2": 432.467, "r_y2": 565.11, "r_x3": 133.768, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 573.816, "r_x1": 477.479, "r_y1": 573.816, "r_x2": 477.479, "r_y2": 565.11, "r_x3": 437.398, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 585.771, "r_x1": 174.725, "r_y1": 585.771, "r_x2": 174.725, "r_y2": 577.065, "r_x3": 133.768, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 585.771, "r_x1": 431.98, "r_y1": 585.771, "r_x2": 431.98, "r_y2": 577.065, "r_x3": 180.075, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 585.771, "r_x1": 477.476, "r_y1": 585.771, "r_x2": 477.476, "r_y2": 577.065, "r_x3": 437.319, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 597.726, "r_x1": 477.476, "r_y1": 597.726, "r_x2": 477.476, "r_y2": 589.02, "r_x3": 133.768, "r_y3": 589.02, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 633.592, "r_x1": 477.482, "r_y1": 633.592, "r_x2": 477.482, "r_y2": 624.886, "r_x3": 133.768, "r_y3": 624.886, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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Lorem ip-", "orig": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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269.33299999999997, "r_x3": 375.548, "r_y3": 269.33299999999997, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 289.995, "r_x1": 351.485, "r_y1": 289.995, "r_x2": 351.485, "r_y2": 281.289, "r_x3": 133.768, "r_y3": 281.289, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "caption", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 226.891, "t": 529.245, "r": 384.355, "b": 537.951, "coord_origin": "TOPLEFT"}, "confidence": 0.9477447271347046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.891, "r_y0": 537.951, "r_x1": 266.075, "r_y1": 537.951, "r_x2": 266.075, "r_y2": 529.245, "r_x3": 226.891, "r_y3": 529.245, "coord_origin": "TOPLEFT"}, "text": "Figure 1:", "orig": "Figure 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.508, "r_y0": 537.951, "r_x1": 384.355, "r_y1": 537.951, "r_x2": 384.355, "r_y2": 529.245, "r_x3": 270.508, "r_y3": 529.245, "coord_origin": "TOPLEFT"}, "text": "This is an example image.", "orig": "This is an example image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 1: This is an example image."}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.768, "t": 553.155, "r": 477.482, "b": 669.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9862836599349976, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 561.861, "r_x1": 477.478, "r_y1": 561.861, "r_x2": 477.478, "r_y2": 553.155, "r_x3": 148.712, "r_y3": 553.155, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 573.816, "r_x1": 432.467, "r_y1": 573.816, "r_x2": 432.467, "r_y2": 565.11, "r_x3": 133.768, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 573.816, "r_x1": 477.479, "r_y1": 573.816, "r_x2": 477.479, "r_y2": 565.11, "r_x3": 437.398, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 585.771, "r_x1": 174.725, "r_y1": 585.771, "r_x2": 174.725, "r_y2": 577.065, "r_x3": 133.768, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 585.771, "r_x1": 431.98, "r_y1": 585.771, "r_x2": 431.98, "r_y2": 577.065, "r_x3": 180.075, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 585.771, "r_x1": 477.476, "r_y1": 585.771, "r_x2": 477.476, "r_y2": 577.065, "r_x3": 437.319, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 597.726, "r_x1": 477.476, "r_y1": 597.726, "r_x2": 477.476, "r_y2": 589.02, "r_x3": 133.768, "r_y3": 589.02, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 609.682, "r_x1": 186.966, "r_y1": 609.682, "r_x2": 186.966, "r_y2": 600.976, "r_x3": 133.768, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "ipsum dolor", "orig": "ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.938, "r_y0": 609.682, "r_x1": 202.509, "r_y1": 609.682, "r_x2": 202.509, "r_y2": 600.976, "r_x3": 191.938, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sit", "orig": "sit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 207.47, "r_y0": 609.682, "r_x1": 231.823, "r_y1": 609.682, "r_x2": 231.823, "r_y2": 600.976, "r_x3": 207.47, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "amet,", "orig": "amet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.203, "r_y0": 609.682, "r_x1": 282.117, "r_y1": 609.682, "r_x2": 282.117, "r_y2": 600.976, "r_x3": 237.203, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "consetetur", "orig": "consetetur", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 287.078, "r_y0": 609.682, "r_x1": 331.468, "r_y1": 609.682, "r_x2": 331.468, "r_y2": 600.976, "r_x3": 287.078, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sadipscing", "orig": "sadipscing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.439, "r_y0": 609.682, "r_x1": 356.946, "r_y1": 609.682, "r_x2": 356.946, "r_y2": 600.976, "r_x3": 336.439, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "elitr,", "orig": "elitr,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 362.316, "r_y0": 609.682, "r_x1": 376.208, "r_y1": 609.682, "r_x2": 376.208, "r_y2": 600.976, "r_x3": 362.316, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sed", "orig": "sed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.179, "r_y0": 609.682, "r_x1": 477.482, "r_y1": 609.682, "r_x2": 477.482, "r_y2": 600.976, "r_x3": 381.179, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "diam nonumy eirmod", "orig": "diam nonumy eirmod", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 621.637, "r_x1": 477.481, "r_y1": 621.637, "r_x2": 477.481, "r_y2": 612.931, "r_x3": 133.768, "r_y3": 612.931, "coord_origin": "TOPLEFT"}, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 633.592, "r_x1": 477.482, "r_y1": 633.592, "r_x2": 477.482, "r_y2": 624.886, "r_x3": 133.768, "r_y3": 624.886, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 645.547, "r_x1": 387.354, "r_y1": 645.547, "r_x2": 387.354, "r_y2": 636.841, "r_x3": 133.768, "r_y3": 636.841, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 645.547, "r_x1": 477.478, "r_y1": 645.547, "r_x2": 477.478, "r_y2": 636.841, "r_x3": 393.571, "r_y3": 636.841, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 657.502, "r_x1": 477.48, "r_y1": 657.502, "r_x2": 477.48, "r_y2": 648.796, "r_x3": 133.768, "r_y3": 648.796, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 669.457, "r_x1": 399.578, "r_y1": 669.457, "r_x2": 399.578, "r_y2": 660.751, "r_x3": 133.768, "r_y3": 660.751, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 208.39800000000002, "r_x1": 477.482, "r_y1": 208.39800000000002, "r_x2": 477.482, "r_y2": 199.692, "r_x3": 133.768, "r_y3": 199.692, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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Lorem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 184.48699999999997, "r_x1": 186.966, "r_y1": 184.48699999999997, "r_x2": 186.966, "r_y2": 175.78099999999995, "r_x3": 133.768, "r_y3": 175.78099999999995, "coord_origin": "TOPLEFT"}, "text": "ipsum dolor", "orig": "ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.938, "r_y0": 184.48699999999997, "r_x1": 202.509, "r_y1": 184.48699999999997, "r_x2": 202.509, "r_y2": 175.78099999999995, "r_x3": 191.938, "r_y3": 175.78099999999995, "coord_origin": "TOPLEFT"}, "text": "sit", "orig": "sit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 207.47, "r_y0": 184.48699999999997, "r_x1": 231.823, "r_y1": 184.48699999999997, "r_x2": 231.823, "r_y2": 175.78099999999995, "r_x3": 207.47, "r_y3": 175.78099999999995, "coord_origin": "TOPLEFT"}, "text": "amet,", "orig": "amet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.203, "r_y0": 184.48699999999997, "r_x1": 282.117, "r_y1": 184.48699999999997, "r_x2": 282.117, "r_y2": 175.78099999999995, "r_x3": 237.203, "r_y3": 175.78099999999995, "coord_origin": "TOPLEFT"}, "text": "consetetur", "orig": "consetetur", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 287.078, "r_y0": 184.48699999999997, "r_x1": 331.468, "r_y1": 184.48699999999997, "r_x2": 331.468, "r_y2": 175.78099999999995, "r_x3": 287.078, "r_y3": 175.78099999999995, "coord_origin": "TOPLEFT"}, "text": "sadipscing", "orig": "sadipscing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.439, "r_y0": 184.48699999999997, "r_x1": 356.946, "r_y1": 184.48699999999997, "r_x2": 356.946, "r_y2": 175.78099999999995, "r_x3": 336.439, "r_y3": 175.78099999999995, "coord_origin": "TOPLEFT"}, "text": "elitr,", "orig": "elitr,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 362.316, "r_y0": 184.48699999999997, "r_x1": 376.208, "r_y1": 184.48699999999997, "r_x2": 376.208, "r_y2": 175.78099999999995, "r_x3": 362.316, "r_y3": 175.78099999999995, "coord_origin": "TOPLEFT"}, "text": "sed", "orig": "sed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.179, "r_y0": 184.48699999999997, "r_x1": 477.482, "r_y1": 184.48699999999997, "r_x2": 477.482, "r_y2": 175.78099999999995, "r_x3": 381.179, "r_y3": 175.78099999999995, "coord_origin": "TOPLEFT"}, "text": "diam nonumy eirmod", "orig": "diam nonumy eirmod", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 196.44299999999998, "r_x1": 477.481, "r_y1": 196.44299999999998, "r_x2": 477.481, "r_y2": 187.73699999999997, "r_x3": 133.768, "r_y3": 187.73699999999997, "coord_origin": "TOPLEFT"}, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 208.39800000000002, "r_x1": 477.482, "r_y1": 208.39800000000002, "r_x2": 477.482, "r_y2": 199.692, "r_x3": 133.768, "r_y3": 199.692, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 220.35299999999995, "r_x1": 387.354, "r_y1": 220.35299999999995, "r_x2": 387.354, "r_y2": 211.64700000000005, "r_x3": 133.768, "r_y3": 211.64700000000005, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 220.35299999999995, "r_x1": 477.478, "r_y1": 220.35299999999995, "r_x2": 477.478, "r_y2": 211.64700000000005, "r_x3": 393.571, "r_y3": 211.64700000000005, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 232.308, "r_x1": 477.48, "r_y1": 232.308, "r_x2": 477.48, "r_y2": 223.60199999999998, "r_x3": 133.768, "r_y3": 223.60199999999998, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 244.26300000000003, "r_x1": 407.17, "r_y1": 244.26300000000003, "r_x2": 407.17, "r_y2": 235.55700000000002, "r_x3": 133.768, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 244.26300000000003, "r_x1": 477.475, "r_y1": 244.26300000000003, "r_x2": 477.475, "r_y2": 235.55700000000002, "r_x3": 414.054, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 256.21799999999996, "r_x1": 323.816, "r_y1": 256.21799999999996, "r_x2": 323.816, "r_y2": 247.51199999999994, "r_x3": 133.768, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 256.21799999999996, "r_x1": 371.404, "r_y1": 256.21799999999996, "r_x2": 371.404, "r_y2": 247.51199999999994, "r_x3": 330.72, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 256.21799999999996, "r_x1": 477.476, "r_y1": 256.21799999999996, "r_x2": 477.476, "r_y2": 247.51199999999994, "r_x3": 375.548, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 268.174, "r_x1": 351.485, "r_y1": 268.174, "r_x2": 351.485, "r_y2": 259.46799999999996, "r_x3": 133.768, "r_y3": 259.46799999999996, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "caption", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "caption", "bbox": {"l": 226.891, "t": 523.321, "r": 384.355, "b": 532.027, "coord_origin": "TOPLEFT"}, "confidence": 0.9494236707687378, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.891, "r_y0": 532.027, "r_x1": 266.075, "r_y1": 532.027, "r_x2": 266.075, "r_y2": 523.321, "r_x3": 226.891, "r_y3": 523.321, "coord_origin": "TOPLEFT"}, "text": "Figure 2:", "orig": "Figure 2:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.508, "r_y0": 532.027, "r_x1": 384.355, "r_y1": 532.027, "r_x2": 384.355, "r_y2": 523.321, "r_x3": 270.508, "r_y3": 523.321, "coord_origin": "TOPLEFT"}, "text": "This is an example image.", "orig": "This is an example image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: This is an example image."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 546.392, "r": 477.482, "b": 674.649, "coord_origin": "TOPLEFT"}, "confidence": 0.9874356985092163, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 555.098, "r_x1": 477.478, "r_y1": 555.098, "r_x2": 477.478, "r_y2": 546.392, "r_x3": 148.712, "r_y3": 546.392, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 567.053, "r_x1": 432.467, "r_y1": 567.053, "r_x2": 432.467, "r_y2": 558.347, "r_x3": 133.768, "r_y3": 558.347, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 567.053, "r_x1": 477.479, "r_y1": 567.053, "r_x2": 477.479, "r_y2": 558.347, "r_x3": 437.398, "r_y3": 558.347, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 579.008, "r_x1": 174.725, "r_y1": 579.008, "r_x2": 174.725, "r_y2": 570.302, "r_x3": 133.768, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 579.008, "r_x1": 431.98, "r_y1": 579.008, "r_x2": 431.98, "r_y2": 570.302, "r_x3": 180.075, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 579.008, "r_x1": 477.476, "r_y1": 579.008, "r_x2": 477.476, "r_y2": 570.302, "r_x3": 437.319, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 590.963, "r_x1": 477.476, "r_y1": 590.963, "r_x2": 477.476, "r_y2": 582.2570000000001, "r_x3": 133.768, "r_y3": 582.2570000000001, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 626.829, "r_x1": 477.482, "r_y1": 626.829, "r_x2": 477.482, "r_y2": 618.123, "r_x3": 133.768, "r_y3": 618.123, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 638.784, "r_x1": 387.354, "r_y1": 638.784, "r_x2": 387.354, "r_y2": 630.078, "r_x3": 133.768, "r_y3": 630.078, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 638.784, "r_x1": 477.478, "r_y1": 638.784, "r_x2": 477.478, "r_y2": 630.078, "r_x3": 393.571, "r_y3": 630.078, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 650.739, "r_x1": 477.48, "r_y1": 650.739, "r_x2": 477.48, "r_y2": 642.033, "r_x3": 133.768, "r_y3": 642.033, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 662.694, "r_x1": 407.17, "r_y1": 662.694, "r_x2": 407.17, "r_y2": 653.988, "r_x3": 133.768, "r_y3": 653.988, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 662.694, "r_x1": 477.475, "r_y1": 662.694, "r_x2": 477.475, "r_y2": 653.988, "r_x3": 414.054, "r_y3": 653.988, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 674.649, "r_x1": 318.017, "r_y1": 674.649, "r_x2": 318.017, "r_y2": 665.943, "r_x3": 133.768, "r_y3": 665.943, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum."}, {"label": "page_footer", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 303.133, "t": 695.831, "r": 308.114, "b": 704.537, "coord_origin": "TOPLEFT"}, "confidence": 0.8888521790504456, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.133, "r_y0": 704.537, "r_x1": 308.114, "r_y1": 704.537, "r_x2": 308.114, "r_y2": 695.831, "r_x3": 303.133, "r_y3": 695.831, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "picture", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 218.8155517578125, "t": 278.0153503417969, "r": 391.96246337890625, "b": 508.89410400390625, "coord_origin": "TOPLEFT"}, "confidence": 0.9837717413902283, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.768, "t": 127.96100000000001, "r": 477.482, "b": 268.174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870928525924683, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 136.66700000000003, "r_x1": 477.478, "r_y1": 136.66700000000003, "r_x2": 477.478, "r_y2": 127.96100000000001, "r_x3": 148.712, "r_y3": 127.96100000000001, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 148.62199999999996, "r_x1": 432.467, "r_y1": 148.62199999999996, "r_x2": 432.467, "r_y2": 139.91600000000005, "r_x3": 133.768, "r_y3": 139.91600000000005, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 148.62199999999996, "r_x1": 477.479, "r_y1": 148.62199999999996, "r_x2": 477.479, "r_y2": 139.91600000000005, "r_x3": 437.398, "r_y3": 139.91600000000005, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 160.577, "r_x1": 174.725, "r_y1": 160.577, "r_x2": 174.725, "r_y2": 151.87099999999998, "r_x3": 133.768, "r_y3": 151.87099999999998, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 160.577, "r_x1": 431.98, "r_y1": 160.577, "r_x2": 431.98, "r_y2": 151.87099999999998, "r_x3": 180.075, "r_y3": 151.87099999999998, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 160.577, "r_x1": 477.476, "r_y1": 160.577, "r_x2": 477.476, "r_y2": 151.87099999999998, "r_x3": 437.319, "r_y3": 151.87099999999998, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 172.53200000000004, "r_x1": 477.476, "r_y1": 172.53200000000004, "r_x2": 477.476, "r_y2": 163.82600000000002, "r_x3": 133.768, "r_y3": 163.82600000000002, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 208.39800000000002, "r_x1": 477.482, "r_y1": 208.39800000000002, "r_x2": 477.482, "r_y2": 199.692, "r_x3": 133.768, "r_y3": 199.692, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 220.35299999999995, "r_x1": 387.354, "r_y1": 220.35299999999995, "r_x2": 387.354, "r_y2": 211.64700000000005, "r_x3": 133.768, "r_y3": 211.64700000000005, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 220.35299999999995, "r_x1": 477.478, "r_y1": 220.35299999999995, "r_x2": 477.478, "r_y2": 211.64700000000005, "r_x3": 393.571, "r_y3": 211.64700000000005, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 232.308, "r_x1": 477.48, "r_y1": 232.308, "r_x2": 477.48, "r_y2": 223.60199999999998, "r_x3": 133.768, "r_y3": 223.60199999999998, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 244.26300000000003, "r_x1": 407.17, "r_y1": 244.26300000000003, "r_x2": 407.17, "r_y2": 235.55700000000002, "r_x3": 133.768, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 244.26300000000003, "r_x1": 477.475, "r_y1": 244.26300000000003, "r_x2": 477.475, "r_y2": 235.55700000000002, "r_x3": 414.054, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 256.21799999999996, "r_x1": 323.816, "r_y1": 256.21799999999996, "r_x2": 323.816, "r_y2": 247.51199999999994, "r_x3": 133.768, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 256.21799999999996, "r_x1": 371.404, "r_y1": 256.21799999999996, "r_x2": 371.404, "r_y2": 247.51199999999994, "r_x3": 330.72, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 256.21799999999996, "r_x1": 477.476, "r_y1": 256.21799999999996, "r_x2": 477.476, "r_y2": 247.51199999999994, "r_x3": 375.548, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 268.174, "r_x1": 351.485, "r_y1": 268.174, "r_x2": 351.485, "r_y2": 259.46799999999996, "r_x3": 133.768, "r_y3": 259.46799999999996, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "caption", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "caption", "bbox": {"l": 226.891, "t": 523.321, "r": 384.355, "b": 532.027, "coord_origin": "TOPLEFT"}, "confidence": 0.9494236707687378, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.891, "r_y0": 532.027, "r_x1": 266.075, "r_y1": 532.027, "r_x2": 266.075, "r_y2": 523.321, "r_x3": 226.891, "r_y3": 523.321, "coord_origin": "TOPLEFT"}, "text": "Figure 2:", "orig": "Figure 2:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.508, "r_y0": 532.027, "r_x1": 384.355, "r_y1": 532.027, "r_x2": 384.355, "r_y2": 523.321, "r_x3": 270.508, "r_y3": 523.321, "coord_origin": "TOPLEFT"}, "text": "This is an example image.", "orig": "This is an example image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: This is an example image."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 546.392, "r": 477.482, "b": 674.649, "coord_origin": "TOPLEFT"}, "confidence": 0.9874356985092163, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 555.098, "r_x1": 477.478, "r_y1": 555.098, "r_x2": 477.478, "r_y2": 546.392, "r_x3": 148.712, "r_y3": 546.392, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 567.053, "r_x1": 432.467, "r_y1": 567.053, "r_x2": 432.467, "r_y2": 558.347, "r_x3": 133.768, "r_y3": 558.347, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 567.053, "r_x1": 477.479, "r_y1": 567.053, "r_x2": 477.479, "r_y2": 558.347, "r_x3": 437.398, "r_y3": 558.347, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 579.008, "r_x1": 174.725, "r_y1": 579.008, "r_x2": 174.725, "r_y2": 570.302, "r_x3": 133.768, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 579.008, "r_x1": 431.98, "r_y1": 579.008, "r_x2": 431.98, "r_y2": 570.302, "r_x3": 180.075, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 579.008, "r_x1": 477.476, "r_y1": 579.008, "r_x2": 477.476, "r_y2": 570.302, "r_x3": 437.319, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 590.963, "r_x1": 477.476, "r_y1": 590.963, "r_x2": 477.476, "r_y2": 582.2570000000001, "r_x3": 133.768, "r_y3": 582.2570000000001, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 626.829, "r_x1": 477.482, "r_y1": 626.829, "r_x2": 477.482, "r_y2": 618.123, "r_x3": 133.768, "r_y3": 618.123, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum."}, {"label": "picture", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 218.8155517578125, "t": 278.0153503417969, "r": 391.96246337890625, "b": 508.89410400390625, "coord_origin": "TOPLEFT"}, "confidence": 0.9837717413902283, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 303.133, "t": 695.831, "r": 308.114, "b": 704.537, "coord_origin": "TOPLEFT"}, "confidence": 0.8888521790504456, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.133, "r_y0": 704.537, "r_x1": 308.114, "r_y1": 704.537, "r_x2": 308.114, "r_y2": 695.831, "r_x3": 303.133, "r_y3": 695.831, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.doctags.txt b/tests/data/groundtruth/docling_v1/redp5110_sampled.doctags.txt index 3b7a0671..20a3604b 100644 --- a/tests/data/groundtruth/docling_v1/redp5110_sampled.doctags.txt +++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.doctags.txt @@ -1,8 +1,9 @@ +Front cover
-Row and Column Access Control +Row and Column Access Control Support in IBM DB2 for i
@@ -10,185 +11,222 @@ Contents -Solution Brief +DB2 for i Center of Excellence +Solution Brief IBM Systems Lab Services and Training
Highlights -- /g115/g3 -- /g115/g3 -- /g115/g3 -- /g115/g3 +- /g115/g3 /g40/g81/g75/g68/g81/g70/g72/g3 /g87/g75/g72/g3 /g83/g72/g85/g73/g82/g85/g80/g68/g81/g70/g72/g3 /g82/g73/g3 /g92/g82/g88/g85/g3 /g71/g68/g87/g68/g69/g68/g86/g72/g3 /g82/g83/g72/g85/g68/g87/g76/g82/g81/g86 +- /g115/g3 /g40/g68/g85/g81/g3 /g74/g85/g72/g68/g87/g72/g85/g3 /g85 /g72/g87/g88/g85/g81/g3 /g82/g81/g3 /g44/g55/g3 /g83/g85 /g82/g77/g72/g70/g87/g86/g3 /g87/g75/g85 /g82/g88/g74/g75/g3 /g80/g82/g71/g72/g85/g81/g76/g93/g68/g87/g76/g82/g81/g3 /g82/g73/g3 /g71/g68/g87/g68/g69/g68/g86/g72/g3 /g68/g81/g71/g3 /g68/g83/g83/g79/g76/g70/g68/g87/g76/g82/g81/g86 +- /g115/g3 /g53/g72/g79/g92/g3 /g82/g81/g3 /g44/g37/g48/g3 /g72/g91/g83/g72/g85/g87/g3 /g70/g82/g81/g86/g88/g79/g87/g76/g81/g74/g15/g3 /g86/g78/g76/g79/g79/g86/g3 /g86/g75/g68/g85/g76/g81/g74/g3 /g68/g81/g71/g3 /g85/g72/g81/g82/g90/g81/g3 /g86/g72/g85/g89/g76/g70/g72/g86 +- /g115/g3 /g55 /g68/g78/g72/g3 /g68/g71/g89/g68/g81/g87/g68/g74/g72/g3 /g82/g73/g3 /g68/g70/g70/g72/g86/g86/g3 /g87/g82/g3 /g68/g3 /g90/g82/g85/g79/g71/g90/g76/g71/g72/g3 /g86/g82/g88/g85/g70/g72/g3 /g82/g73/g3 /g72/g91/g83/g72/g85/g87/g76/g86/g72
Power Services -DB2 for i +DB2 for i Center of Excellence Expert help to achieve your business requirements We build confident, satisfied clients -No one else has the vast consulting experiences, skills sharing and +No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you. Because no one else is IBM. -With combined experiences and direct access to development groups, +With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. Who we are, some of what we do Global CoE engagements cover topics including: -- rglyph -- rglyph -- rglyph -- rglyph -- rglyph -- rglyph -- rglyph -- rglyph -- rglyph +- rglyph Database performance and scalability +- rglyph Advanced SQL knowledge and skills transfer +- rglyph Business intelligence and analytics +- rglyph DB2 Web Query +- rglyph Query/400 modernization for better reporting and analysis capabilities +- rglyph Database modernization and re-engineering +- rglyph Data-centric architecture and design +- rglyph Extremely large database and overcoming limits to growth +- rglyph ISV education and enablement Preface -This IBMfi Redpaper™ publication provides information about the IBM i 7.2 feature of IBM -This paper is intended for database engineers, data-centric application developers, and -This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with +This IBMfi Redpaper™ publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. +This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.
-Jim Bainbridge +Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.
-Hernando Bedoya +Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com . Authors
1 -Securing and protecting IBM DB2 -Recent news headlines are filled with reports of data breaches and cyber-attacks impacting -Businesses must make a serious effort to secure their data and recognize that securing -This chapter describes how you can secure and protect data in DB2 for i. The following topics -- /SM590000 -- /SM590000 -- /SM590000 +Chapter 1. +Securing and protecting IBM DB2 data +Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. +Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement. +This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter: +- /SM590000 Security fundamentals +- /SM590000 Current state of IBM i security +- /SM590000 DB2 for i security controls 1.1 Security fundamentals -Before reviewing database security techniques, there are two fundamental steps in securing -- /SM590000 -- The monitoring and assessment of adherence to the security policy determines whether +Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described: +- /SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. +- The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. A security policy is what defines whether the system and its settings are secure (or not). -- /SM590000 -With your eyes now open to the importance of securing information assets, the rest of this +- /SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. +With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i. 1.2 Current state of IBM i security -Because of the inherently secure nature of IBM i, many clients rely on the default system -Even more disturbing is that many IBM i clients remain in this state, despite the news -Traditionally, IBM i applications have employed menu-based security to counteract this default -Many businesses are trying to limit data access to a need-to-know basis. This security goal +Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE. +Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data. +Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data. +Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage. 1.3.1 Existing row and column control -Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL -Using SQL views to limit access to a subset of the data in a table also has its own set of -Even if you are willing to live with these performance and management issues, a user with +Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator. +Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases. +Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.
Figure 1-2 Existing row and column controls
2.1.6 Change Function Usage CL command The following CL commands can be used to work with, display, or change function usage IDs: -- /SM590000 -- /SM590000 -- /SM590000 -For example, the following +- /SM590000 Work Function Usage ( WRKFCNUSG ) +- /SM590000 Change Function Usage ( CHGFCNUSG ) +- /SM590000 Display Function Usage ( DSPFCNUSG ) +For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules: CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED) 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view -The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 +The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view. +Column nameData typeDescription +FUNCTION_IDVARCHAR(30)ID of the function. +USER_NAMEVARCHAR(10)Name of the user profile that has a usage setting for this function. +USAGEVARCHAR(7)Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function. +USER_TYPEVARCHAR(5)Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group.
Table 2-1 FUNCTION_USAGE view
Table 2-1 FUNCTION_USAGE view -To discover who has authorization to define and manage RCAC, you can use the query that is +To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. Example 2-1 Query to determine who has authority to define and manage RCAC SELECT function_id, +user_name, +usage, user_type FROM function_usage WHERE function_id='QIBM_DB_SECADM' ORDER BY user_name; 2.2 Separation of duties -Separation of duties helps businesses comply with industry regulations or organizational -For example, assume that a business has assigned the duty to manage security on IBM i to -In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, -QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special -QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows -A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function -Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to +Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority. +For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security. +In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table. +QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group. +QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table. +A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself. +Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools. +User action*JOBCTLQIBM_DB_SECADMQIBM_DB_SQLADMQIBM_DB_SYSMONNo Authority +SET CURRENT DEGREE (SQL statement)XX +CHGQRYA command targeting a different user's jobXX +STRDBMON or ENDDBMON commands targeting a different user's jobXX +STRDBMON or ENDDBMON commands targeting a job that matches the current userXXXX +QUSRJOBI() API format 900 or System i Navigator's SQL Details for JobXXX +Visual Explain within Run SQL scriptsXXXX +Visual Explain outside of Run SQL scriptsXX +ANALYZE PLAN CACHE procedureXX +DUMP PLAN CACHE procedureXX +MODIFY PLAN CACHE procedureXX +MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)XX +CHANGE PLAN CACHE SIZE procedure (currently does not check authority)XX
Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority
Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority -The SQL +The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.
Figure 3-1 CREATE PERMISSION SQL statement
Column mask -A column mask is a database object that manifests a column value access control rule for a +A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number. Table 3-1 summarizes these special registers and their values. +Special registerCorresponding value +USER or SESSION_USERThe effective user of the thread excluding adopted authority. +CURRENT_USERThe effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER. +SYSTEM_USERThe authorization ID that initiated the connection.
Table 3-1 Special registers and their corresponding values
Table 3-1 Special registers and their corresponding values -Figure 3-5 shows the difference in the special register values when an adopted authority is -- /SM590000 -- /SM590000 -- /SM590000 -- /SM590000 -- /SM590000 +Figure 3-5 shows the difference in the special register values when an adopted authority is used: +- /SM590000 A user connects to the server using the user profile ALICE. +- /SM590000 USER and CURRENT USER initially have the same value of ALICE. +- /SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called. +- /SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority. +- /SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.
Figure 3-5 Special registers and adopted authority
3.2.2 Built-in global variables -Built-in global variables are provided with the database manager and are used in SQL -IBM DB2 for i supports nine different built-in global variables that are read only and +Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables. +IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic. Table 3-2 lists the nine built-in global variables. +Global variableTypeDescription +CLIENT_HOSTVARCHAR(255)Host name of the current client as returned by the system +CLIENT_IPADDRVARCHAR(128)IP address of the current client as returned by the system +CLIENT_PORTINTEGERPort used by the current client to communicate with the server +PACKAGE_NAMEVARCHAR(128)Name of the currently running package +PACKAGE_SCHEMAVARCHAR(128)Schema name of the currently running package +PACKAGE_VERSIONVARCHAR(64)Version identifier of the currently running package +ROUTINE_SCHEMAVARCHAR(128)Schema name of the currently running routine +ROUTINE_SPECIFIC_NAMEVARCHAR(128)Name of the currently running routine +ROUTINE_TYPECHAR(1)Type of the currently running routine
Table 3-2 Built-in global variables
Table 3-2 Built-in global variables 3.3 VERIFY_GROUP_FOR_USER function -The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily -If a special register value is in the list of user profiles or it is a member of a group profile +The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error. +If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value. Here is an example of using the VERIFY_GROUP_FOR_USER function: -- 1. -- 2. -- 3. -VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') -VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') +- 1. There are user profiles for MGR, JANE, JUDY, and TONY. +- 2. The user profile JANE specifies a group profile of MGR. +- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1: +VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY') RETURN CASE -WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 -- 2. -- - -- - -- - -- - +WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ; +- 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones: +- -Human Resources can see the unmasked TAX_ID of the employees. +- -Employees can see only their own unmasked TAX_ID. +- -Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234). +- -Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX. - To implement this column mask, run the SQL statement that is shown in Example 3-9. -CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES +CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; Example 3-9 Creating a mask on the TAX_ID column -- 3. +- 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.
Figure 3-10 Column masks shown in System i Navigator
3.6.6 Activating RCAC -Now that you have created the row permission and the two column masks, RCAC must be -- 1. +Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps: +- 1. Run the SQL statements that are shown in Example 3-10. Example 3-10 Activating RCAC on the EMPLOYEES table - /* Active Row Access Control (permissions) */ - /* Active Column Access Control (masks) */ +ALTER TABLE HR_SCHEMA.EMPLOYEES +ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL; -- 2. +- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables , right-click the EMPLOYEES table, and click Definition .
Figure 3-11 Selecting the EMPLOYEES table from System i Navigator
-- 2. -- 3. +- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. +- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.
Figure 4-68 Visual Explain with RCAC enabled @@ -197,22 +235,22 @@ Figure 4-69 Index advice with no RCAC
-THEN C . CUSTOMER_TAX_ID -CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C -Row and Column Access Control -Implement roles and -This IBM Redpaper publication provides information about the IBM i 7.2 -Leverage row -Protect columns by -This paper is intended for database engineers, data-centric application +THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ; +Back cover +Row and Column Access Control Support in IBM DB2 for i +Implement roles and separation of duties +Leverage row permissions on the database +This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. +Protect columns by defining column masks +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.
-INTERNATIONAL -BUILDING TECHNICAL -IBM Redbooks are developed -For more information: +INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION +BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE +IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. 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"Authors", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/7"}, {"prov": [{"bbox": [500.4, 661.868, 522.618, 698.831], "page": 5, "span": [0, 1], "__ref_s3_data": null}], "text": "1", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 513.082, 549.157, 537.114], "page": 5, "span": [0, 31], "__ref_s3_data": null}], "text": "Securing and protecting IBM DB2", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 434.078, 542.266, 443.291], "page": 5, "span": [0, 90], "__ref_s3_data": null}], "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 340.059, 525.074, 349.272], "page": 5, "span": [0, 86], "__ref_s3_data": null}], "text": "Businesses must 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Table 2-1", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [136.8, 504.117, 283.949, 512.442], "page": 8, "span": [0, 31], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 330.278, 547.589, 339.491], "page": 8, "span": [0, 90], "__ref_s3_data": null}], "text": "To discover who has authorization to define and manage RCAC, you can use the query that is", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 297.117, 462.374, 305.442], "page": 8, "span": [0, 76], "__ref_s3_data": null}], "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 279.64300000000003, 251.699, 288.043], "page": 8, "span": [0, 23], 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"font": null}, {"prov": [{"bbox": [64.8, 157.01599999999996, 249.582, 171.779], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "2.2 Separation of duties", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 130.23800000000006, 529.043, 139.45100000000002], "page": 8, "span": [0, 88], "__ref_s3_data": null}], "text": "Separation of duties helps businesses comply with industry regulations or organizational", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 711.278, 542.703, 720.491], "page": 9, "span": [0, 88], "__ref_s3_data": null}], "text": "For example, assume that a business has assigned the duty to manage security on IBM i to", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 629.259, 544.17, 638.472], "page": 9, "span": [0, 88], "__ref_s3_data": null}], "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 571.3, 538.66, 580.513], "page": 9, "span": [0, 80], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 537.281, 545.805, 546.494], "page": 9, "span": [0, 85], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 491.262, 533.79, 500.475], "page": 9, "span": [0, 83], "__ref_s3_data": null}], "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 433.302, 543.076, 442.515], "page": 9, "span": [0, 89], "__ref_s3_data": null}], "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [64.8, 400.137, 391.767, 408.462], "page": 9, "span": [0, 81], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 711.278, 179.591, 720.491], "page": 10, "span": [0, 7], "__ref_s3_data": null}], "text": "The SQL", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [136.8, 340.956, 215.383, 352.056], "page": 10, "span": [0, 11], "__ref_s3_data": null}], "text": "Column mask", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": 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the special register values when an adopted authority is", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 539.623, 141.78, 548.024], "page": 11, "span": [0, 9], "__ref_s3_data": null}], "text": "- /SM590000", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 522.644, 141.78, 531.044], "page": 11, "span": [0, 9], "__ref_s3_data": null}], "text": "- /SM590000", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 505.664, 141.78, 514.064], "page": 11, "span": [0, 9], "__ref_s3_data": null}], "text": "- /SM590000", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 476.624, 141.78, 485.025], "page": 11, "span": [0, 9], "__ref_s3_data": null}], "text": "- /SM590000", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.801, 435.645, 141.781, 444.045], "page": 11, "span": [0, 9], "__ref_s3_data": null}], "text": "- /SM590000", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [64.8, 154.457, 247.016, 166.44499999999994], "page": 11, "span": [0, 32], "__ref_s3_data": null}], "text": "3.2.2 Built-in global variables", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 131.07799999999997, 518.01, 140.29099999999994], "page": 11, "span": [0, 84], "__ref_s3_data": null}], "text": "Built-in global variables are provided with the database manager and are used in SQL", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 97.05899999999997, 504.455, 106.27200000000005], "page": 11, "span": [0, 86], "__ref_s3_data": null}], "text": "IBM DB2 for i supports nine different built-in global variables that are read only and", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 711.278, 342.519, 720.491], "page": 12, "span": [0, 51], "__ref_s3_data": null}], "text": "Table 3-2 lists the nine built-in global variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [64.8, 690.177, 201.196, 698.502], "page": 12, "span": [0, 37], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [64.8, 455.036, 384.345, 469.799], "page": 12, "span": [0, 35], "__ref_s3_data": null}], "text": "3.3 VERIFY_GROUP_FOR_USER function", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 428.258, 542.844, 437.471], "page": 12, "span": [0, 83], "__ref_s3_data": null}], "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 334.3, 525.156, 343.513], "page": 12, "span": [0, 96], "__ref_s3_data": null}], "text": "If a special register value is in the list of user profiles or it is a member of a group profile", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 288.28, 458.427, 297.493], "page": 12, "span": [0, 63], "__ref_s3_data": null}], "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 271.241, 145.089, 280.454], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "- 1.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 254.26099999999997, 145.089, 263.47399999999993], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "- 2.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 237.28099999999995, 145.099, 246.49400000000003], "page": 12, "span": [0, 2], "__ref_s3_data": null}], "text": "- 3.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.2, 208.606, 366.057, 217.00700000000006], "page": 12, "span": [0, 43], "__ref_s3_data": null}], "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [151.2, 184.60699999999997, 451.016, 193.00700000000006], "page": 12, "span": [0, 60], "__ref_s3_data": null}], "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.8, 711.642, 166.739, 720.043], "page": 13, "span": [0, 6], "__ref_s3_data": null}], "text": "RETURN", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 699.643, 156.779, 708.043], "page": 13, "span": [0, 4], "__ref_s3_data": null}], "text": "CASE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 687.643, 466.615, 696.043], "page": 13, "span": [0, 61], "__ref_s3_data": null}], "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.8, 507.281, 145.098, 516.494], "page": 13, "span": [0, 2], "__ref_s3_data": null}], "text": "- 2.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.039, 478.302, 157.577, 487.515], "page": 13, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.039, 461.262, 157.577, 470.475], "page": 13, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.039, 444.282, 157.577, 453.495], "page": 13, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.039, 415.303, 157.577, 424.516], "page": 13, "span": [0, 1], "__ref_s3_data": null}], "text": "- -", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.2, 398.263, 530.024, 407.476], "page": 13, "span": [0, 82], "__ref_s3_data": null}], "text": "- To implement this column mask, run the SQL statement that is shown in Example 3-9.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 359.623, 381.657, 368.023], "page": 13, "span": [0, 48], "__ref_s3_data": null}], "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.8, 377.157, 351.999, 385.482], "page": 13, "span": [0, 50], "__ref_s3_data": null}], "text": "Example 3-9 Creating a mask on the TAX_ID column", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 711.278, 145.089, 720.491], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "- 3.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [64.8, 577.637, 203.983, 589.625], "page": 14, "span": [0, 22], "__ref_s3_data": null}], "text": "3.6.6 Activating RCAC", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 554.258, 537.1, 563.471], "page": 14, "span": [0, 83], "__ref_s3_data": null}], "text": "Now that you have created the row permission and the two column masks, RCAC must be", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 513.279, 145.089, 522.492], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "- 1.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 492.117, 375.273, 500.442], "page": 14, "span": [0, 53], "__ref_s3_data": null}], "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 474.643, 376.677, 483.043], "page": 14, "span": [0, 48], "__ref_s3_data": null}], "text": "- /* Active Row Access Control (permissions) */", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 462.643, 376.677, 471.043], "page": 14, "span": [0, 48], "__ref_s3_data": null}], "text": "- /* Active Column Access Control (masks) */", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 426.644, 291.718, 435.044], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ACTIVATE COLUMN ACCESS CONTROL;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 402.279, 145.12, 411.492], "page": 14, "span": [0, 2], "__ref_s3_data": null}], "text": "- 2.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [136.8, 711.278, 145.088, 720.491], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "- 2.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 276.2180000000001, 145.089, 285.431], "page": 15, "span": [0, 2], "__ref_s3_data": null}], "text": "- 3.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"prov": [{"bbox": [64.8, 712.466, 177.119, 720.057], "page": 16, "span": [0, 24], "__ref_s3_data": null}], "text": "THEN C . CUSTOMER_TAX_ID", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [64.8, 228.442, 500.697, 236.03200000000004], "page": 16, "span": [0, 96], "__ref_s3_data": null}], "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [27.0, 688.992, 447.36, 718.362], "page": 18, "span": [0, 29], "__ref_s3_data": null}], "text": "Row and Column Access Control", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [26.7, 539.148, 127.444, 549.828], "page": 18, "span": [0, 19], "__ref_s3_data": null}], "text": "Implement roles and", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.94, 540.407, 414.461, 549.271], "page": 18, "span": [0, 70], "__ref_s3_data": null}], "text": "This IBM Redpaper publication provides information about the IBM i 7.2", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.7, 497.148, 93.971, 507.828], "page": 18, "span": [0, 12], "__ref_s3_data": null}], "text": "Leverage row", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.7, 441.168, 121.45, 451.848], "page": 18, "span": [0, 18], "__ref_s3_data": null}], "text": "Protect columns by", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.94, 451.428, 414.464, 460.293], "page": 18, "span": [0, 71], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"prov": [{"bbox": [467.34, 531.84, 559.801, 544.282], "page": 18, "span": [0, 13], "__ref_s3_data": null}], "text": "INTERNATIONAL", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.34, 429.528, 571.708, 440.208], "page": 18, "span": [0, 18], "__ref_s3_data": null}], "text": "BUILDING TECHNICAL", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.34, 382.368, 587.582, 392.14], "page": 18, "span": [0, 26], "__ref_s3_data": null}], "text": "IBM Redbooks are developed", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.34, 202.48800000000006, 570.948, 213.168], "page": 18, "span": [0, 21], "__ref_s3_data": null}], "text": "For more information:", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [{"prov": [{"bbox": [513.4560546875, 737.1808471679688, 586.1583251953125, 765.9149017333984], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [33.09040069580078, 89.5469970703125, 585.1502075195312, 498.9671630859375], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [316.9404296875, 17.5740966796875, 581.354736328125, 81.87213134765625], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [143.39866638183594, 506.378662109375, 179.56256103515625, 521.7389221191406], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.1669921875, 103.87176513671875, 258.7742919921875, 188.49365234375], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [142.52883911132812, 288.79351806640625, 251.47850036621094, 416.9550476074219], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [145.4144744873047, 156.616943359375, 252.08840942382812, 264.7552490234375], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [32.075252532958984, 554.0420684814453, 239.620361328125, 721.4226226806641], "page": 5, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.92466735839844, 103.39019775390625, 546.4456176757812, 416.0727844238281], "page": 7, "span": [0, 45], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.97177124023438, 381.39068603515625, 545.4180908203125, 684.5892486572266], "page": 10, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 3-1 CREATE PERMISSION SQL statement", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.64837646484375, 197.24334716796875, 301.2367248535156, 407.8262939453125], "page": 11, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.801902770996094, 621.9678497314453, 547.11474609375, 696.6175842285156], "page": 14, "span": [0, 54], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.985130310058594, 145.8603515625, 530.0478515625, 364.09503173828125], "page": 14, "span": [0, 67], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [136.5016632080078, 314.4587707519531, 545.4508666992188, 672.7508773803711], "page": 15, "span": [0, 46], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.27847290039062, 127.91290283203125, 506.39263916015625, 238.41851806640625], "page": 15, "span": [0, 39], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [485.1698303222656, 737.8084144592285, 566.2962036132812, 766.7408027648926], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [474.35540771484375, 602.1873931884766, 592.2726440429688, 711.9486846923828], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [136.1496124267578, 76.34844970703125, 547.5267944335938, 659.9669647216797], "page": 2, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table-of-contents", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [135.52462768554688, 349.949462890625, 545.8714599609375, 502.2747802734375], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [64.41139221191406, 70.39208984375, 547.3950805664062, 398.3863830566406], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "table", "payload": null, "#-cols": 0, "#-rows": 0, "data": [], "model": null, "bounding-box": null}, {"prov": [{"bbox": [134.5462646484375, 587.7283935546875, 542.0460815429688, 688.5811080932617], "page": 11, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding 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The following topics are covered in this chapter:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 253.06100000000004, 250.2, 262.274], "page": 5, "span": [0, 31], "__ref_s3_data": null}], "text": "- /SM590000 Security fundamentals", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 241.06100000000004, 282.955, 250.274], "page": 5, "span": [0, 41], "__ref_s3_data": null}], "text": "- /SM590000 Current state of IBM i security", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 229.06100000000004, 264.868, 238.274], "page": 5, "span": [0, 37], "__ref_s3_data": null}], "text": "- /SM590000 DB2 for i security controls", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 67.27999999999997, 258.332, 76.15999999999997], "page": 5, "span": [0, 30], "__ref_s3_data": null}], "text": "1 http://www.idtheftcenter.org", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [136.8, 57.02800000000002, 234.059, 66.42600000000004], "page": 5, "span": [0, 26], "__ref_s3_data": null}], "text": "2 http://www.ponemon.org /", "type": "footnote", "payload": null, "name": "Footnote", "font": null}, {"prov": [{"bbox": [64.8, 702.896, 267.39, 717.659], "page": 6, "span": [0, 26], "__ref_s3_data": null}], "text": "1.1 Security fundamentals", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 664.178, 545.014, 685.391], "page": 6, "span": [0, 133], "__ref_s3_data": null}], "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 611.139, 547.354, 656.513], "page": 6, "span": [0, 355], "__ref_s3_data": null}], "text": "- /SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199, 522.16, 547.57, 603.372], "page": 6, "span": [0, 587], "__ref_s3_data": null}], "text": "- The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.199, 505.18, 542.001, 514.393], "page": 6, "span": [0, 90], "__ref_s3_data": null}], "text": "A security policy is what defines whether the system and its settings are secure (or not).", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.799, 416.139, 547.407, 497.513], "page": 6, "span": [0, 567], "__ref_s3_data": null}], "text": "- /SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 382.18, 535.371, 403.393], "page": 6, "span": [0, 179], "__ref_s3_data": null}], "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.8, 338.936, 323.37, 353.699], "page": 6, "span": [0, 36], "__ref_s3_data": null}], "text": "1.2 Current state of IBM i security", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 276.159, 547.567, 321.371], "page": 6, "span": [0, 306], "__ref_s3_data": null}], "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 206.14, 547.593, 263.352], "page": 6, "span": [0, 405], "__ref_s3_data": null}], "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 112.12199999999996, 547.652, 193.33299999999997], "page": 6, "span": [0, 640], "__ref_s3_data": null}], "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 639.279, 544.312, 720.491], "page": 7, "span": [0, 589], "__ref_s3_data": null}], "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.8, 606.677, 301.46, 618.665], "page": 7, "span": [0, 38], "__ref_s3_data": null}], "text": "1.3.1 Existing row and column control", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 535.299, 541.576, 592.511], "page": 7, "span": [0, 377], "__ref_s3_data": null}], "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 477.28, 547.69, 522.492], "page": 7, "span": [0, 340], "__ref_s3_data": null}], "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 431.261, 547.541, 464.473], "page": 7, "span": [0, 247], "__ref_s3_data": null}], "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/8"}, {"prov": [{"bbox": [64.8, 708.677, 335.493, 720.665], "page": 8, "span": [0, 39], "__ref_s3_data": null}], "text": "2.1.6 Change Function Usage CL command", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 685.298, 547.251, 694.511], "page": 8, "span": [0, 90], "__ref_s3_data": null}], "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 668.259, 301.517, 677.472], "page": 8, "span": [0, 43], "__ref_s3_data": null}], "text": "- /SM590000 Work Function Usage ( WRKFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.801, 656.259, 313.398, 665.472], "page": 8, "span": [0, 45], "__ref_s3_data": null}], "text": "- /SM590000 Change Function Usage ( CHGFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.801, 644.259, 310.817, 653.472], "page": 8, "span": [0, 46], "__ref_s3_data": null}], "text": "- /SM590000 Display Function Usage ( DSPFCNUSG )", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 610.299, 512.547, 631.512], "page": 8, "span": [0, 126], "__ref_s3_data": null}], "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.801, 593.624, 441.597, 602.025], "page": 8, "span": [0, 61], "__ref_s3_data": null}], "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.8, 560.657, 544.461, 572.645], "page": 8, "span": [0, 73], "__ref_s3_data": null}], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 525.278, 519.527, 546.491], "page": 8, "span": [0, 130], "__ref_s3_data": null}], "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/1"}, {"prov": [{"bbox": [136.8, 504.117, 283.949, 512.442], "page": 8, "span": [0, 31], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 318.278, 547.589, 339.491], "page": 8, "span": [0, 112], "__ref_s3_data": null}], "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 297.117, 462.374, 305.442], "page": 8, "span": [0, 76], "__ref_s3_data": null}], "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 279.64300000000003, 251.699, 288.043], "page": 8, "span": [0, 23], "__ref_s3_data": null}], "text": "SELECT function_id,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 267.64300000000003, 241.739, 276.043], "page": 8, "span": [0, 10], "__ref_s3_data": null}], "text": "user_name,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 255.64300000000003, 221.699, 264.044], "page": 8, "span": [0, 6], "__ref_s3_data": null}], "text": "usage,", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 243.64300000000003, 236.699, 252.04399999999998], "page": 8, "span": [0, 9], "__ref_s3_data": null}], "text": "user_type", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 231.644, 261.718, 240.04399999999998], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "FROM function_usage", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 219.644, 331.677, 228.04399999999998], "page": 8, "span": [0, 39], "__ref_s3_data": null}], "text": "WHERE function_id='QIBM_DB_SECADM'", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 207.644, 241.739, 216.04399999999998], "page": 8, "span": [0, 21], "__ref_s3_data": null}], "text": "ORDER BY user_name;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [64.8, 157.01599999999996, 249.582, 171.779], "page": 8, "span": [0, 25], "__ref_s3_data": null}], "text": "2.2 Separation of duties", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 82.23900000000003, 547.592, 139.45100000000002], "page": 8, "span": [0, 463], "__ref_s3_data": null}], "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 651.279, 542.703, 720.491], "page": 9, "span": [0, 516], "__ref_s3_data": null}], "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 593.26, 547.673, 638.472], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 559.3, 538.66, 580.513], "page": 9, "span": [0, 129], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 513.281, 545.805, 546.494], "page": 9, "span": [0, 204], "__ref_s3_data": null}], "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 455.262, 539.816, 500.475], "page": 9, "span": [0, 285], "__ref_s3_data": null}], "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 421.303, 543.076, 442.515], "page": 9, "span": [0, 136], "__ref_s3_data": null}], "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/2"}, {"prov": [{"bbox": [64.8, 400.137, 391.767, 408.462], "page": 9, "span": [0, 81], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 699.278, 528.74, 720.491], "page": 10, "span": [0, 135], "__ref_s3_data": null}], "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/9"}, {"prov": [{"bbox": [136.8, 340.956, 215.383, 352.056], "page": 10, "span": [0, 11], "__ref_s3_data": null}], "text": "Column mask", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 291.699, 542.776, 336.911], "page": 10, "span": [0, 297], "__ref_s3_data": null}], "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 711.278, 412.174, 720.491], "page": 11, "span": [0, 62], "__ref_s3_data": null}], "text": "Table 3-1 summarizes these special registers and their values.", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/3"}, {"prov": [{"bbox": [136.8, 690.177, 372.618, 698.502], "page": 11, "span": [0, 60], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 556.298, 538.503, 577.511], "page": 11, "span": [0, 97], "__ref_s3_data": null}], "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 539.259, 411.326, 548.472], "page": 11, "span": [0, 69], "__ref_s3_data": null}], "text": "- /SM590000 A user connects to the server using the user profile ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 522.279, 453.245, 531.492], "page": 11, "span": [0, 71], "__ref_s3_data": null}], "text": "- /SM590000 USER and CURRENT USER initially have the same value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 493.299, 541.459, 514.512], "page": 11, "span": [0, 154], "__ref_s3_data": null}], "text": "- /SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 452.26, 547.584, 485.473], "page": 11, "span": [0, 247], "__ref_s3_data": null}], "text": "- /SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.801, 423.281, 547.483, 444.493], "page": 11, "span": [0, 127], "__ref_s3_data": null}], "text": "- /SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/10"}, {"prov": [{"bbox": [64.8, 154.457, 247.016, 166.44499999999994], "page": 11, "span": [0, 32], "__ref_s3_data": null}], "text": "3.2.2 Built-in global variables", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 119.07799999999997, 518.01, 140.29099999999994], "page": 11, "span": [0, 161], "__ref_s3_data": null}], "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 73.05899999999997, 532.347, 106.27200000000005], "page": 11, "span": [0, 233], "__ref_s3_data": null}], "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 711.278, 342.519, 720.491], "page": 12, "span": [0, 51], "__ref_s3_data": null}], "text": "Table 3-2 lists the nine built-in global variables.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Table", "type": "table", "$ref": "#/tables/4"}, {"prov": [{"bbox": [64.8, 690.177, 201.196, 698.502], "page": 12, "span": [0, 37], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [64.8, 455.036, 384.345, 469.799], "page": 12, "span": [0, 35], "__ref_s3_data": null}], "text": "3.3 VERIFY_GROUP_FOR_USER function", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 356.259, 547.543, 437.471], "page": 12, "span": [0, 576], "__ref_s3_data": null}], "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 310.3, 547.386, 343.513], "page": 12, "span": [0, 235], "__ref_s3_data": null}], "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 288.28, 458.427, 297.493], "page": 12, "span": [0, 63], "__ref_s3_data": null}], "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 271.241, 406.048, 280.454], "page": 12, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. There are user profiles for MGR, JANE, JUDY, and TONY.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 254.26099999999997, 396.957, 263.47399999999993], "page": 12, "span": [0, 58], "__ref_s3_data": null}], "text": "- 2. The user profile JANE specifies a group profile of MGR.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 225.28099999999995, 536.577, 246.49400000000003], "page": 12, "span": [0, 127], "__ref_s3_data": null}], "text": "- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.2, 150.64699999999993, 451.016, 217.00700000000006], "page": 12, "span": [0, 265], "__ref_s3_data": null}], "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.8, 711.642, 166.739, 720.043], "page": 13, "span": [0, 6], "__ref_s3_data": null}], "text": "RETURN", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 699.643, 156.779, 708.043], "page": 13, "span": [0, 4], "__ref_s3_data": null}], "text": "CASE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 531.645, 521.574, 696.043], "page": 13, "span": [0, 443], "__ref_s3_data": null}], "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.8, 495.281, 547.281, 516.494], "page": 13, "span": [0, 136], "__ref_s3_data": null}], "text": "- 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.039, 478.302, 469.118, 487.515], "page": 13, "span": [0, 62], "__ref_s3_data": null}], "text": "- -Human Resources can see the unmasked TAX_ID of the employees.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.039, 461.262, 403.932, 470.475], "page": 13, "span": [0, 50], "__ref_s3_data": null}], "text": "- -Employees can see only their own unmasked TAX_ID.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.039, 432.282, 545.118, 453.495], "page": 13, "span": [0, 129], "__ref_s3_data": null}], "text": "- -Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [152.039, 415.303, 529.442, 424.516], "page": 13, "span": [0, 77], "__ref_s3_data": null}], "text": "- -Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [151.2, 398.263, 530.024, 407.476], "page": 13, "span": [0, 82], "__ref_s3_data": null}], "text": "- To implement this column mask, run the SQL statement that is shown in Example 3-9.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 107.62699999999995, 526.555, 368.023], "page": 13, "span": [0, 607], "__ref_s3_data": null}], "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [136.8, 377.157, 351.999, 385.482], "page": 13, "span": [0, 50], "__ref_s3_data": null}], "text": "Example 3-9 Creating a mask on the TAX_ID column", "type": "caption", "payload": null, "name": "Caption", "font": null}, {"prov": [{"bbox": [136.8, 711.278, 449.917, 720.491], "page": 14, "span": [0, 65], "__ref_s3_data": null}], "text": "- 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/11"}, {"prov": [{"bbox": [64.8, 577.637, 203.983, 589.625], "page": 14, "span": [0, 22], "__ref_s3_data": null}], "text": "3.6.6 Activating RCAC", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 530.259, 547.209, 563.471], "page": 14, "span": [0, 265], "__ref_s3_data": null}], "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 513.279, 409.443, 522.492], "page": 14, "span": [0, 57], "__ref_s3_data": null}], "text": "- 1. Run the SQL statements that are shown in Example 3-10.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 492.117, 375.273, 500.442], "page": 14, "span": [0, 53], "__ref_s3_data": null}], "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [136.8, 474.643, 376.677, 483.043], "page": 14, "span": [0, 48], "__ref_s3_data": null}], "text": "- /* Active Row Access Control (permissions) */", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 462.643, 376.677, 471.043], "page": 14, "span": [0, 48], "__ref_s3_data": null}], "text": "- /* Active Column Access Control (masks) */", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 450.643, 291.718, 459.043], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 438.643, 271.678, 447.044], "page": 14, "span": [0, 27], "__ref_s3_data": null}], "text": "ACTIVATE ROW ACCESS CONTROL", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 426.644, 291.718, 435.044], "page": 14, "span": [0, 31], "__ref_s3_data": null}], "text": "ACTIVATE COLUMN ACCESS CONTROL;", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [136.8, 378.28, 540.81, 411.492], "page": 14, "span": [0, 231], "__ref_s3_data": null}], "text": "- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/12"}, {"prov": [{"bbox": [136.8, 687.278, 514.058, 720.491], "page": 15, "span": [0, 228], "__ref_s3_data": null}], "text": "- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"prov": [{"bbox": [136.8, 252.21900000000005, 547.308, 285.431], "page": 15, "span": [0, 232], "__ref_s3_data": null}], "text": "- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "type": "paragraph", "payload": null, "name": "List-item", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/13"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/14"}, {"prov": [{"bbox": [64.8, 85.46100000000001, 500.697, 720.057], "page": 16, "span": [0, 2007], "__ref_s3_data": null}], "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "type": "paragraph", "payload": null, "name": "Code", "font": null}, {"prov": [{"bbox": [287.22, 741.252, 414.264, 763.452], "page": 18, "span": [0, 10], "__ref_s3_data": null}], "text": "Back cover", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [27.0, 651.54, 447.36, 718.362], "page": 18, "span": [0, 54], "__ref_s3_data": null}], "text": "Row and Column Access Control Support in IBM DB2 for i", "type": "subtitle-level-1", "payload": null, "name": "Section-header", "font": null}, {"prov": [{"bbox": [26.7, 525.168, 127.444, 549.828], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Implement roles and separation of duties", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.7, 469.128, 120.283, 507.828], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Leverage row permissions on the database", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.94, 468.408, 414.461, 549.271], "page": 18, "span": [0, 464], "__ref_s3_data": null}], "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [26.7, 413.148, 121.45, 451.848], "page": 18, "span": [0, 40], "__ref_s3_data": null}], "text": "Protect columns by defining column masks", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [152.94, 403.429, 414.464, 460.293], "page": 18, "span": [0, 309], "__ref_s3_data": null}], "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"name": "Picture", "type": "figure", "$ref": "#/figures/15"}, {"name": "Picture", "type": "figure", "$ref": "#/figures/16"}, {"prov": [{"bbox": [467.34, 489.839, 559.801, 544.282], "page": 18, "span": [0, 44], "__ref_s3_data": null}], "text": "INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.34, 405.528, 587.779, 440.208], "page": 18, "span": [0, 60], "__ref_s3_data": null}], "text": "BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.34, 250.36599999999999, 587.755, 392.14], "page": 18, "span": [0, 323], "__ref_s3_data": null}], "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [467.34, 190.48800000000006, 570.948, 213.168], "page": 18, "span": [0, 39], "__ref_s3_data": null}], "text": "For more information: ibm.com /redbooks", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [{"prov": [{"bbox": [513.4560546875, 737.1808471679688, 586.1583251953125, 765.9149017333984], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [33.09040069580078, 89.5469970703125, 585.1502075195312, 498.9671630859375], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [316.9404296875, 17.5740966796875, 581.354736328125, 81.87213134765625], "page": 1, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [143.39866638183594, 506.378662109375, 179.56256103515625, 521.7389221191406], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.1669921875, 103.87176513671875, 258.7742919921875, 188.49365234375], "page": 3, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [142.52883911132812, 288.79351806640625, 251.47850036621094, 416.9550476074219], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [145.4144744873047, 156.616943359375, 252.08840942382812, 264.7552490234375], "page": 4, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [32.075252532958984, 554.0420684814453, 239.620361328125, 721.4226226806641], "page": 5, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.92466735839844, 103.39019775390625, 546.4456176757812, 416.0727844238281], "page": 7, "span": [0, 45], "__ref_s3_data": null}], "text": "Figure 1-2 Existing row and column controls", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.97177124023438, 381.39068603515625, 545.4180908203125, 684.5892486572266], "page": 10, "span": [0, 44], "__ref_s3_data": null}], "text": "Figure 3-1 CREATE PERMISSION SQL statement", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [135.64837646484375, 197.24334716796875, 301.2367248535156, 407.8262939453125], "page": 11, "span": [0, 52], "__ref_s3_data": null}], "text": "Figure 3-5 Special registers and adopted authority", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.801902770996094, 621.9678497314453, 547.11474609375, 696.6175842285156], "page": 14, "span": [0, 54], "__ref_s3_data": null}], "text": "Figure 3-10 Column masks shown in System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [63.985130310058594, 145.8603515625, 530.0478515625, 364.09503173828125], "page": 14, "span": [0, 67], "__ref_s3_data": null}], "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [136.5016632080078, 314.4587707519531, 545.4508666992188, 672.7508773803711], "page": 15, "span": [0, 46], "__ref_s3_data": null}], "text": "Figure 4-68 Visual Explain with RCAC enabled", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [64.27847290039062, 127.91290283203125, 506.39263916015625, 238.41851806640625], "page": 15, "span": [0, 39], "__ref_s3_data": null}], "text": "Figure 4-69 Index advice with no RCAC", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [485.1698303222656, 737.8084144592285, 566.2962036132812, 766.7408027648926], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}, {"prov": [{"bbox": [474.35540771484375, 602.1873931884766, 592.2726440429688, 711.9486846923828], "page": 18, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "figure", "payload": null, "bounding-box": null}], "tables": [{"prov": [{"bbox": [136.1496124267578, 76.34844970703125, 547.5267944335938, 659.9669647216797], "page": 2, "span": [0, 0], "__ref_s3_data": null}], "text": "", "type": "table-of-contents", "payload": null, "#-cols": 1, "#-rows": 41, "data": [[{"bbox": [136.8, 132.649, 547.19, 141.86199999999997], "spans": [[0, 0]], "text": "Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": true, "row-span": [0, 1]}], [{"bbox": [136.799, 145.12800000000004, 547.184, 154.341], "spans": [[1, 0]], "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": true, "row-span": [1, 2]}], [{"bbox": [136.799, 167.62800000000004, 547.199, 176.841], "spans": [[2, 0]], "text": "DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": true, "row-span": [2, 3]}], [{"bbox": [136.799, 190.12800000000004, 547.188, 199.341], "spans": [[3, 0]], "text": "Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": true, "row-span": [3, 4]}], [{"bbox": [136.798, 202.60799999999995, 547.188, 211.82100000000003], "spans": [[4, 0]], "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}], [{"bbox": [136.798, 215.14700000000005, 547.146, 224.36], "spans": [[5, 0]], "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}], [{"bbox": [136.798, 227.62699999999995, 547.189, 236.84000000000003], "spans": [[6, 0]], "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}], [{"bbox": [136.798, 240.10699999999997, 547.129, 249.32000000000005], "spans": [[7, 0]], "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": true, "row-span": [7, 8]}], [{"bbox": [136.798, 262.60699999999997, 547.207, 271.82000000000005], "spans": [[8, 0]], "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": true, "row-span": [8, 9]}], [{"bbox": [136.798, 274.60699999999997, 547.216, 283.82], "spans": [[9, 0]], "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": true, "row-span": [9, 10]}], [{"bbox": [136.798, 287.146, 547.201, 296.359], "spans": [[10, 0]], "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": true, "row-span": [10, 11]}], [{"bbox": [136.798, 299.626, 547.216, 308.839], "spans": [[11, 0]], "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": true, "row-span": [11, 12]}], [{"bbox": [151.197, 312.106, 547.156, 321.319], "spans": [[12, 0]], "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": true, "row-span": [12, 13]}], [{"bbox": [151.197, 324.646, 547.197, 333.859], "spans": [[13, 0]], "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 13, "row-header": true, "row-span": [13, 14]}], [{"bbox": [136.797, 347.145, 547.204, 356.358], "spans": [[14, 0]], "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 14, "row-header": true, "row-span": [14, 15]}], [{"bbox": [136.797, 359.145, 547.214, 368.358], "spans": [[15, 0]], "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 15, "row-header": true, "row-span": [15, 16]}], [{"bbox": [151.197, 371.625, 547.164, 380.838], "spans": [[16, 0]], "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 16, "row-header": true, "row-span": [16, 17]}], [{"bbox": [151.197, 384.105, 547.156, 393.318], "spans": [[17, 0]], "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 17, "row-header": true, "row-span": [17, 18]}], [{"bbox": [151.197, 396.644, 547.193, 405.857], "spans": [[18, 0]], "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 18, "row-header": true, "row-span": [18, 19]}], [{"bbox": [151.197, 409.124, 547.186, 418.337], "spans": [[19, 0]], "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 19, "row-header": true, "row-span": [19, 20]}], [{"bbox": [151.197, 421.604, 547.167, 430.817], "spans": [[20, 0]], "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 20, "row-header": true, "row-span": [20, 21]}], [{"bbox": [151.197, 434.144, 547.176, 443.357], "spans": [[21, 0]], "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 21, "row-header": true, "row-span": [21, 22]}], [{"bbox": [151.197, 446.624, 547.158, 455.837], "spans": [[22, 0]], "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 22, "row-header": true, "row-span": [22, 23]}], [{"bbox": [136.797, 459.104, 547.215, 468.317], "spans": [[23, 0]], "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 23, "row-header": true, "row-span": [23, 24]}], [{"bbox": [136.797, 481.603, 547.205, 490.816], "spans": [[24, 0]], "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 24, "row-header": true, "row-span": [24, 25]}], [{"bbox": [136.797, 493.603, 547.173, 502.816], "spans": [[25, 0]], "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 25, "row-header": true, "row-span": [25, 26]}], [{"bbox": [151.197, 506.143, 547.163, 515.356], "spans": [[26, 0]], "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 26, "row-header": true, "row-span": [26, 27]}], [{"bbox": [151.197, 518.623, 547.127, 527.836], "spans": [[27, 0]], "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 27, "row-header": true, "row-span": [27, 28]}], [{"bbox": [136.797, 531.162, 547.154, 540.375], "spans": [[28, 0]], "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 28, "row-header": true, "row-span": [28, 29]}], [{"bbox": [151.197, 543.642, 547.169, 552.855], "spans": [[29, 0]], "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 29, "row-header": true, "row-span": [29, 30]}], [{"bbox": [151.197, 556.1220000000001, 547.185, 565.335], "spans": [[30, 0]], "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 30, "row-header": true, "row-span": [30, 31]}], [{"bbox": [136.797, 568.662, 547.129, 577.875], "spans": [[31, 0]], "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 31, "row-header": true, "row-span": [31, 32]}], [{"bbox": [136.797, 581.141, 547.221, 590.354], "spans": [[32, 0]], "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text. . . . . . . . . . . . . 21", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 32, "row-header": true, "row-span": [32, 33]}], [{"bbox": [136.797, 606.1610000000001, 547.215, 615.374], "spans": [[33, 0]], "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 33, "row-header": true, "row-span": [33, 34]}], [{"bbox": [151.197, 618.641, 547.162, 627.854], "spans": [[34, 0]], "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . . 23", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 34, "row-header": true, "row-span": [34, 35]}], [{"bbox": [151.197, 631.121, 547.168, 640.3340000000001], "spans": [[35, 0]], "text": "3.6.2 Creating group profiles for the users and their roles. . . . . . . . . . . . . . . . . . . . . . . 23", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 35, "row-header": true, "row-span": [35, 36]}], [{"bbox": [151.197, 643.66, 547.158, 652.873], "spans": [[36, 0]], "text": "3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 36, "row-header": true, "row-span": [36, 37]}], [{"bbox": [151.197, 656.14, 547.16, 665.353], "spans": [[37, 0]], "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 37, "row-header": true, "row-span": [37, 38]}], [{"bbox": [151.197, 668.62, 547.17, 677.833], "spans": [[38, 0]], "text": "3.6.5 Defining and creating column masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 38, "row-header": true, "row-span": [38, 39]}], [{"bbox": [151.197, 681.16, 547.189, 690.373], "spans": [[39, 0]], "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 39, "row-header": true, "row-span": [39, 40]}], [{"bbox": [151.197, 706.12, 547.13, 715.333], "spans": [[40, 0]], "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 40, "row-header": true, "row-span": [40, 41]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [135.52462768554688, 349.949462890625, 545.8714599609375, 502.2747802734375], "page": 8, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-1 FUNCTION_USAGE view", "type": "table", "payload": null, "#-cols": 3, "#-rows": 5, "data": [[{"bbox": [142.8, 296.538, 202.253, 304.863], "spans": [[0, 0]], "text": "Column name", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [216.809, 296.538, 257.219, 304.863], "spans": [[0, 1]], "text": "Data type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [289.475, 296.538, 338.903, 304.863], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.8, 315.558, 203.243, 323.883], "spans": [[1, 0]], "text": "FUNCTION_ID", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [216.785, 315.558, 276.014, 323.883], "spans": [[1, 1]], "text": "VARCHAR(30)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [289.458, 315.558, 359.865, 323.883], "spans": [[1, 2]], "text": "ID of the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.8, 334.518, 198.685, 342.843], "spans": [[2, 0]], "text": "USER_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [216.741, 334.518, 275.939, 342.843], "spans": [[2, 1]], "text": "VARCHAR(10)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [289.382, 334.518, 515.035, 353.883], "spans": [[2, 2]], "text": "Name of the user profile that has a usage setting for this function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.8, 364.519, 173.997, 372.844], "spans": [[3, 0]], "text": "USAGE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [216.774, 364.519, 270.993, 372.844], "spans": [[3, 1]], "text": "VARCHAR(7)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [289.416, 364.519, 539.121, 394.864], "spans": [[3, 2]], "text": "Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [142.8, 405.559, 196.235, 413.884], "spans": [[4, 0]], "text": "USER_TYPE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [216.752, 405.559, 271.009, 413.884], "spans": [[4, 1]], "text": "VARCHAR(5)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [289.432, 405.559, 448.132, 435.844], "spans": [[4, 2]], "text": "Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group.", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [64.41139221191406, 70.39208984375, 547.3950805664062, 398.3863830566406], "page": 9, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "type": "table", "payload": null, "#-cols": 6, "#-rows": 13, "data": [[{"bbox": [70.8, 400.518, 119.758, 408.843], "spans": [[0, 0]], "text": "User action", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [424.938, 447.561, 433.263, 487.02], "spans": [[0, 1]], "text": "*JOBCTL", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [450.138, 401.586, 458.463, 487.02], "spans": [[0, 2]], "text": "QIBM_DB_SECADM", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [475.938, 401.563, 484.263, 487.02], "spans": [[0, 3]], "text": "QIBM_DB_SQLADM", "type": "col_header", "col": 3, "col-header": true, "col-span": [3, 4], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [501.138, 401.611, 509.463, 487.02], "spans": [[0, 4]], "text": "QIBM_DB_SYSMON", "type": "col_header", "col": 4, "col-header": true, "col-span": [4, 5], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [526.399, 432.805, 534.724, 487.02], "spans": [[0, 5]], "text": "No Authority", "type": "col_header", "col": 5, "col-header": true, "col-span": [5, 6], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.8, 498.558, 220.133, 506.883], "spans": [[1, 0]], "text": "SET CURRENT DEGREE (SQL statement)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": true, "row-span": [1, 2]}, {"bbox": [429.0, 498.558, 435.003, 506.883], "spans": [[1, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 498.558, 486.003, 506.883], "spans": [[1, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": null, "spans": [[1, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[1, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 517.518, 264.564, 525.8430000000001], "spans": [[2, 0]], "text": "CHGQRYA command targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": true, "row-span": [2, 3]}, {"bbox": [429.0, 517.518, 435.003, 525.8430000000001], "spans": [[2, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 517.518, 486.003, 525.8430000000001], "spans": [[2, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": null, "spans": [[2, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[2, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 536.538, 322.516, 544.863], "spans": [[3, 0]], "text": "STRDBMON or ENDDBMON commands targeting a different user's job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": true, "row-span": [3, 4]}, {"bbox": [429.0, 536.538, 435.003, 544.863], "spans": [[3, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 536.538, 486.003, 544.863], "spans": [[3, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": null, "spans": [[3, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[3, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 555.558, 381.0, 563.883], "spans": [[4, 0]], "text": "STRDBMON or ENDDBMON commands targeting a job that matches the current user", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": true, "row-span": [4, 5]}, {"bbox": [429.0, 555.558, 435.003, 563.883], "spans": [[4, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": null, "spans": [[4, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 555.558, 486.003, 563.883], "spans": [[4, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [505.261, 555.558, 511.264, 563.883], "spans": [[4, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [530.76, 555.558, 536.763, 563.883], "spans": [[4, 5]], "text": "X", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [70.8, 574.518, 359.528, 582.843], "spans": [[5, 0]], "text": "QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": true, "row-span": [5, 6]}, {"bbox": [429.0, 574.518, 435.003, 582.843], "spans": [[5, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 574.518, 486.003, 582.843], "spans": [[5, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [505.261, 574.518, 511.264, 582.843], "spans": [[5, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": null, "spans": [[5, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 593.538, 220.766, 601.863], "spans": [[6, 0]], "text": "Visual Explain within Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": true, "row-span": [6, 7]}, {"bbox": [429.0, 593.538, 435.003, 601.863], "spans": [[6, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": null, "spans": [[6, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 593.538, 486.003, 601.863], "spans": [[6, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [505.261, 593.538, 511.264, 601.863], "spans": [[6, 4]], "text": "X", "type": "body", "col": 4, "col-header": false, "col-span": [4, 5], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [530.76, 593.538, 536.763, 601.863], "spans": [[6, 5]], "text": "X", "type": "body", "col": 5, "col-header": false, "col-span": [5, 6], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.8, 612.557, 236.674, 620.8820000000001], "spans": [[7, 0]], "text": "Visual Explain outside of Run SQL scripts", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": true, "row-span": [7, 8]}, {"bbox": [429.0, 612.557, 435.003, 620.8820000000001], "spans": [[7, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 612.557, 486.003, 620.8820000000001], "spans": [[7, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": null, "spans": [[7, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[7, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 631.518, 213.135, 639.843], "spans": [[8, 0]], "text": "ANALYZE PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": true, "row-span": [8, 9]}, {"bbox": [429.0, 631.518, 435.003, 639.843], "spans": [[8, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 631.518, 486.003, 639.843], "spans": [[8, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": null, "spans": [[8, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[8, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 650.537, 199.889, 658.862], "spans": [[9, 0]], "text": "DUMP PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": true, "row-span": [9, 10]}, {"bbox": [429.0, 650.537, 435.003, 658.862], "spans": [[9, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 650.537, 486.003, 658.862], "spans": [[9, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": null, "spans": [[9, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[9, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 669.557, 208.372, 677.8820000000001], "spans": [[10, 0]], "text": "MODIFY PLAN CACHE procedure", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 10, "row-header": true, "row-span": [10, 11]}, {"bbox": [429.0, 669.557, 435.003, 677.8820000000001], "spans": [[10, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 669.557, 486.003, 677.8820000000001], "spans": [[10, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 10, "row-header": false, "row-span": [10, 11]}, {"bbox": null, "spans": [[10, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[10, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 688.577, 411.212, 696.902], "spans": [[11, 0]], "text": "MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 11, "row-header": true, "row-span": [11, 12]}, {"bbox": [429.0, 688.577, 435.003, 696.902], "spans": [[11, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 688.577, 486.003, 696.902], "spans": [[11, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 11, "row-header": false, "row-span": [11, 12]}, {"bbox": null, "spans": [[11, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[11, 5]], "text": "", "type": "body"}], [{"bbox": [70.8, 707.537, 377.136, 715.862], "spans": [[12, 0]], "text": "CHANGE PLAN CACHE SIZE procedure (currently does not check authority)", "type": "row_header", "col": 0, "col-header": false, "col-span": [0, 1], "row": 12, "row-header": true, "row-span": [12, 13]}, {"bbox": [429.0, 707.537, 435.003, 715.862], "spans": [[12, 1]], "text": "X", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 2]], "text": "", "type": "body"}, {"bbox": [480.0, 707.537, 486.003, 715.862], "spans": [[12, 3]], "text": "X", "type": "body", "col": 3, "col-header": false, "col-span": [3, 4], "row": 12, "row-header": false, "row-span": [12, 13]}, {"bbox": null, "spans": [[12, 4]], "text": "", "type": "body"}, {"bbox": null, "spans": [[12, 5]], "text": "", "type": "body"}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [134.5462646484375, 587.7283935546875, 542.0460815429688, 688.5811080932617], "page": 11, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-1 Special registers and their corresponding values", "type": "table", "payload": null, "#-cols": 2, "#-rows": 4, "data": [[{"bbox": [142.8, 110.53800000000001, 209.69, 118.86300000000006], "spans": [[0, 0]], "text": "Special register", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [230.189, 110.53800000000001, 319.933, 118.86300000000006], "spans": [[0, 1]], "text": "Corresponding value", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [142.8, 129.49800000000005, 212.711, 148.86400000000003], "spans": [[1, 0]], "text": "USER or SESSION_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [230.22, 129.49800000000005, 468.01, 137.82299999999998], "spans": [[1, 1]], "text": "The effective user of the thread excluding adopted authority.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [142.8, 159.558, 216.656, 167.88300000000004], "spans": [[2, 0]], "text": "CURRENT_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [230.198, 159.558, 536.173, 178.86300000000006], "spans": [[2, 1]], "text": "The effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [142.801, 189.558, 209.743, 197.88300000000004], "spans": [[3, 0]], "text": "SYSTEM_USER", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [230.245, 189.558, 425.656, 197.88300000000004], "spans": [[3, 1]], "text": "The authorization ID that initiated the connection.", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}]], "model": null, "bounding-box": null}, {"prov": [{"bbox": [63.55636978149414, 495.77532958984375, 548.5687255859375, 687.7661285400391], "page": 12, "span": [0, 0], "__ref_s3_data": null}], "text": "Table 3-2 Built-in global variables", "type": "table", "payload": null, "#-cols": 3, "#-rows": 10, "data": [[{"bbox": [70.8, 110.53800000000001, 134.993, 118.86300000000006], "spans": [[0, 0]], "text": "Global variable", "type": "col_header", "col": 0, "col-header": true, "col-span": [0, 1], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [202.889, 110.53800000000001, 223.349, 118.86300000000006], "spans": [[0, 1]], "text": "Type", "type": "col_header", "col": 1, "col-header": true, "col-span": [1, 2], "row": 0, "row-header": false, "row-span": [0, 1]}, {"bbox": [281.825, 110.53800000000001, 331.345, 118.86300000000006], "spans": [[0, 2]], "text": "Description", "type": "col_header", "col": 2, "col-header": true, "col-span": [2, 3], "row": 0, "row-header": false, "row-span": [0, 1]}], [{"bbox": [70.8, 129.49800000000005, 132.729, 137.82299999999998], "spans": [[1, 0]], "text": "CLIENT_HOST", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [202.89, 129.49800000000005, 267.085, 137.82299999999998], "spans": [[1, 1]], "text": "VARCHAR(255)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 1, "row-header": false, "row-span": [1, 2]}, {"bbox": [281.847, 129.49800000000005, 510.176, 137.82299999999998], "spans": [[1, 2]], "text": "Host name of the current client as returned by the system", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 1, "row-header": false, "row-span": [1, 2]}], [{"bbox": [70.8, 148.51800000000003, 140.671, 156.84299999999996], "spans": [[2, 0]], "text": "CLIENT_IPADDR", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [202.872, 148.51800000000003, 267.083, 156.84299999999996], "spans": [[2, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 2, "row-header": false, "row-span": [2, 3]}, {"bbox": [281.845, 148.51800000000003, 509.611, 156.84299999999996], "spans": [[2, 2]], "text": "IP address of the current client as returned by the system", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 2, "row-header": false, "row-span": [2, 3]}], [{"bbox": [70.8, 167.538, 134.965, 175.86300000000006], "spans": [[3, 0]], "text": "CLIENT_PORT", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [202.903, 167.538, 242.816, 175.86300000000006], "spans": [[3, 1]], "text": "INTEGER", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 3, "row-header": false, "row-span": [3, 4]}, {"bbox": [281.798, 167.538, 527.608, 175.86300000000006], "spans": [[3, 2]], "text": "Port used by the current client to communicate with the server", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 3, "row-header": false, "row-span": [3, 4]}], [{"bbox": [70.8, 186.55700000000002, 143.529, 194.88199999999995], "spans": [[4, 0]], "text": "PACKAGE_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [202.806, 186.55700000000002, 267.089, 194.88199999999995], "spans": [[4, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 4, "row-header": false, "row-span": [4, 5]}, {"bbox": [281.852, 186.55700000000002, 436.592, 194.88199999999995], "spans": [[4, 2]], "text": "Name of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 4, "row-header": false, "row-span": [4, 5]}], [{"bbox": [70.8, 205.51800000000003, 156.038, 213.84299999999996], "spans": [[5, 0]], "text": "PACKAGE_SCHEMA", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [202.835, 205.51800000000003, 267.108, 213.84299999999996], "spans": [[5, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 5, "row-header": false, "row-span": [5, 6]}, {"bbox": [281.871, 205.51800000000003, 470.468, 213.84299999999996], "spans": [[5, 2]], "text": "Schema name of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 5, "row-header": false, "row-span": [5, 6]}], [{"bbox": [70.8, 224.53700000000003, 157.923, 232.86199999999997], "spans": [[6, 0]], "text": "PACKAGE_VERSION", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [202.725, 224.53700000000003, 262.006, 232.86199999999997], "spans": [[6, 1]], "text": "VARCHAR(64)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 6, "row-header": false, "row-span": [6, 7]}, {"bbox": [281.749, 224.53700000000003, 478.859, 232.86199999999997], "spans": [[6, 2]], "text": "Version identifier of the currently running package", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 6, "row-header": false, "row-span": [6, 7]}], [{"bbox": [70.8, 243.55700000000002, 154.435, 251.88199999999995], "spans": [[7, 0]], "text": "ROUTINE_SCHEMA", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [202.793, 243.55700000000002, 267.108, 251.88199999999995], "spans": [[7, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 7, "row-header": false, "row-span": [7, 8]}, {"bbox": [281.872, 243.55700000000002, 464.275, 251.88199999999995], "spans": [[7, 2]], "text": "Schema name of the currently running routine", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 7, "row-header": false, "row-span": [7, 8]}], [{"bbox": [70.8, 262.51700000000005, 188.447, 270.842], "spans": [[8, 0]], "text": "ROUTINE_SPECIFIC_NAME", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [202.844, 262.51700000000005, 267.044, 270.842], "spans": [[8, 1]], "text": "VARCHAR(128)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 8, "row-header": false, "row-span": [8, 9]}, {"bbox": [281.807, 262.51700000000005, 430.403, 270.842], "spans": [[8, 2]], "text": "Name of the currently running routine", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 8, "row-header": false, "row-span": [8, 9]}], [{"bbox": [70.8, 281.537, 139.449, 289.862], "spans": [[9, 0]], "text": "ROUTINE_TYPE", "type": "body", "col": 0, "col-header": false, "col-span": [0, 1], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [202.746, 281.537, 239.308, 289.862], "spans": [[9, 1]], "text": "CHAR(1)", "type": "body", "col": 1, "col-header": false, "col-span": [1, 2], "row": 9, "row-header": false, "row-span": [9, 10]}, {"bbox": [281.791, 281.537, 425.109, 289.862], "spans": [[9, 2]], "text": "Type of the currently running routine", "type": "body", "col": 2, "col-header": false, "col-span": [2, 3], "row": 9, "row-header": false, "row-span": [9, 10]}]], "model": null, "bounding-box": null}], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 792.0, "page": 1, "width": 612.0}, {"height": 792.0, "page": 2, "width": 612.0}, {"height": 792.0, "page": 3, "width": 612.0}, {"height": 792.0, "page": 4, "width": 612.0}, {"height": 792.0, "page": 5, "width": 612.0}, {"height": 792.0, "page": 6, "width": 612.0}, {"height": 792.0, "page": 7, "width": 612.0}, {"height": 792.0, "page": 8, "width": 612.0}, {"height": 792.0, "page": 9, "width": 612.0}, {"height": 792.0, "page": 10, "width": 612.0}, {"height": 792.0, "page": 11, "width": 612.0}, {"height": 792.0, "page": 12, "width": 612.0}, {"height": 792.0, "page": 13, "width": 612.0}, {"height": 792.0, "page": 14, "width": 612.0}, {"height": 792.0, "page": 15, "width": 612.0}, {"height": 792.0, "page": 16, "width": 612.0}, {"height": 792.0, "page": 17, "width": 612.0}, {"height": 792.0, "page": 18, "width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.md b/tests/data/groundtruth/docling_v1/redp5110_sampled.md index 436e6434..88140d92 100644 --- a/tests/data/groundtruth/docling_v1/redp5110_sampled.md +++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.md @@ -1,6 +1,8 @@ +Front cover + -## Row and Column Access Control +## Row and Column Access Control Support in IBM DB2 for i @@ -8,51 +10,75 @@ ## Contents -Solution Brief +DB2 for i Center of Excellence + +Solution Brief IBM Systems Lab Services and Training ## Highlights -- /g115/g3 +- /g115/g3 /g40/g81/g75/g68/g81/g70/g72/g3 /g87/g75/g72/g3 /g83/g72/g85/g73/g82/g85/g80/g68/g81/g70/g72/g3 /g82/g73/g3 /g92/g82/g88/g85/g3 /g71/g68/g87/g68/g69/g68/g86/g72/g3 /g82/g83/g72/g85/g68/g87/g76/g82/g81/g86 + +- /g115/g3 /g40/g68/g85/g81/g3 /g74/g85/g72/g68/g87/g72/g85/g3 /g85 /g72/g87/g88/g85/g81/g3 /g82/g81/g3 /g44/g55/g3 /g83/g85 /g82/g77/g72/g70/g87/g86/g3 /g87/g75/g85 /g82/g88/g74/g75/g3 /g80/g82/g71/g72/g85/g81/g76/g93/g68/g87/g76/g82/g81/g3 /g82/g73/g3 /g71/g68/g87/g68/g69/g68/g86/g72/g3 /g68/g81/g71/g3 /g68/g83/g83/g79/g76/g70/g68/g87/g76/g82/g81/g86 + +- /g115/g3 /g53/g72/g79/g92/g3 /g82/g81/g3 /g44/g37/g48/g3 /g72/g91/g83/g72/g85/g87/g3 /g70/g82/g81/g86/g88/g79/g87/g76/g81/g74/g15/g3 /g86/g78/g76/g79/g79/g86/g3 /g86/g75/g68/g85/g76/g81/g74/g3 /g68/g81/g71/g3 /g85/g72/g81/g82/g90/g81/g3 /g86/g72/g85/g89/g76/g70/g72/g86 + +- /g115/g3 /g55 /g68/g78/g72/g3 /g68/g71/g89/g68/g81/g87/g68/g74/g72/g3 /g82/g73/g3 /g68/g70/g70/g72/g86/g86/g3 /g87/g82/g3 /g68/g3 /g90/g82/g85/g79/g71/g90/g76/g71/g72/g3 /g86/g82/g88/g85/g70/g72/g3 /g82/g73/g3 /g72/g91/g83/g72/g85/g87/g76/g86/g72 Power Services -## DB2 for i +## DB2 for i Center of Excellence Expert help to achieve your business requirements ## We build confident, satisfied clients -No one else has the vast consulting experiences, skills sharing and +No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you. Because no one else is IBM. -With combined experiences and direct access to development groups, +With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. ## Who we are, some of what we do Global CoE engagements cover topics including: -- rglyph +- rglyph Database performance and scalability + +- rglyph Advanced SQL knowledge and skills transfer + +- rglyph Business intelligence and analytics + +- rglyph DB2 Web Query + +- rglyph Query/400 modernization for better reporting and analysis capabilities + +- rglyph Database modernization and re-engineering + +- rglyph Data-centric architecture and design + +- rglyph Extremely large database and overcoming limits to growth + +- rglyph ISV education and enablement ## Preface -This IBMfi Redpaper™ publication provides information about the IBM i 7.2 feature of IBM +This IBMfi Redpaper™ publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. -This paper is intended for database engineers, data-centric application developers, and +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. -This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with +This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US. -Jim Bainbridge +Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office. -Hernando Bedoya +Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com . ## Authors @@ -60,47 +86,53 @@ Hernando Bedoya 1 -## Securing and protecting IBM DB2 +Chapter 1. -Recent news headlines are filled with reports of data breaches and cyber-attacks impacting +## Securing and protecting IBM DB2 data -Businesses must make a serious effort to secure their data and recognize that securing +Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. -This chapter describes how you can secure and protect data in DB2 for i. The following topics +Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement. -- /SM590000 +This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter: + +- /SM590000 Security fundamentals + +- /SM590000 Current state of IBM i security + +- /SM590000 DB2 for i security controls ## 1.1 Security fundamentals -Before reviewing database security techniques, there are two fundamental steps in securing +Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described: -- /SM590000 +- /SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. -- The monitoring and assessment of adherence to the security policy determines whether +- The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. A security policy is what defines whether the system and its settings are secure (or not). -- /SM590000 +- /SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. -With your eyes now open to the importance of securing information assets, the rest of this +With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i. ## 1.2 Current state of IBM i security -Because of the inherently secure nature of IBM i, many clients rely on the default system +Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE. -Even more disturbing is that many IBM i clients remain in this state, despite the news +Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data. -Traditionally, IBM i applications have employed menu-based security to counteract this default +Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data. -Many businesses are trying to limit data access to a need-to-know basis. This security goal +Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage. ## 1.3.1 Existing row and column control -Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL +Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator. -Using SQL views to limit access to a subset of the data in a table also has its own set of +Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases. -Even if you are willing to live with these performance and management issues, a user with +Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view. Figure 1-2 Existing row and column controls @@ -109,26 +141,39 @@ Figure 1-2 Existing row and column controls The following CL commands can be used to work with, display, or change function usage IDs: -- /SM590000 +- /SM590000 Work Function Usage ( WRKFCNUSG ) -For example, the following +- /SM590000 Change Function Usage ( CHGFCNUSG ) + +- /SM590000 Display Function Usage ( DSPFCNUSG ) + +For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules: CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED) ## 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view -The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 +The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view. Table 2-1 FUNCTION_USAGE view +| Column name | Data type | Description | +|---------------|-------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| +| FUNCTION_ID | VARCHAR(30) | ID of the function. | +| USER_NAME | VARCHAR(10) | Name of the user profile that has a usage setting for this function. | +| USAGE | VARCHAR(7) | Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function. | +| USER_TYPE | VARCHAR(5) | Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group. | - -To discover who has authorization to define and manage RCAC, you can use the query that is +To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. Example 2-1 Query to determine who has authority to define and manage RCAC SELECT function_id, +user_name, + +usage, + user_type FROM function_usage @@ -139,102 +184,141 @@ ORDER BY user_name; ## 2.2 Separation of duties -Separation of duties helps businesses comply with industry regulations or organizational +Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority. -For example, assume that a business has assigned the duty to manage security on IBM i to +For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security. -In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, +In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table. -QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special +QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group. -QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows +QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table. -A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function +A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself. -Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to +Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools. Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority +| User action | *JOBCTL | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | No Authority | +|--------------------------------------------------------------------------------|-----------|------------------|------------------|------------------|----------------| +| SET CURRENT DEGREE (SQL statement) | X | | X | | | +| CHGQRYA command targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a job that matches the current user | X | | X | X | X | +| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job | X | | X | X | | +| Visual Explain within Run SQL scripts | X | | X | X | X | +| Visual Explain outside of Run SQL scripts | X | | X | | | +| ANALYZE PLAN CACHE procedure | X | | X | | | +| DUMP PLAN CACHE procedure | X | | X | | | +| MODIFY PLAN CACHE procedure | X | | X | | | +| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) | X | | X | | | +| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) | X | | X | | | - -The SQL +The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules. Figure 3-1 CREATE PERMISSION SQL statement ## Column mask -A column mask is a database object that manifests a column value access control rule for a +A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number. Table 3-1 summarizes these special registers and their values. Table 3-1 Special registers and their corresponding values +| Special register | Corresponding value | +|----------------------|---------------------------------------------------------------------------------------------------------------------------------------| +| USER or SESSION_USER | The effective user of the thread excluding adopted authority. | +| CURRENT_USER | The effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER. | +| SYSTEM_USER | The authorization ID that initiated the connection. | +Figure 3-5 shows the difference in the special register values when an adopted authority is used: -Figure 3-5 shows the difference in the special register values when an adopted authority is +- /SM590000 A user connects to the server using the user profile ALICE. -- /SM590000 +- /SM590000 USER and CURRENT USER initially have the same value of ALICE. + +- /SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called. + +- /SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority. + +- /SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE. Figure 3-5 Special registers and adopted authority ## 3.2.2 Built-in global variables -Built-in global variables are provided with the database manager and are used in SQL +Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables. -IBM DB2 for i supports nine different built-in global variables that are read only and +IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic. Table 3-2 lists the nine built-in global variables. Table 3-2 Built-in global variables - +| Global variable | Type | Description | +|-----------------------|--------------|----------------------------------------------------------------| +| CLIENT_HOST | VARCHAR(255) | Host name of the current client as returned by the system | +| CLIENT_IPADDR | VARCHAR(128) | IP address of the current client as returned by the system | +| CLIENT_PORT | INTEGER | Port used by the current client to communicate with the server | +| PACKAGE_NAME | VARCHAR(128) | Name of the currently running package | +| PACKAGE_SCHEMA | VARCHAR(128) | Schema name of the currently running package | +| PACKAGE_VERSION | VARCHAR(64) | Version identifier of the currently running package | +| ROUTINE_SCHEMA | VARCHAR(128) | Schema name of the currently running routine | +| ROUTINE_SPECIFIC_NAME | VARCHAR(128) | Name of the currently running routine | +| ROUTINE_TYPE | CHAR(1) | Type of the currently running routine | ## 3.3 VERIFY_GROUP_FOR_USER function -The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily +The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error. -If a special register value is in the list of user profiles or it is a member of a group profile +If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value. Here is an example of using the VERIFY_GROUP_FOR_USER function: -- 1. +- 1. There are user profiles for MGR, JANE, JUDY, and TONY. -- 2. +- 2. The user profile JANE specifies a group profile of MGR. -- 3. +- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1: -VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') - -VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') +VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY') RETURN CASE -WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 +WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ; -- 2. +- 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones: -- - +- -Human Resources can see the unmasked TAX_ID of the employees. + +- -Employees can see only their own unmasked TAX_ID. + +- -Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234). + +- -Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX. - To implement this column mask, run the SQL statement that is shown in Example 3-9. -CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES +CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; Example 3-9 Creating a mask on the TAX_ID column -- 3. +- 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA. Figure 3-10 Column masks shown in System i Navigator ## 3.6.6 Activating RCAC -Now that you have created the row permission and the two column masks, RCAC must be +Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps: -- 1. +- 1. Run the SQL statements that are shown in Example 3-10. ## Example 3-10 Activating RCAC on the EMPLOYEES table @@ -242,14 +326,20 @@ Now that you have created the row permission and the two column masks, RCAC must - /* Active Column Access Control (masks) */ +ALTER TABLE HR_SCHEMA.EMPLOYEES + +ACTIVATE ROW ACCESS CONTROL + ACTIVATE COLUMN ACCESS CONTROL; -- 2. +- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables , right-click the EMPLOYEES table, and click Definition . Figure 3-11 Selecting the EMPLOYEES table from System i Navigator -- 3. +- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. + +- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause. Figure 4-68 Visual Explain with RCAC enabled @@ -257,30 +347,30 @@ Figure 4-68 Visual Explain with RCAC enabled Figure 4-69 Index advice with no RCAC -THEN C . CUSTOMER_TAX_ID +THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ; -CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C +Back cover -## Row and Column Access Control +## Row and Column Access Control Support in IBM DB2 for i -Implement roles and +Implement roles and separation of duties -This IBM Redpaper publication provides information about the IBM i 7.2 +Leverage row permissions on the database -Leverage row +This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. -Protect columns by +Protect columns by defining column masks -This paper is intended for database engineers, data-centric application +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. -INTERNATIONAL +INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION -BUILDING TECHNICAL +BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE -IBM Redbooks are developed +IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment. -For more information: \ No newline at end of file +For more information: ibm.com /redbooks \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json b/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json index 3925de2b..ef7b4b5a 100644 --- a/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json +++ b/tests/data/groundtruth/docling_v1/redp5110_sampled.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.9, "r_y0": 765.105, "r_x1": 98.598, "r_y1": 765.105, "r_x2": 98.598, "r_y2": 751.23, "r_x3": 36.9, "r_y3": 751.23, "coord_origin": "TOPLEFT"}, "text": "ibm.com", "orig": "ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "iii", "orig": "iii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 168.742, "r_y1": 96.048, "r_x2": 168.742, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Contents", "orig": "Contents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 172.877, "r_y1": 141.86199999999997, "r_x2": 172.877, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 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"coord_origin": "TOPLEFT"}, "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "orig": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 176.841, "r_x1": 279.358, "r_y1": 176.841, "r_x2": 279.358, "r_y2": 167.62800000000004, "r_x3": 136.799, "r_y3": 167.62800000000004, "coord_origin": "TOPLEFT"}, "text": "DB2 for i Center of Excellence", "orig": "DB2 for i Center of Excellence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.619, "r_y0": 176.841, "r_x1": 547.199, "r_y1": 176.841, "r_x2": 547.199, "r_y2": 167.62800000000004, "r_x3": 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 249.32000000000005, "r_x1": 547.129, "r_y1": 249.32000000000005, "r_x2": 547.129, "r_y2": 240.10699999999997, "r_x3": 136.798, "r_y3": 240.10699999999997, "coord_origin": "TOPLEFT"}, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "orig": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 271.82000000000005, "r_x1": 373.148, "r_y1": 271.82000000000005, "r_x2": 373.148, 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. Roles and separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.997, "r_y0": 356.358, "r_x1": 547.204, "r_y1": 356.358, "r_x2": 547.204, "r_y2": 347.145, "r_x3": 338.997, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 368.358, "r_x1": 547.214, "r_y1": 368.358, "r_x2": 547.214, "r_y2": 359.145, "r_x3": 136.797, "r_y3": 359.145, "coord_origin": "TOPLEFT"}, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "orig": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 380.838, "r_x1": 547.164, "r_y1": 380.838, "r_x2": 547.164, "r_y2": 371.625, "r_x3": 151.197, "r_y3": 371.625, "coord_origin": "TOPLEFT"}, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "orig": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 393.318, "r_x1": 547.156, "r_y1": 393.318, "r_x2": 547.156, "r_y2": 384.105, "r_x3": 151.197, "r_y3": 384.105, "coord_origin": "TOPLEFT"}, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "orig": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 405.857, "r_x1": 547.193, "r_y1": 405.857, "r_x2": 547.193, "r_y2": 396.644, "r_x3": 151.197, "r_y3": 396.644, "coord_origin": "TOPLEFT"}, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 418.337, "r_x1": 547.186, "r_y1": 418.337, "r_x2": 547.186, "r_y2": 409.124, "r_x3": 151.197, "r_y3": 409.124, "coord_origin": "TOPLEFT"}, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 430.817, "r_x1": 547.167, "r_y1": 430.817, "r_x2": 547.167, "r_y2": 421.604, "r_x3": 151.197, "r_y3": 421.604, "coord_origin": "TOPLEFT"}, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 443.357, "r_x1": 547.176, "r_y1": 443.357, "r_x2": 547.176, "r_y2": 434.144, "r_x3": 151.197, "r_y3": 434.144, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "orig": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 455.837, "r_x1": 547.158, "r_y1": 455.837, "r_x2": 547.158, "r_y2": 446.624, "r_x3": 151.197, "r_y3": 446.624, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 468.317, "r_x1": 547.215, "r_y1": 468.317, "r_x2": 547.215, "r_y2": 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Row and Column Access Control", "orig": "Chapter 3. 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. . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 515.356, "r_x1": 547.163, "r_y1": 515.356, "r_x2": 547.163, "r_y2": 506.143, "r_x3": 151.197, "r_y3": 506.143, "coord_origin": "TOPLEFT"}, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "orig": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 527.836, "r_x1": 547.127, "r_y1": 527.836, "r_x2": 547.127, "r_y2": 518.623, "r_x3": 151.197, "r_y3": 518.623, "coord_origin": "TOPLEFT"}, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, "coord_origin": "TOPLEFT"}, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 565.335, "r_x1": 547.185, "r_y1": 565.335, "r_x2": 547.185, "r_y2": 556.1220000000001, "r_x3": 151.197, "r_y3": 556.1220000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "orig": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, 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users and their roles. . . . . . . . . . . . . . . . . . . . . . . 23", "orig": "3.6.2 Creating group profiles for the users and their roles. . . . . . . . . . . . . . . . . . . . . . . 23", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 652.873, "r_x1": 547.158, "r_y1": 652.873, "r_x2": 547.158, "r_y2": 643.66, "r_x3": 151.197, "r_y3": 643.66, "coord_origin": "TOPLEFT"}, "text": "3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24", "orig": "3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 665.353, "r_x1": 547.16, "r_y1": 665.353, "r_x2": 547.16, "r_y2": 656.14, "r_x3": 151.197, "r_y3": 656.14, "coord_origin": "TOPLEFT"}, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25", "orig": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 677.833, "r_x1": 547.17, "r_y1": 677.833, "r_x2": 547.17, "r_y2": 668.62, "r_x3": 151.197, "r_y3": 668.62, "coord_origin": "TOPLEFT"}, "text": "3.6.5 Defining and creating column masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "orig": "3.6.5 Defining and creating column masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. 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A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. 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He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. Hernando can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 680.837, "r_x1": 320.585, "r_y1": 680.837, "r_x2": 320.585, "r_y2": 671.624, "r_x3": 263.4, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": "contacted at ", "orig": "contacted at ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.64, "r_y0": 680.473, "r_x1": 410.578, "r_y1": 680.473, "r_x2": 410.578, "r_y2": 672.072, "r_x3": 320.64, "r_y3": 672.072, "coord_origin": "TOPLEFT"}, "text": "hbedoya@us.ibm.com", "orig": "hbedoya@us.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.579, "r_y0": 680.837, "r_x1": 413.348, "r_y1": 680.837, "r_x2": 413.348, "r_y2": 671.624, "r_x3": 410.579, "r_y3": 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, "coord_origin": "TOPLEFT"}, "text": "global businesses of all sizes. The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 538.939, "r_x1": 250.2, "r_y1": 538.939, "r_x2": 250.2, "r_y2": 529.726, "r_x3": 151.2, 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.075252532958984, "t": 70.57737731933594, "r": 239.620361328125, "b": 237.9579315185547, "coord_origin": "TOPLEFT"}, "confidence": 0.7509335279464722, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 500.4, "t": 93.16899999999998, "r": 522.618, "b": 130.13199999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.7085074782371521, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.4, "r_y0": 130.13199999999995, "r_x1": 522.618, "r_y1": 130.13199999999995, "r_x2": 522.618, "r_y2": 93.16899999999998, "r_x3": 500.4, "r_y3": 93.16899999999998, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "section_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88599999999997, "r": 549.157, "b": 278.918, "coord_origin": "TOPLEFT"}, "confidence": 0.9371159076690674, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Securing and protecting IBM DB2"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 348.709, "r": 542.266, "b": 357.922, "coord_origin": "TOPLEFT"}, "confidence": 0.9868631362915039, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting"}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 442.728, "r": 525.074, "b": 451.941, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing"}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 500.687, "r": 547.404, "b": 509.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics"}, {"label": "list_item", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 530.175, "r": 141.78, "b": 538.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9373421669006348, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 542.174, "r": 141.78, "b": 550.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9334256649017334, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 550.575, "r_x1": 141.78, "r_y1": 550.575, "r_x2": 141.78, "r_y2": 542.174, "r_x3": 136.8, "r_y3": 542.174, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 554.174, "r": 141.78, "b": 562.5740000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9462205767631531, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 562.5740000000001, "r_x1": 141.78, "r_y1": 562.5740000000001, "r_x2": 141.78, "r_y2": 554.174, "r_x3": 136.8, "r_y3": 554.174, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "footnote", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "footnote", "bbox": {"l": 136.8, "t": 715.84, "r": 146.34, "b": 721.204, "coord_origin": "TOPLEFT"}, "confidence": 0.5138102173805237, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 721.204, "r_x1": 146.34, "r_y1": 721.204, "r_x2": 146.34, "r_y2": 715.84, "r_x3": 136.8, "r_y3": 715.84, "coord_origin": "TOPLEFT"}, "text": "1 ", "orig": "1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "footnote", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "footnote", "bbox": {"l": 136.8, "t": 725.574, "r": 143.908, "b": 731.457, "coord_origin": "TOPLEFT"}, "confidence": 0.7965563535690308, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 731.457, "r_x1": 143.908, "r_y1": 731.457, "r_x2": 143.908, "r_y2": 725.574, "r_x3": 136.8, "r_y3": 725.574, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.075252532958984, "t": 70.57737731933594, "r": 239.620361328125, "b": 237.9579315185547, "coord_origin": "TOPLEFT"}, "confidence": 0.7509335279464722, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.68, "t": 754.849, "r": 547.218, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "page_footer", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9364771246910095, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 127.822, "r_x1": 324.463, "r_y1": 127.822, "r_x2": 324.463, "r_y2": 118.60900000000004, "r_x3": 136.8, "r_y3": 118.60900000000004, "coord_origin": "TOPLEFT"}, "text": "information assets that must be described:", "orig": "information assets that must be described:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 74.34100000000001, "r": 267.39, "b": 89.10400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.60900000000004, "r": 545.014, "b": 115.822, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 136.096, "r": 141.78, "b": 144.49699999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 151.199, "t": 188.62800000000004, "r": 541.654, "b": 197.841, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 151.199, "t": 277.60699999999997, "r": 542.001, "b": 286.82, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). 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323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.629, "r": 530.314, "b": 479.842, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.648, "r": 513.913, "b": 537.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.667, "r": 547.652, "b": 607.88, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default"}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 541.139, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. 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command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 123.74099999999999, "r_x1": 253.222, "r_y1": 123.74099999999999, "r_x2": 253.222, "r_y2": 114.52800000000002, "r_x3": 151.2, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Work Function Usage (", "orig": "Work Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.26, "r_y0": 123.42399999999998, "r_x1": 298.2, "r_y1": 123.42399999999998, "r_x2": 298.2, "r_y2": 114.976, "r_x3": 253.26, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "WRKFCNUSG", "orig": "WRKFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 298.201, "r_y0": 123.74099999999999, "r_x1": 301.517, "r_y1": 123.74099999999999, "r_x2": 301.517, "r_y2": 114.52800000000002, "r_x3": 298.201, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 135.74099999999999, "r_x1": 265.099, "r_y1": 135.74099999999999, "r_x2": 265.099, "r_y2": 126.52800000000002, "r_x3": 151.201, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Change Function Usage (", "orig": "Change Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.081, "r_y0": 135.42399999999998, "r_x1": 310.02, "r_y1": 135.42399999999998, "r_x2": 310.02, "r_y2": 126.976, "r_x3": 265.081, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.081, "r_y0": 135.74099999999999, "r_x1": 313.398, "r_y1": 135.74099999999999, "r_x2": 313.398, "r_y2": 126.52800000000002, "r_x3": 310.081, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 147.74099999999999, "r_x1": 262.535, "r_y1": 147.74099999999999, "r_x2": 262.535, "r_y2": 138.52800000000002, "r_x3": 151.201, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Display Function Usage (", "orig": "Display Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.561, "r_y0": 147.42399999999998, "r_x1": 307.5, "r_y1": 147.42399999999998, "r_x2": 307.5, "r_y2": 138.976, "r_x3": 262.561, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "DSPFCNUSG", "orig": "DSPFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 307.5, "r_y0": 147.74099999999999, "r_x1": 310.817, "r_y1": 147.74099999999999, "r_x2": 310.817, "r_y2": 138.52800000000002, "r_x3": 307.5, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 673.762, "r_x1": 547.592, "r_y1": 673.762, "r_x2": 547.592, "r_y2": 664.549, "r_x3": 136.8, "r_y3": 664.549, "coord_origin": "TOPLEFT"}, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 697.761, "r_x1": 544.288, "r_y1": 697.761, "r_x2": 544.288, "r_y2": 688.548, "r_x3": 136.8, "r_y3": 688.548, "coord_origin": "TOPLEFT"}, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "orig": "administrative functions to be divided across individuals without overlapping responsibilities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 709.761, "r_x1": 536.25, "r_y1": 709.761, "r_x2": 536.25, "r_y2": 700.548, "r_x3": 136.8, "r_y3": 700.548, "coord_origin": "TOPLEFT"}, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "orig": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 304.863, "r_x1": 202.253, "r_y1": 304.863, "r_x2": 202.253, "r_y2": 296.538, "r_x3": 142.8, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Column name", "orig": "Column name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.809, "r_y0": 304.863, "r_x1": 257.219, "r_y1": 304.863, "r_x2": 257.219, "r_y2": 296.538, "r_x3": 216.809, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Data type", "orig": "Data type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.475, "r_y0": 304.863, "r_x1": 338.903, "r_y1": 304.863, "r_x2": 338.903, "r_y2": 296.538, "r_x3": 289.475, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Description", "orig": "Description", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 323.883, "r_x1": 203.243, "r_y1": 323.883, "r_x2": 203.243, "r_y2": 315.558, "r_x3": 142.8, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "FUNCTION_ID", "orig": "FUNCTION_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.785, "r_y0": 323.883, "r_x1": 276.014, "r_y1": 323.883, "r_x2": 276.014, "r_y2": 315.558, "r_x3": 216.785, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(30)", "orig": "VARCHAR(30)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.458, "r_y0": 323.883, "r_x1": 359.865, "r_y1": 323.883, "r_x2": 359.865, "r_y2": 315.558, "r_x3": 289.458, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "ID of the function.", "orig": "ID of the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 342.843, "r_x1": 198.685, "r_y1": 342.843, "r_x2": 198.685, "r_y2": 334.518, "r_x3": 142.8, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "USER_NAME", "orig": "USER_NAME", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.741, "r_y0": 342.843, "r_x1": 275.939, "r_y1": 342.843, "r_x2": 275.939, "r_y2": 334.518, "r_x3": 216.741, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(10)", "orig": "VARCHAR(10)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.382, "r_y0": 342.843, "r_x1": 515.035, "r_y1": 342.843, "r_x2": 515.035, "r_y2": 334.518, "r_x3": 289.382, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "Name of the user profile that has a usage setting for this ", "orig": "Name of the user profile that has a usage setting for this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 353.883, "r_x1": 323.439, "r_y1": 353.883, "r_x2": 323.439, "r_y2": 345.558, "r_x3": 289.44, "r_y3": 345.558, "coord_origin": "TOPLEFT"}, "text": "function.", "orig": "function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 372.844, "r_x1": 173.997, "r_y1": 372.844, "r_x2": 173.997, "r_y2": 364.519, "r_x3": 142.8, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "USAGE", "orig": "USAGE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.774, "r_y0": 372.844, "r_x1": 270.993, "r_y1": 372.844, "r_x2": 270.993, "r_y2": 364.519, "r_x3": 216.774, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(7)", "orig": "VARCHAR(7)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.416, "r_y0": 372.844, "r_x1": 346.901, "r_y1": 372.844, "r_x2": 346.901, "r_y2": 364.519, "r_x3": 289.416, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "Usage setting:", "orig": "Usage setting:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 383.555, "r_x1": 293.94, "r_y1": 383.555, "r_x2": 293.94, "r_y2": 375.964, "r_x3": 289.44, "r_y3": 375.964, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 383.884, "r_x1": 535.18, "r_y1": 383.884, "r_x2": 535.18, "r_y2": 375.559, "r_x3": 303.84, "r_y3": 375.559, "coord_origin": "TOPLEFT"}, "text": "ALLOWED: The user profile is allowed to use the function.", "orig": "ALLOWED: The user profile is allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 394.535, "r_x1": 293.94, "r_y1": 394.535, "r_x2": 293.94, "r_y2": 386.944, "r_x3": 289.44, "r_y3": 386.944, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 394.864, "r_x1": 539.121, "r_y1": 394.864, "r_x2": 539.121, "r_y2": 386.539, "r_x3": 303.84, "r_y3": 386.539, "coord_origin": "TOPLEFT"}, "text": "DENIED: The user profile is not allowed to use the function.", "orig": "DENIED: The user profile is not allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 413.884, "r_x1": 196.235, "r_y1": 413.884, "r_x2": 196.235, "r_y2": 405.559, "r_x3": 142.8, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "USER_TYPE", "orig": "USER_TYPE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.752, "r_y0": 413.884, "r_x1": 271.009, "r_y1": 413.884, "r_x2": 271.009, "r_y2": 405.559, "r_x3": 216.752, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(5)", "orig": "VARCHAR(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.432, "r_y0": 413.884, "r_x1": 367.811, "r_y1": 413.884, "r_x2": 367.811, "r_y2": 405.559, "r_x3": 289.432, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "Type of user profile:", "orig": "Type of user profile:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 424.534, "r_x1": 293.94, "r_y1": 424.534, "r_x2": 293.94, "r_y2": 416.944, "r_x3": 289.44, "r_y3": 416.944, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 424.864, "r_x1": 434.795, "r_y1": 424.864, "r_x2": 434.795, "r_y2": 416.539, "r_x3": 303.84, "r_y3": 416.539, "coord_origin": "TOPLEFT"}, "text": "USER: The user profile is a user.", "orig": "USER: The user profile is a user.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 435.514, "r_x1": 293.94, "r_y1": 435.514, "r_x2": 293.94, "r_y2": 427.924, "r_x3": 289.44, "r_y3": 427.924, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 435.844, "r_x1": 448.132, "r_y1": 435.844, "r_x2": 448.132, "r_y2": 427.519, "r_x3": 303.84, "r_y3": 427.519, "coord_origin": "TOPLEFT"}, "text": "GROUP: The user profile is a group.", "orig": "GROUP: The user profile is a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.976, "r": 141.78, "b": 123.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.976, "r": 141.781, "b": 135.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.976, "r": 141.781, "b": 147.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 255.049, "b": 169.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 254.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 461.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 529.043, "b": 661.762, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.976, "r": 141.78, "b": 123.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.976, "r": 141.781, "b": 135.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.976, "r": 141.781, "b": 147.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 255.049, "b": 169.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, the following"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 254.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1"}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 461.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is"}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 529.043, "b": 661.762, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational"}, {"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "body": [{"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.976, "r": 141.78, "b": 123.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.976, "r": 141.781, "b": 135.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.976, "r": 141.781, "b": 147.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 255.049, "b": 169.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, the following"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 254.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1"}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 461.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is"}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 529.043, "b": 661.762, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.32, "r_y0": 763.863, "r_x1": 523.523, "r_y1": 763.863, "r_x2": 523.523, "r_y2": 755.538, "r_x3": 355.32, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 542.703, "r_y1": 80.72199999999998, "r_x2": 542.703, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 513.687, "r_y1": 92.72199999999998, "r_x2": 513.687, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. ", "orig": "table. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.1, "r_y0": 220.70000000000005, "r_x1": 232.869, "r_y1": 220.70000000000005, "r_x2": 232.869, "r_y2": 211.48699999999997, "r_x3": 230.1, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70000000000005, "r_x1": 346.314, "r_y1": 232.70000000000005, "r_x2": 346.314, "r_y2": 223.48699999999997, "r_x3": 136.8, "r_y3": 223.48699999999997, "coord_origin": "TOPLEFT"}, "text": "authority and can be given to a user or a group.", "orig": "authority and can be given to a user or a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.71900000000005, "r_x1": 529.471, "r_y1": 266.71900000000005, "r_x2": 529.471, "r_y2": 257.506, "r_x3": 136.8, "r_y3": 257.506, "coord_origin": "TOPLEFT"}, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "orig": "a user is allowed to access in a table and whether a user is allowed to see information in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.71900000000005, "r_x1": 253.462, "r_y1": 278.71900000000005, "r_x2": 253.462, "r_y2": 269.506, "r_x3": 136.8, "r_y3": 269.506, "coord_origin": "TOPLEFT"}, "text": "certain columns of a table.", "orig": "certain columns of a table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 312.738, "r_x1": 539.816, "r_y1": 312.738, "r_x2": 539.816, "r_y2": 303.525, "r_x3": 136.8, "r_y3": 303.525, "coord_origin": "TOPLEFT"}, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 370.697, "r_x1": 343.798, "r_y1": 370.697, "r_x2": 343.798, "r_y2": 361.484, "r_x3": 136.8, "r_y3": 361.484, "coord_origin": "TOPLEFT"}, "text": "the different CL commands and DB2 for i tools.", "orig": "the different CL commands and DB2 for i tools.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 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usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.8, "t": 245.50599999999997, "r": 545.805, "b": 254.71900000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 291.525, "r": 533.79, "b": 300.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function"}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 349.485, "r": 543.076, "b": 358.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to"}, {"label": "caption", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 64.8, "t": 383.538, "r": 391.767, "b": 391.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9337189793586731, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 391.863, "r_x1": 391.767, "r_y1": 391.863, "r_x2": 391.767, "r_y2": 383.538, "r_x3": 64.8, "r_y3": 383.538, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"label": "page_footer", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8987301588058472, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "11"}, {"label": "page_footer", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 355.32, "t": 755.538, "r": 523.523, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9508126378059387, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.32, "r_y0": 763.863, "r_x1": 523.523, "r_y1": 763.863, "r_x2": 523.523, "r_y2": 755.538, "r_x3": 355.32, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 64.41139221191406, "t": 393.6136169433594, "r": 547.3950805664062, "b": 721.60791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9899571537971497, "cells": [], "children": []}, "text": null, "otsl_seq": ["fcel", "ched", "ched", "ched", "ched", "ched", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "fcel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "fcel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "body": [{"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 542.703, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 542.703, "r_y1": 80.72199999999998, "r_x2": 542.703, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to"}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 153.52800000000002, "r": 544.17, "b": 162.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities,"}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 211.48699999999997, "r": 538.66, "b": 220.70000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.8, "t": 245.50599999999997, "r": 545.805, "b": 254.71900000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 291.525, "r": 533.79, "b": 300.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function"}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 349.485, "r": 543.076, "b": 358.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to"}, {"label": "caption", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 64.8, "t": 383.538, "r": 391.767, "b": 391.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9337189793586731, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 391.863, "r_x1": 391.767, "r_y1": 391.863, "r_x2": 391.767, "r_y2": 383.538, "r_x3": 64.8, "r_y3": 383.538, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 64.41139221191406, "t": 393.6136169433594, "r": 547.3950805664062, "b": 721.60791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9899571537971497, "cells": [], "children": []}, "text": null, "otsl_seq": ["fcel", "ched", "ched", "ched", "ched", "ched", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "fcel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "fcel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", 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Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "15", "orig": "15", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 179.591, "r_y1": 80.72199999999998, "r_x2": 179.591, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The SQL ", "orig": "The SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.58, "r_y0": 80.40499999999997, "r_x1": 264.479, "r_y1": 80.40499999999997, "r_x2": 264.479, "r_y2": 71.957, "r_x3": 179.58, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION", "orig": "CREATE PERMISSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.54, "r_y0": 80.72199999999998, "r_x1": 528.74, "r_y1": 80.72199999999998, "r_x2": 528.74, "r_y2": 71.50900000000001, "r_x3": 264.54, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": " statement that is shown in Figure 3-1 is used to define and ", "orig": " statement that is shown in Figure 3-1 is used to define and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 341.703, "r_y1": 92.72199999999998, "r_x2": 341.703, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "initially enable or disable the row access rules.", "orig": "initially enable or disable the row access rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 422.463, "r_x1": 341.985, "r_y1": 422.463, "r_x2": 341.985, "r_y2": 414.138, "r_x3": 136.8, "r_y3": 414.138, "coord_origin": "TOPLEFT"}, "text": "Figure 3-1 CREATE PERMISSION SQL statement", "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.044, "r_x1": 215.383, "r_y1": 451.044, "r_x2": 215.383, "r_y2": 439.944, "r_x3": 136.8, "r_y3": 439.944, "coord_origin": "TOPLEFT"}, "text": "Column mask", "orig": "Column mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 464.302, "r_x1": 542.776, "r_y1": 464.302, "r_x2": 542.776, "r_y2": 455.089, "r_x3": 136.8, "r_y3": 455.089, "coord_origin": "TOPLEFT"}, "text": "A column mask is a database object that manifests a column value access control rule for a ", "orig": "A column mask is a database object that manifests a column value access control rule for a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 476.302, "r_x1": 537.85, "r_y1": 476.302, "r_x2": 537.85, "r_y2": 467.089, "r_x3": 136.8, "r_y3": 467.089, "coord_origin": "TOPLEFT"}, "text": "specific column in a specific table. It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. For example, a teller can see only the last four digits of a tax ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 500.301, "r_x1": 231.178, "r_y1": 500.301, "r_x2": 231.178, "r_y2": 491.088, "r_x3": 136.8, "r_y3": 491.088, "coord_origin": "TOPLEFT"}, "text": "identification number.", "orig": "identification number.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 149.37099999999998, "r_x1": 251.916, "r_y1": 149.37099999999998, "r_x2": 251.916, "r_y2": 140.20799999999997, "r_x3": 148.134, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION ", "orig": "CREATE PERMISSION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.565, "r_y0": 129.22400000000005, "r_x1": 530.748, "r_y1": 129.22400000000005, "r_x2": 530.748, "r_y2": 121.88599999999997, "r_x3": 346.565, "r_y3": 121.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Names the row permission for row access control", "orig": "Names the row permission for row access control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 190.75400000000002, "r_x1": 163.458, "r_y1": 190.75400000000002, "r_x2": 163.458, "r_y2": 181.591, "r_x3": 148.134, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "ON", "orig": "ON", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.584, "r_y0": 190.74300000000005, "r_x1": 174.303, "r_y1": 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"rect": {"r_x0": 311.32, "r_y0": 174.05600000000004, "r_x1": 529.937, "r_y1": 174.05600000000004, "r_x2": 529.937, "r_y2": 166.71799999999996, "r_x3": 311.32, "r_y3": 166.71799999999996, "coord_origin": "TOPLEFT"}, "text": "Identifies the table on which the row permission is created", "orig": "Identifies the table on which the row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 232.13699999999994, "r_x1": 163.113, "r_y1": 232.13699999999994, "r_x2": 163.113, "r_y2": 222.97400000000005, "r_x3": 148.134, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "AS ", "orig": "AS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.687, "r_y0": 232.12599999999998, "r_x1": 171.406, "r_y1": 232.12599999999998, "r_x2": 171.406, "r_y2": 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"r_y2": 246.51800000000003, "r_x3": 321.563, "r_y3": 246.51800000000003, "coord_origin": "TOPLEFT"}, "text": "Indicates that a row permission is created", "orig": "Indicates that a row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.597, "r_y0": 274.06500000000005, "r_x1": 530.955, "r_y1": 274.06500000000005, "r_x2": 530.955, "r_y2": 266.726, "r_x3": 321.597, "r_y3": 266.726, "coord_origin": "TOPLEFT"}, "text": "Specifies a condition that can be true, false, or unknown", "orig": "Specifies a condition that can be true, false, or unknown", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 301.109, "r_x1": 183.43, "r_y1": 301.109, "r_x2": 183.43, "r_y2": 291.945, "r_x3": 148.134, "r_y3": 291.945, "coord_origin": "TOPLEFT"}, "text": "WHERE", 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Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.245, "r_y0": 197.88300000000004, "r_x1": 425.656, "r_y1": 197.88300000000004, "r_x2": 425.656, "r_y2": 189.558, "r_x3": 230.245, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "The authorization ID that initiated the connection.", "orig": "The authorization ID that initiated the connection.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.732, "r_y0": 395.376, "r_x1": 216.376, "r_y1": 395.376, "r_x2": 216.376, "r_y2": 387.442, "r_x3": 140.732, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": "Signed on as ALICE", "orig": "Signed on as ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.708, "r_y0": 395.376, "r_x1": 169.958, "r_y1": 395.376, "r_x2": 169.958, "r_y2": 387.442, "r_x3": 167.708, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", 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"text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49800000000005, "r": 372.618, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": 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variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and"}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "headers": [{"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 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Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 118.86300000000006, "r_x1": 134.993, "r_y1": 118.86300000000006, "r_x2": 134.993, "r_y2": 110.53800000000001, "r_x3": 70.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Global variable", "orig": "Global variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.889, "r_y0": 118.86300000000006, "r_x1": 223.349, "r_y1": 118.86300000000006, "r_x2": 223.349, "r_y2": 110.53800000000001, "r_x3": 202.889, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Type", "orig": "Type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.825, "r_y0": 118.86300000000006, "r_x1": 331.345, "r_y1": 118.86300000000006, "r_x2": 331.345, "r_y2": 110.53800000000001, "r_x3": 281.825, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Description", "orig": "Description", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 137.82299999999998, "r_x1": 132.729, "r_y1": 137.82299999999998, "r_x2": 132.729, "r_y2": 129.49800000000005, "r_x3": 70.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "CLIENT_HOST", "orig": "CLIENT_HOST", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.89, "r_y0": 137.82299999999998, "r_x1": 267.085, "r_y1": 137.82299999999998, "r_x2": 267.085, "r_y2": 129.49800000000005, "r_x3": 202.89, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(255)", "orig": "VARCHAR(255)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.847, "r_y0": 137.82299999999998, "r_x1": 510.176, "r_y1": 137.82299999999998, "r_x2": 510.176, "r_y2": 129.49800000000005, "r_x3": 281.847, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Host name of the current client as returned by the system", "orig": "Host name of the current client as returned by the system", "text_direction": 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156.84299999999996, "r_x2": 509.611, "r_y2": 148.51800000000003, "r_x3": 281.845, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "IP address of the current client as returned by the system", "orig": "IP address of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 175.86300000000006, "r_x1": 134.965, "r_y1": 175.86300000000006, "r_x2": 134.965, "r_y2": 167.538, "r_x3": 70.8, "r_y3": 167.538, "coord_origin": "TOPLEFT"}, "text": "CLIENT_PORT ", "orig": "CLIENT_PORT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.903, "r_y0": 175.86300000000006, "r_x1": 242.816, "r_y1": 175.86300000000006, "r_x2": 242.816, "r_y2": 167.538, "r_x3": 202.903, "r_y3": 167.538, "coord_origin": "TOPLEFT"}, "text": "INTEGER", "orig": 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lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.8, "t": 93.49800000000005, "r": 201.196, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 322.201, "r": 384.345, "b": 336.964, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.529, "r": 542.844, "b": 363.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 525.156, "b": 457.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile"}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, 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"r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 145.089, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 145.099, "b": 554.719, 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11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9187920093536377, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9505079388618469, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 342.519, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8370978832244873, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.8, "t": 93.49800000000005, "r": 201.196, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 322.201, "r": 384.345, "b": 336.964, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.529, "r": 542.844, "b": 363.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 525.156, "b": 457.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile"}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, "r": 458.427, "b": 503.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 511.546, "r": 145.089, "b": 520.759, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 145.089, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 145.099, "b": 554.719, 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78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9505079388618469, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.35699999999997, "r_x1": 311.697, "r_y1": 116.35699999999997, "r_x2": 311.697, "r_y2": 107.957, "r_x3": 136.8, "r_y3": 107.957, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 164.356, "r_x1": 311.697, "r_y1": 164.356, "r_x2": 311.697, "r_y2": 155.95600000000002, "r_x3": 136.8, "r_y3": 155.95600000000002, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 564.375, "r_x1": 141.78, "r_y1": 564.375, "r_x2": 141.78, "r_y2": 555.975, "r_x3": 136.8, "r_y3": 555.975, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 466.615, "b": 104.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 145.098, "b": 284.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.039, "t": 304.485, "r": 157.577, "b": 313.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.039, "t": 321.525, "r": 157.577, "b": 330.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 157.577, "b": 347.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 157.577, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 381.657, "b": 432.377, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 466.615, "b": 104.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 145.098, "b": 284.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.039, 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"confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 157.577, "b": 347.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 157.577, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 381.657, "b": 432.377, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, 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"coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 157.577, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 381.657, "b": 432.377, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, 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"bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.699, "r_y0": 401.72, "r_x1": 446.468, "r_y1": 401.72, "r_x2": 446.468, "r_y2": 392.507, "r_x3": 443.699, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 522.418, "r_y0": 401.72, "r_x1": 525.187, "r_y1": 401.72, "r_x2": 525.187, "r_y2": 392.507, "r_x3": 522.418, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 145.089, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.8, "t": 173.538, "r": 293.146, "b": 181.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.8, "t": 202.375, "r": 203.983, "b": 214.36300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.529, "r": 537.1, "b": 237.74199999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 145.089, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": 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"r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 145.089, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": 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3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": 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"text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 145.089, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.957, "r": 376.677, "b": 329.357, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Column Access Control (masks) */"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.956, "r": 291.718, "b": 365.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.508, "r": 145.12, "b": 389.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.8, "t": 649.038, "r": 347.441, "b": 657.363, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. 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CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . 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VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . 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CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 2, "label": "code", "bbox": {"l": 64.8, "t": 71.94299999999998, "r": 177.119, "b": 79.53399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, 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"Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 249.32000000000005, "r_x1": 547.129, "r_y1": 249.32000000000005, "r_x2": 547.129, "r_y2": 240.10699999999997, "r_x3": 136.798, "r_y3": 240.10699999999997, "coord_origin": "TOPLEFT"}, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "orig": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 271.82000000000005, "r_x1": 373.148, "r_y1": 271.82000000000005, "r_x2": 373.148, 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. Roles and separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.997, "r_y0": 356.358, "r_x1": 547.204, "r_y1": 356.358, "r_x2": 547.204, "r_y2": 347.145, "r_x3": 338.997, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 368.358, "r_x1": 547.214, "r_y1": 368.358, "r_x2": 547.214, "r_y2": 359.145, "r_x3": 136.797, "r_y3": 359.145, "coord_origin": "TOPLEFT"}, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "orig": "2.1 Roles . . . . . . . . . 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. . . . . . . . . . . . . 8", "orig": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 405.857, "r_x1": 547.193, "r_y1": 405.857, "r_x2": 547.193, "r_y2": 396.644, "r_x3": 151.197, "r_y3": 396.644, "coord_origin": "TOPLEFT"}, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 418.337, "r_x1": 547.186, "r_y1": 418.337, "r_x2": 547.186, "r_y2": 409.124, "r_x3": 151.197, "r_y3": 409.124, "coord_origin": "TOPLEFT"}, "text": "2.1.4 Database 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Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 502.816, "r_x1": 547.173, "r_y1": 502.816, "r_x2": 547.173, "r_y2": 493.603, "r_x3": 136.797, "r_y3": 493.603, "coord_origin": "TOPLEFT"}, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "orig": "3.1 Explanation of RCAC and the concept of access control . . . . . . . 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"TOPLEFT"}, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 565.335, "r_x1": 547.185, "r_y1": 565.335, "r_x2": 547.185, "r_y2": 556.1220000000001, "r_x3": 151.197, "r_y3": 556.1220000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "orig": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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262.60699999999997, "coord_origin": "TOPLEFT"}, "text": "Chapter 1. Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 375.118, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.797, "t": 347.145, "r": 336.783, "b": 356.358, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. 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"TOPLEFT"}, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 430.817, "r_x1": 547.167, "r_y1": 430.817, "r_x2": 547.167, "r_y2": 421.604, "r_x3": 151.197, "r_y3": 421.604, "coord_origin": "TOPLEFT"}, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 136.797, "t": 481.603, "r": 348.65, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 490.816, "r_x1": 348.65, "r_y1": 490.816, "r_x2": 348.65, "r_y2": 481.603, "r_x3": 136.797, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 350.097, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 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column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 151.197, "t": 518.623, "r": 547.127, "b": 527.836, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 527.836, "r_x1": 547.127, "r_y1": 527.836, "r_x2": 547.127, "r_y2": 518.623, "r_x3": 151.197, "r_y3": 518.623, "coord_origin": "TOPLEFT"}, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.797, "t": 531.162, "r": 547.154, "b": 540.375, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 577.875, "r_x1": 547.129, "r_y1": 577.875, "r_x2": 547.129, "r_y2": 568.662, "r_x3": 136.797, "r_y3": 568.662, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "orig": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": 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"TOPLEFT"}, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 136.797, "t": 606.1610000000001, "r": 547.215, "b": 615.374, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 615.374, "r_x1": 547.215, "r_y1": 615.374, "r_x2": 547.215, "r_y2": 606.1610000000001, "r_x3": 136.797, "r_y3": 606.1610000000001, "coord_origin": "TOPLEFT"}, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. 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Row and Column Access Control", "orig": "Chapter 3. 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. 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, "coord_origin": "TOPLEFT"}, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 565.335, "r_x1": 547.185, "r_y1": 565.335, "r_x2": 547.185, "r_y2": 556.1220000000001, "r_x3": 151.197, "r_y3": 556.1220000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "orig": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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262.60699999999997, "coord_origin": "TOPLEFT"}, "text": "Chapter 1. Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 375.118, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.797, "t": 347.145, "r": 336.783, "b": 356.358, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 136.797, "t": 481.603, "r": 348.65, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 490.816, "r_x1": 348.65, "r_y1": 490.816, "r_x2": 348.65, "r_y2": 481.603, "r_x3": 136.797, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 350.097, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 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"coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 577.875, "r_x1": 547.129, "r_y1": 577.875, "r_x2": 547.129, "r_y2": 568.662, "r_x3": 136.797, "r_y3": 568.662, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "orig": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": 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. . . . . . . 24", "orig": "3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 48, "label": "text", "bbox": {"l": 151.197, "t": 656.14, "r": 547.16, "b": 665.353, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 665.353, "r_x1": 547.16, "r_y1": 665.353, "r_x2": 547.16, "r_y2": 656.14, "r_x3": 151.197, "r_y3": 656.14, "coord_origin": "TOPLEFT"}, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25", "orig": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 49, "label": "text", "bbox": {"l": 151.197, "t": 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. . . . . . 29", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 151.197, "t": 706.12, "r": 547.13, "b": 715.333, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 715.333, "r_x1": 547.13, "r_y1": 715.333, "r_x2": 547.13, "r_y2": 706.12, "r_x3": 151.197, "r_y3": 706.12, "coord_origin": "TOPLEFT"}, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "orig": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl"], "num_rows": 41, "num_cols": 1, "table_cells": [{"bbox": {"l": 136.8, "t": 132.649, "r": 547.19, "b": 141.86199999999997, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 145.12800000000004, "r": 547.184, "b": 154.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 167.62800000000004, "r": 547.199, "b": 176.841, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 190.12800000000004, "r": 547.188, "b": 199.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 347.145, "r": 547.204, "b": 356.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 359.145, "r": 547.214, "b": 368.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 371.625, "r": 547.164, "b": 380.838, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 384.105, "r": 547.156, "b": 393.318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 396.644, "r": 547.193, "b": 405.857, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 409.124, "r": 547.186, "b": 418.337, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 434.144, "r": 547.176, "b": 443.357, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 446.624, "r": 547.158, "b": 455.837, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 459.104, "r": 547.215, "b": 468.317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 506.143, "r": 547.163, "b": 515.356, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, 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29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 556.1220000000001, "r": 547.185, "b": 565.335, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 31, "end_row_offset_idx": 32, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.3 VERIFY_GROUP_FOR_USER 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you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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262.60699999999997, "coord_origin": "TOPLEFT"}, "text": "Chapter 1. Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 375.118, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.797, "t": 347.145, "r": 336.783, "b": 356.358, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. 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"TOPLEFT"}, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 430.817, "r_x1": 547.167, "r_y1": 430.817, "r_x2": 547.167, "r_y2": 421.604, "r_x3": 151.197, "r_y3": 421.604, "coord_origin": "TOPLEFT"}, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 136.797, "t": 481.603, "r": 348.65, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 490.816, "r_x1": 348.65, "r_y1": 490.816, "r_x2": 348.65, "r_y2": 481.603, "r_x3": 136.797, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 350.097, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 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column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 151.197, "t": 518.623, "r": 547.127, "b": 527.836, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 527.836, "r_x1": 547.127, "r_y1": 527.836, "r_x2": 547.127, "r_y2": 518.623, "r_x3": 151.197, "r_y3": 518.623, "coord_origin": "TOPLEFT"}, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.797, "t": 531.162, "r": 547.154, "b": 540.375, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 577.875, "r_x1": 547.129, "r_y1": 577.875, "r_x2": 547.129, "r_y2": 568.662, "r_x3": 136.797, "r_y3": 568.662, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "orig": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": 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"TOPLEFT"}, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 136.797, "t": 606.1610000000001, "r": 547.215, "b": 615.374, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 615.374, "r_x1": 547.215, "r_y1": 615.374, "r_x2": 547.215, "r_y2": 606.1610000000001, "r_x3": 136.797, "r_y3": 606.1610000000001, "coord_origin": "TOPLEFT"}, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . 29", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 151.197, "t": 706.12, "r": 547.13, "b": 715.333, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 715.333, "r_x1": 547.13, "r_y1": 715.333, "r_x2": 547.13, "r_y2": 706.12, "r_x3": 151.197, "r_y3": 706.12, "coord_origin": "TOPLEFT"}, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "orig": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl"], "num_rows": 41, "num_cols": 1, "table_cells": [{"bbox": {"l": 136.8, "t": 132.649, "r": 547.19, "b": 141.86199999999997, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 145.12800000000004, "r": 547.184, "b": 154.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 167.62800000000004, "r": 547.199, "b": 176.841, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 190.12800000000004, "r": 547.188, "b": 199.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 347.145, "r": 547.204, "b": 356.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 359.145, "r": 547.214, "b": 368.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 371.625, "r": 547.164, "b": 380.838, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 384.105, "r": 547.156, "b": 393.318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 396.644, "r": 547.193, "b": 405.857, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 409.124, "r": 547.186, "b": 418.337, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 434.144, "r": 547.176, "b": 443.357, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 446.624, "r": 547.158, "b": 455.837, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 459.104, "r": 547.215, "b": 468.317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 506.143, "r": 547.163, "b": 515.356, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, 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190.12800000000004, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 249.32000000000005, "r_x1": 547.129, "r_y1": 249.32000000000005, "r_x2": 547.129, "r_y2": 240.10699999999997, "r_x3": 136.798, "r_y3": 240.10699999999997, "coord_origin": "TOPLEFT"}, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "orig": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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262.60699999999997, "coord_origin": "TOPLEFT"}, "text": "Chapter 1. Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 375.118, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.797, "t": 347.145, "r": 336.783, "b": 356.358, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. 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"TOPLEFT"}, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 430.817, "r_x1": 547.167, "r_y1": 430.817, "r_x2": 547.167, "r_y2": 421.604, "r_x3": 151.197, "r_y3": 421.604, "coord_origin": "TOPLEFT"}, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 136.797, "t": 481.603, "r": 348.65, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 490.816, "r_x1": 348.65, "r_y1": 490.816, "r_x2": 348.65, "r_y2": 481.603, "r_x3": 136.797, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 350.097, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 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column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 151.197, "t": 518.623, "r": 547.127, "b": 527.836, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 527.836, "r_x1": 547.127, "r_y1": 527.836, "r_x2": 547.127, "r_y2": 518.623, "r_x3": 151.197, "r_y3": 518.623, "coord_origin": "TOPLEFT"}, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.797, "t": 531.162, "r": 547.154, "b": 540.375, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 577.875, "r_x1": 547.129, "r_y1": 577.875, "r_x2": 547.129, "r_y2": 568.662, "r_x3": 136.797, "r_y3": 568.662, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "orig": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": 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"TOPLEFT"}, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 136.797, "t": 606.1610000000001, "r": 547.215, "b": 615.374, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 615.374, "r_x1": 547.215, "r_y1": 615.374, "r_x2": 547.215, "r_y2": 606.1610000000001, "r_x3": 136.797, "r_y3": 606.1610000000001, "coord_origin": "TOPLEFT"}, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . 29", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 151.197, "t": 706.12, "r": 547.13, "b": 715.333, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 715.333, "r_x1": 547.13, "r_y1": 715.333, "r_x2": 547.13, "r_y2": 706.12, "r_x3": 151.197, "r_y3": 706.12, "coord_origin": "TOPLEFT"}, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "orig": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl"], "num_rows": 41, "num_cols": 1, "table_cells": [{"bbox": {"l": 136.8, "t": 132.649, "r": 547.19, "b": 141.86199999999997, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 145.12800000000004, "r": 547.184, "b": 154.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 167.62800000000004, "r": 547.199, "b": 176.841, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 190.12800000000004, "r": 547.188, "b": 199.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 347.145, "r": 547.204, "b": 356.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 359.145, "r": 547.214, "b": 368.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 371.625, "r": 547.164, "b": 380.838, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 384.105, "r": 547.156, "b": 393.318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 396.644, "r": 547.193, "b": 405.857, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 409.124, "r": 547.186, "b": 418.337, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 434.144, "r": 547.176, "b": 443.357, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 446.624, "r": 547.158, "b": 455.837, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 459.104, "r": 547.215, "b": 468.317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 506.143, "r": 547.163, "b": 515.356, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. 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A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. In the years since, Jim has held ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 480.86, "r_x1": 520.251, "r_y1": 480.86, "r_x2": 520.251, "r_y2": 471.647, "r_x3": 263.4, "r_y3": 471.647, "coord_origin": "TOPLEFT"}, "text": "numerous technical roles, including independent software ", "orig": "numerous technical roles, including independent software ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 492.86, "r_x1": 490.706, "r_y1": 492.86, "r_x2": 490.706, "r_y2": 483.647, "r_x3": 263.4, "r_y3": 483.647, "coord_origin": "TOPLEFT"}, "text": "vendors technical support on a broad range of IBM ", "orig": "vendors technical support on a broad range of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 504.86, "r_x1": 530.964, "r_y1": 504.86, "r_x2": 530.964, "r_y2": 495.647, "r_x3": 263.4, "r_y3": 495.647, "coord_origin": "TOPLEFT"}, "text": "technologies and products, and supporting customers in the ", "orig": "technologies and products, and supporting customers in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 516.86, "r_x1": 501.633, "r_y1": 516.86, "r_x2": 501.633, "r_y2": 507.647, "r_x3": 263.4, "r_y3": 507.647, "coord_origin": "TOPLEFT"}, "text": "IBM Executive Briefing Center and IBM Project Office.", "orig": "IBM Executive Briefing Center and IBM Project Office.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 536.8389999999999, "r_x1": 348.368, "r_y1": 536.8389999999999, "r_x2": 348.368, "r_y2": 527.626, "r_x3": 263.4, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": "Hernando Bedoya", "orig": "Hernando Bedoya", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.419, "r_y0": 536.8389999999999, "r_x1": 512.352, "r_y1": 536.8389999999999, "r_x2": 512.352, "r_y2": 527.626, "r_x3": 348.419, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": " is a Senior IT Specialist at STG Lab ", "orig": " is a Senior IT Specialist at STG Lab ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 548.8389999999999, "r_x1": 519.272, "r_y1": 548.8389999999999, "r_x2": 519.272, "r_y2": 539.626, "r_x3": 263.4, "r_y3": 539.626, "coord_origin": "TOPLEFT"}, "text": "Services and Training in Rochester, Minnesota. He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. Hernando can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 680.837, "r_x1": 320.585, "r_y1": 680.837, "r_x2": 320.585, "r_y2": 671.624, "r_x3": 263.4, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": "contacted at ", "orig": "contacted at ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.64, "r_y0": 680.473, "r_x1": 410.578, "r_y1": 680.473, "r_x2": 410.578, "r_y2": 672.072, "r_x3": 320.64, "r_y3": 672.072, "coord_origin": "TOPLEFT"}, "text": "hbedoya@us.ibm.com", "orig": "hbedoya@us.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.579, "r_y0": 680.837, "r_x1": 413.348, "r_y1": 680.837, "r_x2": 413.348, "r_y2": 671.624, "r_x3": 410.579, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 538.86, "t": 754.849, "r": 547.208, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.904093861579895, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 64.8, "t": 73.84799999999996, "r": 151.469, "b": 96.048, "coord_origin": "TOPLEFT"}, "confidence": 0.9174711108207703, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 132.649, "r": 547.557, "b": 201.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 189.861, "r_x1": 547.557, "r_y1": 189.861, "r_x2": 547.557, "r_y2": 180.64800000000002, "r_x3": 136.8, "r_y3": 180.64800000000002, "coord_origin": "TOPLEFT"}, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "orig": "of defining, creating, and implementing the row permissions and column masks in a relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 201.861, "r_x1": 238.276, "r_y1": 201.861, "r_x2": 238.276, "r_y2": 192.64800000000002, "r_x3": 136.8, "r_y3": 192.64800000000002, "coord_origin": "TOPLEFT"}, "text": "database environment.", "orig": "database environment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 214.60699999999997, "r": 546.415, "b": 259.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.82000000000005, "r_x1": 524.194, "r_y1": 223.82000000000005, "r_x2": 524.194, "r_y2": 214.60699999999997, "r_x3": 136.8, "r_y3": 214.60699999999997, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application developers, and ", "orig": "This paper is intended for database engineers, data-centric application developers, and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.82000000000005, "r_x1": 546.415, "r_y1": 235.82000000000005, "r_x2": 546.415, "r_y2": 226.60699999999997, "r_x3": 136.8, "r_y3": 226.60699999999997, "coord_origin": "TOPLEFT"}, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "orig": "security officers who want to design and implement RCAC as a part of their data control and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 247.82000000000005, "r_x1": 521.264, "r_y1": 247.82000000000005, "r_x2": 521.264, "r_y2": 238.60699999999997, "r_x3": 136.8, "r_y3": 238.60699999999997, "coord_origin": "TOPLEFT"}, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 64.8, "t": 288.301, "r": 125.338, "b": 303.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.629, "r": 547.246, "b": 341.842, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 263.4, "t": 375.649, "r": 541.74, "b": 516.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. 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He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.86, "t": 754.849, "r": 547.208, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.904093861579895, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "xi"}, {"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.8, "t": 73.84799999999996, "r": 151.469, "b": 96.048, "coord_origin": "TOPLEFT"}, "confidence": 0.9174711108207703, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 132.649, "r": 547.557, "b": 201.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 189.861, "r_x1": 547.557, "r_y1": 189.861, "r_x2": 547.557, "r_y2": 180.64800000000002, "r_x3": 136.8, "r_y3": 180.64800000000002, "coord_origin": "TOPLEFT"}, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "orig": "of defining, creating, and implementing the row permissions and column masks in a relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 201.861, "r_x1": 238.276, "r_y1": 201.861, "r_x2": 238.276, "r_y2": 192.64800000000002, "r_x3": 136.8, "r_y3": 192.64800000000002, "coord_origin": "TOPLEFT"}, "text": "database environment.", "orig": "database environment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 214.60699999999997, "r": 546.415, "b": 259.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.82000000000005, "r_x1": 524.194, "r_y1": 223.82000000000005, "r_x2": 524.194, "r_y2": 214.60699999999997, "r_x3": 136.8, "r_y3": 214.60699999999997, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application developers, and ", "orig": "This paper is intended for database engineers, data-centric application developers, and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.82000000000005, "r_x1": 546.415, "r_y1": 235.82000000000005, "r_x2": 546.415, "r_y2": 226.60699999999997, "r_x3": 136.8, "r_y3": 226.60699999999997, "coord_origin": "TOPLEFT"}, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "orig": "security officers who want to design and implement RCAC as a part of their data control and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 247.82000000000005, "r_x1": 521.264, "r_y1": 247.82000000000005, "r_x2": 521.264, "r_y2": 238.60699999999997, "r_x3": 136.8, "r_y3": 238.60699999999997, "coord_origin": "TOPLEFT"}, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.8, "t": 288.301, "r": 125.338, "b": 303.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.629, "r": 547.246, "b": 341.842, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.4, "t": 375.649, "r": 541.74, "b": 516.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. In the years since, Jim has held ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 480.86, "r_x1": 520.251, "r_y1": 480.86, "r_x2": 520.251, "r_y2": 471.647, "r_x3": 263.4, "r_y3": 471.647, "coord_origin": "TOPLEFT"}, "text": "numerous technical roles, including independent software ", "orig": "numerous technical roles, including independent software ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 492.86, "r_x1": 490.706, "r_y1": 492.86, "r_x2": 490.706, "r_y2": 483.647, "r_x3": 263.4, "r_y3": 483.647, "coord_origin": "TOPLEFT"}, "text": "vendors technical support on a broad range of IBM ", "orig": "vendors technical support on a broad range of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 504.86, "r_x1": 530.964, "r_y1": 504.86, "r_x2": 530.964, "r_y2": 495.647, "r_x3": 263.4, "r_y3": 495.647, "coord_origin": "TOPLEFT"}, "text": "technologies and products, and supporting customers in the ", "orig": "technologies and products, and supporting customers in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 516.86, "r_x1": 501.633, "r_y1": 516.86, "r_x2": 501.633, "r_y2": 507.647, "r_x3": 263.4, "r_y3": 507.647, "coord_origin": "TOPLEFT"}, "text": "IBM Executive Briefing Center and IBM Project Office.", "orig": "IBM Executive Briefing Center and IBM Project Office.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.4, "t": 527.626, "r": 541.582, "b": 680.837, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 536.8389999999999, "r_x1": 348.368, "r_y1": 536.8389999999999, "r_x2": 348.368, "r_y2": 527.626, "r_x3": 263.4, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": "Hernando Bedoya", "orig": "Hernando Bedoya", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.419, "r_y0": 536.8389999999999, "r_x1": 512.352, "r_y1": 536.8389999999999, "r_x2": 512.352, "r_y2": 527.626, "r_x3": 348.419, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": " is a Senior IT Specialist at STG Lab ", "orig": " is a Senior IT Specialist at STG Lab ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 548.8389999999999, "r_x1": 519.272, "r_y1": 548.8389999999999, "r_x2": 519.272, "r_y2": 539.626, "r_x3": 263.4, "r_y3": 539.626, "coord_origin": "TOPLEFT"}, "text": "Services and Training in Rochester, Minnesota. He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. Hernando can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 680.837, "r_x1": 320.585, "r_y1": 680.837, "r_x2": 320.585, "r_y2": 671.624, "r_x3": 263.4, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": "contacted at ", "orig": "contacted at ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.64, "r_y0": 680.473, "r_x1": 410.578, "r_y1": 680.473, "r_x2": 410.578, "r_y2": 672.072, "r_x3": 320.64, "r_y3": 672.072, "coord_origin": "TOPLEFT"}, "text": "hbedoya@us.ibm.com", "orig": "hbedoya@us.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.579, "r_y0": 680.837, "r_x1": 413.348, "r_y1": 680.837, "r_x2": 413.348, "r_y2": 671.624, "r_x3": 410.579, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.4144744873047, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9871657490730286, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.8, "t": 73.84799999999996, "r": 151.469, "b": 96.048, "coord_origin": "TOPLEFT"}, "confidence": 0.9174711108207703, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 132.649, "r": 547.557, "b": 201.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 189.861, "r_x1": 547.557, "r_y1": 189.861, "r_x2": 547.557, "r_y2": 180.64800000000002, "r_x3": 136.8, "r_y3": 180.64800000000002, "coord_origin": "TOPLEFT"}, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "orig": "of defining, creating, and implementing the row permissions and column masks in a relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 201.861, "r_x1": 238.276, "r_y1": 201.861, "r_x2": 238.276, "r_y2": 192.64800000000002, "r_x3": 136.8, "r_y3": 192.64800000000002, "coord_origin": "TOPLEFT"}, "text": "database environment.", "orig": "database environment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 214.60699999999997, "r": 546.415, "b": 259.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.82000000000005, "r_x1": 524.194, "r_y1": 223.82000000000005, "r_x2": 524.194, "r_y2": 214.60699999999997, "r_x3": 136.8, "r_y3": 214.60699999999997, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application developers, and ", "orig": "This paper is intended for database engineers, data-centric application developers, and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.82000000000005, "r_x1": 546.415, "r_y1": 235.82000000000005, "r_x2": 546.415, "r_y2": 226.60699999999997, "r_x3": 136.8, "r_y3": 226.60699999999997, "coord_origin": "TOPLEFT"}, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "orig": "security officers who want to design and implement RCAC as a part of their data control and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 247.82000000000005, "r_x1": 521.264, "r_y1": 247.82000000000005, "r_x2": 521.264, "r_y2": 238.60699999999997, "r_x3": 136.8, "r_y3": 238.60699999999997, "coord_origin": "TOPLEFT"}, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.8, "t": 288.301, "r": 125.338, "b": 303.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.629, "r": 547.246, "b": 341.842, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.4, "t": 375.649, "r": 541.74, "b": 516.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. In the years since, Jim has held ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 480.86, "r_x1": 520.251, "r_y1": 480.86, "r_x2": 520.251, "r_y2": 471.647, "r_x3": 263.4, "r_y3": 471.647, "coord_origin": "TOPLEFT"}, "text": "numerous technical roles, including independent software ", "orig": "numerous technical roles, including independent software ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 492.86, "r_x1": 490.706, "r_y1": 492.86, "r_x2": 490.706, "r_y2": 483.647, "r_x3": 263.4, "r_y3": 483.647, "coord_origin": "TOPLEFT"}, "text": "vendors technical support on a broad range of IBM ", "orig": "vendors technical support on a broad range of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 504.86, "r_x1": 530.964, "r_y1": 504.86, "r_x2": 530.964, "r_y2": 495.647, "r_x3": 263.4, "r_y3": 495.647, "coord_origin": "TOPLEFT"}, "text": "technologies and products, and supporting customers in the ", "orig": "technologies and products, and supporting customers in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 516.86, "r_x1": 501.633, "r_y1": 516.86, "r_x2": 501.633, "r_y2": 507.647, "r_x3": 263.4, "r_y3": 507.647, "coord_origin": "TOPLEFT"}, "text": "IBM Executive Briefing Center and IBM Project Office.", "orig": "IBM Executive Briefing Center and IBM Project Office.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.4, "t": 527.626, "r": 541.582, "b": 680.837, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 536.8389999999999, "r_x1": 348.368, "r_y1": 536.8389999999999, "r_x2": 348.368, "r_y2": 527.626, "r_x3": 263.4, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": "Hernando Bedoya", "orig": "Hernando Bedoya", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.419, "r_y0": 536.8389999999999, "r_x1": 512.352, "r_y1": 536.8389999999999, "r_x2": 512.352, "r_y2": 527.626, "r_x3": 348.419, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": " is a Senior IT Specialist at STG Lab ", "orig": " is a Senior IT Specialist at STG Lab ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 548.8389999999999, "r_x1": 519.272, "r_y1": 548.8389999999999, "r_x2": 519.272, "r_y2": 539.626, "r_x3": 263.4, "r_y3": 539.626, "coord_origin": "TOPLEFT"}, "text": "Services and Training in Rochester, Minnesota. He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. Hernando can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 680.837, "r_x1": 320.585, "r_y1": 680.837, "r_x2": 320.585, "r_y2": 671.624, "r_x3": 263.4, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": "contacted at ", "orig": "contacted at ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.64, "r_y0": 680.473, "r_x1": 410.578, "r_y1": 680.473, "r_x2": 410.578, "r_y2": 672.072, "r_x3": 320.64, "r_y3": 672.072, "coord_origin": "TOPLEFT"}, "text": "hbedoya@us.ibm.com", "orig": "hbedoya@us.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.579, "r_y0": 680.837, "r_x1": 413.348, "r_y1": 680.837, "r_x2": 413.348, "r_y2": 671.624, "r_x3": 410.579, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.4144744873047, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9871657490730286, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.86, "t": 754.849, "r": 547.208, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.904093861579895, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "xi"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, "coord_origin": "TOPLEFT"}, "text": "global businesses of all sizes. The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 538.939, "r_x1": 250.2, "r_y1": 538.939, "r_x2": 250.2, "r_y2": 529.726, "r_x3": 151.2, "r_y3": 529.726, "coord_origin": "TOPLEFT"}, "text": "Security fundamentals", "orig": "Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 550.575, "r_x1": 141.78, "r_y1": 550.575, "r_x2": 141.78, "r_y2": 542.174, "r_x3": 136.8, "r_y3": 542.174, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 550.939, "r_x1": 282.955, "r_y1": 550.939, "r_x2": 282.955, "r_y2": 541.726, "r_x3": 151.2, "r_y3": 541.726, "coord_origin": "TOPLEFT"}, "text": "Current state of IBM i security", "orig": "Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.88, "r_y0": 734.68, "r_x1": 231.872, "r_y1": 734.68, "r_x2": 231.872, "r_y2": 727.949, "r_x3": 143.88, "r_y3": 727.949, "coord_origin": "TOPLEFT"}, "text": "http://www.ponemon.org", "orig": "http://www.ponemon.org", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 231.84, "r_y0": 734.972, "r_x1": 234.059, "r_y1": 734.972, "r_x2": 234.059, "r_y2": 727.59, "r_x3": 231.84, "r_y3": 727.59, "coord_origin": "TOPLEFT"}, "text": "/", "orig": "/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9364771246910095, "cells": 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 541.68, "t": 754.849, "r": 547.218, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 81.0, "t": 268.543, "r": 115.155, "b": 274.981, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88599999999997, "r": 549.157, "b": 309.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9371159076690674, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 348.709, "r": 547.503, "b": 429.921, "coord_origin": "TOPLEFT"}, "confidence": 0.9868631362915039, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, "coord_origin": "TOPLEFT"}, "text": "global businesses of all sizes. The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 442.728, "r": 527.156, "b": 487.94, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 500.687, "r": 547.404, "b": 521.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 529.726, "r": 250.2, "b": 538.939, "coord_origin": "TOPLEFT"}, "confidence": 0.9373421669006348, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.68, "t": 754.849, "r": 547.218, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "text", "id": 19, "page_no": 4, "cluster": {"id": 19, "label": "text", "bbox": {"l": 81.0, "t": 268.543, "r": 115.155, "b": 274.981, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 1."}, {"label": "section_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88599999999997, "r": 549.157, "b": 309.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9371159076690674, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Securing and protecting IBM DB2 data"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 348.709, "r": 547.503, "b": 429.921, "coord_origin": "TOPLEFT"}, "confidence": 0.9868631362915039, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, "coord_origin": "TOPLEFT"}, "text": "global businesses of all sizes. The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 442.728, "r": 527.156, "b": 487.94, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 500.687, "r": 547.404, "b": 521.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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"text": "2 http://www.ponemon.org /"}, {"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.075252532958984, "t": 70.57737731933594, "r": 239.620361328125, "b": 237.9579315185547, "coord_origin": "TOPLEFT"}, "confidence": 0.7509335279464722, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 19, "page_no": 4, "cluster": {"id": 19, "label": "text", "bbox": {"l": 81.0, "t": 268.543, "r": 115.155, "b": 274.981, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 1."}, {"label": "section_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88599999999997, "r": 549.157, "b": 309.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9371159076690674, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Securing and protecting IBM DB2 data"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 348.709, "r": 547.503, "b": 429.921, "coord_origin": "TOPLEFT"}, "confidence": 0.9868631362915039, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, 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The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 442.728, "r": 527.156, "b": 487.94, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 500.687, "r": 547.404, "b": 521.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:"}, {"label": "list_item", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 529.726, "r": 250.2, "b": 538.939, "coord_origin": "TOPLEFT"}, "confidence": 0.9373421669006348, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 538.939, "r_x1": 250.2, "r_y1": 538.939, "r_x2": 250.2, "r_y2": 529.726, "r_x3": 151.2, "r_y3": 529.726, "coord_origin": "TOPLEFT"}, "text": "Security fundamentals", "orig": "Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, 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"text": "2 http://www.ponemon.org /"}, {"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.075252532958984, "t": 70.57737731933594, "r": 239.620361328125, "b": 237.9579315185547, "coord_origin": "TOPLEFT"}, "confidence": 0.7509335279464722, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9364771246910095, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.68, "t": 754.849, "r": 547.218, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 127.822, "r_x1": 324.463, "r_y1": 127.822, "r_x2": 324.463, "r_y2": 118.60900000000004, "r_x3": 136.8, "r_y3": 118.60900000000004, "coord_origin": "TOPLEFT"}, "text": "information assets that must be described:", "orig": "information assets that must be described:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 74.34100000000001, "r": 267.39, "b": 89.10400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.60900000000004, "r": 545.014, "b": 127.822, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 127.822, "r_x1": 324.463, "r_y1": 127.822, "r_x2": 324.463, "r_y2": 118.60900000000004, "r_x3": 136.8, "r_y3": 118.60900000000004, "coord_origin": "TOPLEFT"}, "text": "information assets that must be described:", "orig": "information assets that must be described:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.48699999999997, "r": 547.354, "b": 180.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 151.199, "t": 188.62800000000004, "r": 547.57, "b": 269.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 151.199, "t": 277.60699999999997, "r": 542.001, "b": 286.82, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.799, "t": 294.487, "r": 547.407, "b": 375.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 388.607, "r": 535.371, "b": 409.82, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 438.301, "r": 323.37, "b": 453.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.629, "r": 547.567, "b": 515.841, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.648, "r": 547.593, "b": 585.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.667, "r": 547.652, "b": 679.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"index": 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false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 74.34100000000001, "r": 267.39, "b": 89.10400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.60900000000004, "r": 545.014, "b": 127.822, "coord_origin": "TOPLEFT"}, 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false}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.48699999999997, "r": 547.354, "b": 180.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.199, "t": 188.62800000000004, "r": 547.57, "b": 269.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.199, "t": 277.60699999999997, "r": 542.001, "b": 286.82, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.799, "t": 294.487, "r": 547.407, "b": 375.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 388.607, "r": 535.371, "b": 409.82, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 438.301, "r": 323.37, "b": 453.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.629, "r": 547.567, "b": 515.841, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.648, "r": 547.593, "b": 585.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.667, "r": 547.652, "b": 679.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 74.34100000000001, "r": 267.39, "b": 89.10400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.60900000000004, "r": 545.014, "b": 127.822, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 127.822, "r_x1": 324.463, "r_y1": 127.822, "r_x2": 324.463, "r_y2": 118.60900000000004, "r_x3": 136.8, "r_y3": 118.60900000000004, "coord_origin": "TOPLEFT"}, "text": "information assets that must be described:", "orig": "information assets that must be described:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.48699999999997, "r": 547.354, "b": 180.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.199, "t": 188.62800000000004, "r": 547.57, "b": 269.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.199, "t": 277.60699999999997, "r": 542.001, "b": 286.82, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.799, "t": 294.487, "r": 547.407, "b": 375.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 388.607, "r": 535.371, "b": 409.82, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 438.301, "r": 323.37, "b": 453.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.629, "r": 547.567, "b": 515.841, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.648, "r": 547.593, "b": 585.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.667, "r": 547.652, "b": 679.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 544.312, "b": 152.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 173.33500000000004, "r": 301.46, "b": 185.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 199.48900000000003, "r": 541.576, "b": 256.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 547.69, "b": 314.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 327.527, "r": 547.541, "b": 360.739, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.462, "b": 700.143, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 10, "label": "text", "bbox": {"l": 180.959, "t": 383.25, "r": 212.54, "b": 389.388, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 170.006, "t": 390.746, "r": 226.926, "b": 396.884, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 544.312, "b": 152.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 173.33500000000004, "r": 301.46, "b": 185.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 199.48900000000003, "r": 541.576, "b": 256.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 547.69, "b": 314.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 327.527, "r": 547.541, "b": 360.739, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.462, "b": 700.143, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 10, "label": "text", "bbox": {"l": 180.959, "t": 383.25, "r": 212.54, "b": 389.388, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 170.006, "t": 390.746, "r": 226.926, "b": 396.884, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 544.312, "b": 152.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 173.33500000000004, "r": 301.46, "b": 185.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 199.48900000000003, "r": 541.576, "b": 256.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 547.69, "b": 314.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 327.527, "r": 547.541, "b": 360.739, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.462, "b": 700.143, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, 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"cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 123.74099999999999, "r_x1": 253.222, "r_y1": 123.74099999999999, "r_x2": 253.222, "r_y2": 114.52800000000002, "r_x3": 151.2, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Work Function Usage (", "orig": "Work Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.26, "r_y0": 123.42399999999998, "r_x1": 298.2, "r_y1": 123.42399999999998, "r_x2": 298.2, "r_y2": 114.976, "r_x3": 253.26, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "WRKFCNUSG", "orig": "WRKFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 298.201, "r_y0": 123.74099999999999, "r_x1": 301.517, "r_y1": 123.74099999999999, "r_x2": 301.517, "r_y2": 114.52800000000002, "r_x3": 298.201, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 135.74099999999999, "r_x1": 265.099, "r_y1": 135.74099999999999, "r_x2": 265.099, "r_y2": 126.52800000000002, "r_x3": 151.201, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Change Function Usage (", "orig": "Change Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.081, "r_y0": 135.42399999999998, "r_x1": 310.02, "r_y1": 135.42399999999998, "r_x2": 310.02, "r_y2": 126.976, "r_x3": 265.081, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.081, "r_y0": 135.74099999999999, "r_x1": 313.398, "r_y1": 135.74099999999999, "r_x2": 313.398, "r_y2": 126.52800000000002, "r_x3": 310.081, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 147.74099999999999, "r_x1": 262.535, "r_y1": 147.74099999999999, "r_x2": 262.535, "r_y2": 138.52800000000002, "r_x3": 151.201, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Display Function Usage (", "orig": "Display Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.561, "r_y0": 147.42399999999998, "r_x1": 307.5, "r_y1": 147.42399999999998, "r_x2": 307.5, "r_y2": 138.976, "r_x3": 262.561, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "DSPFCNUSG", "orig": "DSPFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 307.5, "r_y0": 147.74099999999999, "r_x1": 310.817, "r_y1": 147.74099999999999, "r_x2": 310.817, "r_y2": 138.52800000000002, "r_x3": 307.5, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 673.762, "r_x1": 547.592, "r_y1": 673.762, "r_x2": 547.592, "r_y2": 664.549, "r_x3": 136.8, "r_y3": 664.549, "coord_origin": "TOPLEFT"}, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 697.761, "r_x1": 544.288, "r_y1": 697.761, "r_x2": 544.288, "r_y2": 688.548, "r_x3": 136.8, "r_y3": 688.548, "coord_origin": "TOPLEFT"}, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "orig": "administrative functions to be divided across individuals without overlapping responsibilities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 709.761, "r_x1": 536.25, "r_y1": 709.761, "r_x2": 536.25, "r_y2": 700.548, "r_x3": 136.8, "r_y3": 700.548, "coord_origin": "TOPLEFT"}, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "orig": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 304.863, "r_x1": 202.253, "r_y1": 304.863, "r_x2": 202.253, "r_y2": 296.538, "r_x3": 142.8, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Column name", "orig": "Column name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.809, "r_y0": 304.863, "r_x1": 257.219, "r_y1": 304.863, "r_x2": 257.219, "r_y2": 296.538, "r_x3": 216.809, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Data type", "orig": "Data type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.475, "r_y0": 304.863, "r_x1": 338.903, "r_y1": 304.863, "r_x2": 338.903, "r_y2": 296.538, "r_x3": 289.475, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Description", "orig": "Description", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 323.883, "r_x1": 203.243, "r_y1": 323.883, "r_x2": 203.243, "r_y2": 315.558, "r_x3": 142.8, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "FUNCTION_ID", "orig": "FUNCTION_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.785, "r_y0": 323.883, "r_x1": 276.014, "r_y1": 323.883, "r_x2": 276.014, "r_y2": 315.558, "r_x3": 216.785, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(30)", "orig": "VARCHAR(30)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.458, "r_y0": 323.883, "r_x1": 359.865, "r_y1": 323.883, "r_x2": 359.865, "r_y2": 315.558, "r_x3": 289.458, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "ID of the function.", "orig": "ID of the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 342.843, "r_x1": 198.685, "r_y1": 342.843, "r_x2": 198.685, "r_y2": 334.518, "r_x3": 142.8, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "USER_NAME", "orig": "USER_NAME", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.741, "r_y0": 342.843, "r_x1": 275.939, "r_y1": 342.843, "r_x2": 275.939, "r_y2": 334.518, "r_x3": 216.741, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(10)", "orig": "VARCHAR(10)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.382, "r_y0": 342.843, "r_x1": 515.035, "r_y1": 342.843, "r_x2": 515.035, "r_y2": 334.518, "r_x3": 289.382, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "Name of the user profile that has a usage setting for this ", "orig": "Name of the user profile that has a usage setting for this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 353.883, "r_x1": 323.439, "r_y1": 353.883, "r_x2": 323.439, "r_y2": 345.558, "r_x3": 289.44, "r_y3": 345.558, "coord_origin": "TOPLEFT"}, "text": "function.", "orig": "function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 372.844, "r_x1": 173.997, "r_y1": 372.844, "r_x2": 173.997, "r_y2": 364.519, "r_x3": 142.8, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "USAGE", "orig": "USAGE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.774, "r_y0": 372.844, "r_x1": 270.993, "r_y1": 372.844, "r_x2": 270.993, "r_y2": 364.519, "r_x3": 216.774, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(7)", "orig": "VARCHAR(7)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.416, "r_y0": 372.844, "r_x1": 346.901, "r_y1": 372.844, "r_x2": 346.901, "r_y2": 364.519, "r_x3": 289.416, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "Usage setting:", "orig": "Usage setting:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 383.555, "r_x1": 293.94, "r_y1": 383.555, "r_x2": 293.94, "r_y2": 375.964, "r_x3": 289.44, "r_y3": 375.964, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 383.884, "r_x1": 535.18, "r_y1": 383.884, "r_x2": 535.18, "r_y2": 375.559, "r_x3": 303.84, "r_y3": 375.559, "coord_origin": "TOPLEFT"}, "text": "ALLOWED: The user profile is allowed to use the function.", "orig": "ALLOWED: The user profile is allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 394.535, "r_x1": 293.94, "r_y1": 394.535, "r_x2": 293.94, "r_y2": 386.944, "r_x3": 289.44, "r_y3": 386.944, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 394.864, "r_x1": 539.121, "r_y1": 394.864, "r_x2": 539.121, "r_y2": 386.539, "r_x3": 303.84, "r_y3": 386.539, "coord_origin": "TOPLEFT"}, "text": "DENIED: The user profile is not allowed to use the function.", "orig": "DENIED: The user profile is not allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 413.884, "r_x1": 196.235, "r_y1": 413.884, "r_x2": 196.235, "r_y2": 405.559, "r_x3": 142.8, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "USER_TYPE", "orig": "USER_TYPE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.752, "r_y0": 413.884, "r_x1": 271.009, "r_y1": 413.884, "r_x2": 271.009, "r_y2": 405.559, "r_x3": 216.752, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(5)", "orig": "VARCHAR(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.432, "r_y0": 413.884, "r_x1": 367.811, "r_y1": 413.884, "r_x2": 367.811, "r_y2": 405.559, "r_x3": 289.432, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "Type of user profile:", "orig": "Type of user profile:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 424.534, "r_x1": 293.94, "r_y1": 424.534, "r_x2": 293.94, "r_y2": 416.944, "r_x3": 289.44, "r_y3": 416.944, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 424.864, "r_x1": 434.795, "r_y1": 424.864, "r_x2": 434.795, "r_y2": 416.539, "r_x3": 303.84, "r_y3": 416.539, "coord_origin": "TOPLEFT"}, "text": "USER: The user profile is a user.", "orig": "USER: The user profile is a user.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 435.514, "r_x1": 293.94, "r_y1": 435.514, "r_x2": 293.94, "r_y2": 427.924, "r_x3": 289.44, "r_y3": 427.924, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 435.844, "r_x1": 448.132, "r_y1": 435.844, "r_x2": 448.132, "r_y2": 427.519, "r_x3": 303.84, "r_y3": 427.519, "coord_origin": "TOPLEFT"}, "text": "GROUP: The user profile is a group.", "orig": "GROUP: The user profile is a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52800000000002, "r": 301.517, "b": 123.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 123.74099999999999, "r_x1": 253.222, "r_y1": 123.74099999999999, "r_x2": 253.222, "r_y2": 114.52800000000002, "r_x3": 151.2, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Work Function Usage (", "orig": "Work Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.26, "r_y0": 123.42399999999998, "r_x1": 298.2, "r_y1": 123.42399999999998, "r_x2": 298.2, "r_y2": 114.976, "r_x3": 253.26, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "WRKFCNUSG", "orig": "WRKFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 298.201, "r_y0": 123.74099999999999, "r_x1": 301.517, "r_y1": 123.74099999999999, "r_x2": 301.517, "r_y2": 114.52800000000002, "r_x3": 298.201, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.52800000000002, "r": 313.398, "b": 135.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 135.74099999999999, "r_x1": 265.099, "r_y1": 135.74099999999999, "r_x2": 265.099, "r_y2": 126.52800000000002, "r_x3": 151.201, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Change Function Usage (", "orig": "Change Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.081, "r_y0": 135.42399999999998, "r_x1": 310.02, "r_y1": 135.42399999999998, "r_x2": 310.02, "r_y2": 126.976, "r_x3": 265.081, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.081, "r_y0": 135.74099999999999, "r_x1": 313.398, "r_y1": 135.74099999999999, "r_x2": 313.398, "r_y2": 126.52800000000002, "r_x3": 310.081, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.52800000000002, "r": 310.817, "b": 147.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 147.74099999999999, "r_x1": 262.535, "r_y1": 147.74099999999999, "r_x2": 262.535, "r_y2": 138.52800000000002, "r_x3": 151.201, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Display Function Usage (", "orig": "Display Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.561, "r_y0": 147.42399999999998, "r_x1": 307.5, "r_y1": 147.42399999999998, "r_x2": 307.5, "r_y2": 138.976, "r_x3": 262.561, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "DSPFCNUSG", "orig": "DSPFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 307.5, "r_y0": 147.74099999999999, "r_x1": 310.817, "r_y1": 147.74099999999999, "r_x2": 310.817, "r_y2": 138.52800000000002, "r_x3": 307.5, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 512.547, "b": 181.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 266.722, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 473.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.8, "t": 515.957, "r": 241.739, "b": 524.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 136.8, "t": 527.956, "r": 221.699, "b": 536.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 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Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 697.761, "r_x1": 544.288, "r_y1": 697.761, "r_x2": 544.288, "r_y2": 688.548, "r_x3": 136.8, "r_y3": 688.548, "coord_origin": "TOPLEFT"}, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "orig": "administrative functions to be divided across individuals without overlapping responsibilities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 709.761, "r_x1": 536.25, "r_y1": 709.761, "r_x2": 536.25, "r_y2": 700.548, "r_x3": 136.8, "r_y3": 700.548, "coord_origin": "TOPLEFT"}, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "orig": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [{"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 304.863, "r_x1": 202.253, "r_y1": 304.863, "r_x2": 202.253, "r_y2": 296.538, "r_x3": 142.8, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Column name", "orig": "Column name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.809, "r_y0": 304.863, "r_x1": 257.219, "r_y1": 304.863, "r_x2": 257.219, "r_y2": 296.538, "r_x3": 216.809, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Data type", "orig": "Data type", "text_direction": 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"VARCHAR(30)", "orig": "VARCHAR(30)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.458, "r_y0": 323.883, "r_x1": 359.865, "r_y1": 323.883, "r_x2": 359.865, "r_y2": 315.558, "r_x3": 289.458, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "ID of the function.", "orig": "ID of the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 342.843, "r_x1": 198.685, "r_y1": 342.843, "r_x2": 198.685, "r_y2": 334.518, "r_x3": 142.8, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "USER_NAME", "orig": "USER_NAME", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.741, "r_y0": 342.843, "r_x1": 275.939, "r_y1": 342.843, "r_x2": 275.939, "r_y2": 334.518, "r_x3": 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"from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 383.555, "r_x1": 293.94, "r_y1": 383.555, "r_x2": 293.94, "r_y2": 375.964, "r_x3": 289.44, "r_y3": 375.964, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 383.884, "r_x1": 535.18, "r_y1": 383.884, "r_x2": 535.18, "r_y2": 375.559, "r_x3": 303.84, "r_y3": 375.559, "coord_origin": "TOPLEFT"}, "text": "ALLOWED: The user profile is allowed to use the function.", "orig": "ALLOWED: The user profile is allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 394.535, "r_x1": 293.94, "r_y1": 394.535, "r_x2": 293.94, "r_y2": 386.944, "r_x3": 289.44, "r_y3": 386.944, 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435.514, "r_x1": 293.94, "r_y1": 435.514, "r_x2": 293.94, "r_y2": 427.924, "r_x3": 289.44, "r_y3": 427.924, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 63, "label": "text", "bbox": {"l": 303.84, "t": 427.519, "r": 448.132, "b": 435.844, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 435.844, "r_x1": 448.132, "r_y1": 435.844, "r_x2": 448.132, "r_y2": 427.519, "r_x3": 303.84, "r_y3": 427.519, "coord_origin": "TOPLEFT"}, "text": "GROUP: The user profile is a group.", "orig": "GROUP: The user profile is a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 5, "num_cols": 3, "table_cells": [{"bbox": {"l": 142.8, "t": 296.538, "r": 202.253, "b": 304.863, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Column name", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 216.809, "t": 296.538, "r": 257.219, "b": 304.863, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Data type", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 289.475, "t": 296.538, "r": 338.903, "b": 304.863, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Description", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 315.558, "r": 203.243, "b": 323.883, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "FUNCTION_ID", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.785, "t": 315.558, "r": 276.014, "b": 323.883, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(30)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.458, "t": 315.558, "r": 359.865, "b": 323.883, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "ID of the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 334.518, "r": 198.685, "b": 342.843, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.741, "t": 334.518, "r": 275.939, "b": 342.843, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(10)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.382, "t": 334.518, "r": 515.035, "b": 353.883, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the user profile that has a usage setting for this function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 364.519, "r": 173.997, "b": 372.844, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USAGE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.774, "t": 364.519, "r": 270.993, "b": 372.844, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(7)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.416, "t": 364.519, "r": 539.121, "b": 394.864, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.559, "r": 196.235, "b": 413.884, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.752, "t": 405.559, "r": 271.009, "b": 413.884, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.432, "t": 405.559, "r": 448.132, "b": 435.844, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52800000000002, "r": 301.517, "b": 123.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 123.74099999999999, "r_x1": 253.222, "r_y1": 123.74099999999999, "r_x2": 253.222, "r_y2": 114.52800000000002, "r_x3": 151.2, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Work Function Usage (", "orig": "Work Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.26, "r_y0": 123.42399999999998, "r_x1": 298.2, "r_y1": 123.42399999999998, "r_x2": 298.2, "r_y2": 114.976, "r_x3": 253.26, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "WRKFCNUSG", "orig": "WRKFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 298.201, "r_y0": 123.74099999999999, "r_x1": 301.517, "r_y1": 123.74099999999999, "r_x2": 301.517, "r_y2": 114.52800000000002, "r_x3": 298.201, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.52800000000002, "r": 313.398, "b": 135.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 135.74099999999999, "r_x1": 265.099, "r_y1": 135.74099999999999, "r_x2": 265.099, "r_y2": 126.52800000000002, "r_x3": 151.201, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Change Function Usage (", "orig": "Change Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.081, "r_y0": 135.42399999999998, "r_x1": 310.02, "r_y1": 135.42399999999998, "r_x2": 310.02, "r_y2": 126.976, "r_x3": 265.081, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.081, "r_y0": 135.74099999999999, "r_x1": 313.398, "r_y1": 135.74099999999999, "r_x2": 313.398, "r_y2": 126.52800000000002, "r_x3": 310.081, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.52800000000002, "r": 310.817, "b": 147.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 147.74099999999999, "r_x1": 262.535, "r_y1": 147.74099999999999, "r_x2": 262.535, "r_y2": 138.52800000000002, "r_x3": 151.201, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Display Function Usage (", "orig": "Display Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.561, "r_y0": 147.42399999999998, "r_x1": 307.5, "r_y1": 147.42399999999998, "r_x2": 307.5, "r_y2": 138.976, "r_x3": 262.561, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "DSPFCNUSG", "orig": "DSPFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 307.5, "r_y0": 147.74099999999999, "r_x1": 310.817, "r_y1": 147.74099999999999, "r_x2": 310.817, "r_y2": 138.52800000000002, "r_x3": 307.5, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 512.547, "b": 181.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 266.722, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 473.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.8, "t": 515.957, "r": 241.739, "b": 524.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 136.8, "t": 527.956, "r": 221.699, "b": 536.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 547.592, "b": 709.761, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 673.762, "r_x1": 547.592, "r_y1": 673.762, "r_x2": 547.592, "r_y2": 664.549, "r_x3": 136.8, "r_y3": 664.549, "coord_origin": "TOPLEFT"}, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 697.761, "r_x1": 544.288, "r_y1": 697.761, "r_x2": 544.288, "r_y2": 688.548, "r_x3": 136.8, "r_y3": 688.548, "coord_origin": "TOPLEFT"}, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "orig": "administrative functions to be divided across individuals without overlapping responsibilities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 709.761, "r_x1": 536.25, "r_y1": 709.761, "r_x2": 536.25, "r_y2": 700.548, "r_x3": 136.8, "r_y3": 700.548, "coord_origin": "TOPLEFT"}, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "orig": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. 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"r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 266.722, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 473.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.8, "t": 515.957, "r": 241.739, "b": 524.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 136.8, "t": 527.956, "r": 221.699, "b": 536.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 547.592, "b": 709.761, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 673.762, "r_x1": 547.592, "r_y1": 673.762, "r_x2": 547.592, "r_y2": 664.549, "r_x3": 136.8, "r_y3": 664.549, "coord_origin": "TOPLEFT"}, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. 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"10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.32, "r_y0": 763.863, "r_x1": 523.523, "r_y1": 763.863, "r_x2": 523.523, "r_y2": 755.538, "r_x3": 355.32, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 542.703, "r_y1": 80.72199999999998, "r_x2": 542.703, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 513.687, "r_y1": 92.72199999999998, "r_x2": 513.687, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. ", "orig": "table. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.1, "r_y0": 220.70000000000005, "r_x1": 232.869, "r_y1": 220.70000000000005, "r_x2": 232.869, "r_y2": 211.48699999999997, "r_x3": 230.1, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70000000000005, "r_x1": 346.314, "r_y1": 232.70000000000005, "r_x2": 346.314, "r_y2": 223.48699999999997, "r_x3": 136.8, "r_y3": 223.48699999999997, "coord_origin": "TOPLEFT"}, "text": "authority and can be given to a user or a group.", "orig": "authority and can be given to a user or a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.71900000000005, "r_x1": 529.471, "r_y1": 266.71900000000005, "r_x2": 529.471, "r_y2": 257.506, "r_x3": 136.8, "r_y3": 257.506, "coord_origin": "TOPLEFT"}, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "orig": "a user is allowed to access in a table and whether a user is allowed to see information in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.71900000000005, "r_x1": 253.462, "r_y1": 278.71900000000005, "r_x2": 253.462, "r_y2": 269.506, "r_x3": 136.8, "r_y3": 269.506, "coord_origin": "TOPLEFT"}, "text": "certain columns of a table.", "orig": "certain columns of a table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 312.738, "r_x1": 539.816, "r_y1": 312.738, "r_x2": 539.816, "r_y2": 303.525, "r_x3": 136.8, "r_y3": 303.525, "coord_origin": "TOPLEFT"}, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 370.697, "r_x1": 343.798, "r_y1": 370.697, "r_x2": 343.798, "r_y2": 361.484, "r_x3": 136.8, "r_y3": 361.484, "coord_origin": "TOPLEFT"}, "text": "the different CL commands and DB2 for i tools.", "orig": "the different CL commands and DB2 for i tools.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 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Roles and separation of duties ", "orig": "Chapter 2. 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 153.52800000000002, "r": 547.673, "b": 198.74, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. 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The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 153.52800000000002, "r": 547.673, "b": 198.74, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. ", "orig": "table. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 211.48699999999997, "r": 538.66, "b": 232.70000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70000000000005, "r_x1": 346.314, "r_y1": 232.70000000000005, "r_x2": 346.314, "r_y2": 223.48699999999997, "r_x3": 136.8, "r_y3": 223.48699999999997, "coord_origin": "TOPLEFT"}, "text": "authority and can be given to a user or a group.", "orig": "authority and can be given to a user or a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.8, "t": 245.50599999999997, "r": 545.805, "b": 278.71900000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.71900000000005, "r_x1": 529.471, "r_y1": 266.71900000000005, "r_x2": 529.471, "r_y2": 257.506, "r_x3": 136.8, "r_y3": 257.506, "coord_origin": "TOPLEFT"}, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "orig": "a user is allowed to access in a table and whether a user is allowed to see information in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.71900000000005, "r_x1": 253.462, "r_y1": 278.71900000000005, "r_x2": 253.462, "r_y2": 269.506, "r_x3": 136.8, "r_y3": 269.506, "coord_origin": "TOPLEFT"}, "text": "certain columns of a table.", "orig": "certain columns of a table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 291.525, "r": 539.816, "b": 336.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 312.738, "r_x1": 539.816, "r_y1": 312.738, "r_x2": 539.816, "r_y2": 303.525, "r_x3": 136.8, "r_y3": 303.525, "coord_origin": "TOPLEFT"}, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 349.485, "r": 543.076, "b": 370.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 370.697, "r_x1": 343.798, "r_y1": 370.697, "r_x2": 343.798, "r_y2": 361.484, "r_x3": 136.8, "r_y3": 361.484, "coord_origin": "TOPLEFT"}, "text": "the different CL commands and DB2 for i tools.", "orig": "the different CL commands and DB2 for i tools.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 64.8, "t": 383.538, "r": 391.767, "b": 391.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9337189793586731, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 391.863, "r_x1": 391.767, "r_y1": 391.863, "r_x2": 391.767, "r_y2": 383.538, "r_x3": 64.8, "r_y3": 383.538, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 64.41139221191406, "t": 393.6136169433594, "r": 547.3950805664062, "b": 721.60791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9899571537971497, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 408.843, "r_x1": 119.758, "r_y1": 408.843, "r_x2": 119.758, "r_y2": 400.518, "r_x3": 70.8, "r_y3": 400.518, "coord_origin": "TOPLEFT"}, "text": "User action", "orig": "User action", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.263, "r_y0": 487.02, "r_x1": 433.263, "r_y1": 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 153.52800000000002, "r": 547.673, "b": 198.74, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. 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", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 211.48699999999997, "r": 538.66, "b": 232.70000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70000000000005, "r_x1": 346.314, "r_y1": 232.70000000000005, "r_x2": 346.314, "r_y2": 223.48699999999997, "r_x3": 136.8, "r_y3": 223.48699999999997, "coord_origin": "TOPLEFT"}, "text": "authority and can be given to a user or a group.", "orig": "authority and can be given to a user or a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.8, "t": 245.50599999999997, "r": 545.805, "b": 278.71900000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.71900000000005, "r_x1": 529.471, "r_y1": 266.71900000000005, "r_x2": 529.471, "r_y2": 257.506, "r_x3": 136.8, "r_y3": 257.506, "coord_origin": "TOPLEFT"}, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "orig": "a user is allowed to access in a table and whether a user is allowed to see information in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.71900000000005, "r_x1": 253.462, "r_y1": 278.71900000000005, "r_x2": 253.462, "r_y2": 269.506, "r_x3": 136.8, "r_y3": 269.506, "coord_origin": "TOPLEFT"}, "text": "certain columns of a table.", "orig": "certain columns of a table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 291.525, "r": 539.816, "b": 336.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 312.738, "r_x1": 539.816, "r_y1": 312.738, "r_x2": 539.816, "r_y2": 303.525, "r_x3": 136.8, "r_y3": 303.525, "coord_origin": "TOPLEFT"}, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 349.485, "r": 543.076, "b": 370.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 370.697, "r_x1": 343.798, "r_y1": 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Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 2. 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It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. 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Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9071078896522522, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "15", "orig": "15", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 528.74, "b": 92.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.872844398021698, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 179.591, "r_y1": 80.72199999999998, "r_x2": 179.591, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The SQL ", "orig": "The SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.58, "r_y0": 80.40499999999997, "r_x1": 264.479, "r_y1": 80.40499999999997, "r_x2": 264.479, "r_y2": 71.957, "r_x3": 179.58, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION", "orig": "CREATE PERMISSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.54, "r_y0": 80.72199999999998, "r_x1": 528.74, "r_y1": 80.72199999999998, "r_x2": 528.74, "r_y2": 71.50900000000001, "r_x3": 264.54, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": " statement that is shown in Figure 3-1 is used to define and ", "orig": " statement that is shown in Figure 3-1 is used to define and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 341.703, "r_y1": 92.72199999999998, "r_x2": 341.703, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "initially enable or disable the row access rules.", "orig": "initially enable or disable the row access rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 136.8, "t": 414.138, "r": 341.985, "b": 422.463, "coord_origin": "TOPLEFT"}, "confidence": 0.938783586025238, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 422.463, "r_x1": 341.985, "r_y1": 422.463, "r_x2": 341.985, "r_y2": 414.138, "r_x3": 136.8, "r_y3": 414.138, "coord_origin": "TOPLEFT"}, "text": "Figure 3-1 CREATE PERMISSION SQL statement", "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 136.8, "t": 439.944, "r": 215.383, "b": 451.044, "coord_origin": "TOPLEFT"}, "confidence": 0.9477567076683044, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.044, "r_x1": 215.383, "r_y1": 451.044, "r_x2": 215.383, "r_y2": 439.944, "r_x3": 136.8, "r_y3": 439.944, "coord_origin": "TOPLEFT"}, "text": "Column mask", "orig": "Column mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 455.089, "r": 542.776, "b": 500.301, "coord_origin": "TOPLEFT"}, "confidence": 0.9843975901603699, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 464.302, "r_x1": 542.776, "r_y1": 464.302, "r_x2": 542.776, "r_y2": 455.089, "r_x3": 136.8, "r_y3": 455.089, "coord_origin": "TOPLEFT"}, "text": "A column mask is a database object that manifests a column value access control rule for a ", "orig": "A column mask is a database object that manifests a column value access control rule for a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 476.302, "r_x1": 537.85, "r_y1": 476.302, "r_x2": 537.85, "r_y2": 467.089, "r_x3": 136.8, "r_y3": 467.089, "coord_origin": "TOPLEFT"}, "text": "specific column in a specific table. It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. For example, a teller can see only the last four digits of a tax ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 500.301, "r_x1": 231.178, "r_y1": 500.301, "r_x2": 231.178, "r_y2": 491.088, "r_x3": 136.8, "r_y3": 491.088, "coord_origin": "TOPLEFT"}, "text": "identification number.", "orig": "identification number.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 135.97177124023438, "t": 107.41075134277344, "r": 545.4180908203125, "b": 410.60931396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9788318872451782, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 149.37099999999998, "r_x1": 251.916, "r_y1": 149.37099999999998, "r_x2": 251.916, "r_y2": 140.20799999999997, "r_x3": 148.134, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION ", "orig": "CREATE PERMISSION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.867, "r_y0": 149.36, "r_x1": 257.586, "r_y1": 149.36, "r_x2": 257.586, "r_y2": 140.20799999999997, "r_x3": 251.867, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.592, "r_y0": 149.37099999999998, "r_x1": 337.002, "r_y1": 149.37099999999998, "r_x2": 337.002, "r_y2": 140.20799999999997, "r_x3": 257.592, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "permission name", "orig": "permission name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.012, 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"orig": "ON", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.584, "r_y0": 190.74300000000005, "r_x1": 174.303, "r_y1": 190.74300000000005, "r_x2": 174.303, "r_y2": 181.591, "r_x3": 168.584, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.309, "r_y0": 190.75400000000002, "r_x1": 226.875, "r_y1": 190.75400000000002, "r_x2": 226.875, "r_y2": 181.591, "r_x3": 174.309, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "table name", "orig": "table name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.865, "r_y0": 190.74300000000005, "r_x1": 232.584, "r_y1": 190.74300000000005, "r_x2": 232.584, "r_y2": 181.591, "r_x3": 226.865, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 311.32, "r_y0": 174.05600000000004, "r_x1": 529.937, "r_y1": 174.05600000000004, "r_x2": 529.937, "r_y2": 166.71799999999996, "r_x3": 311.32, "r_y3": 166.71799999999996, "coord_origin": "TOPLEFT"}, "text": "Identifies the table on which the row permission is created", "orig": "Identifies the table on which the row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 232.13699999999994, "r_x1": 163.113, "r_y1": 232.13699999999994, "r_x2": 163.113, "r_y2": 222.97400000000005, "r_x3": 148.134, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "AS ", "orig": "AS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.687, "r_y0": 232.12599999999998, "r_x1": 171.406, "r_y1": 232.12599999999998, "r_x2": 171.406, "r_y2": 222.97400000000005, "r_x3": 165.687, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.411, "r_y0": 232.13699999999994, "r_x1": 251.207, "r_y1": 232.13699999999994, "r_x2": 251.207, "r_y2": 222.97400000000005, "r_x3": 171.411, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "correlation name", "orig": "correlation name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.211, "r_y0": 232.12599999999998, "r_x1": 256.93, "r_y1": 232.12599999999998, "r_x2": 256.93, "r_y2": 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{"id": 2, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9526246786117554, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9071078896522522, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "15", "orig": "15", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "15"}, {"label": "caption", "id": 6, "page_no": 9, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 528.74, "b": 92.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.872844398021698, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 179.591, "r_y1": 80.72199999999998, "r_x2": 179.591, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The SQL ", "orig": "The SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.58, "r_y0": 80.40499999999997, "r_x1": 264.479, "r_y1": 80.40499999999997, "r_x2": 264.479, "r_y2": 71.957, "r_x3": 179.58, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION", "orig": "CREATE PERMISSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.54, "r_y0": 80.72199999999998, "r_x1": 528.74, "r_y1": 80.72199999999998, "r_x2": 528.74, "r_y2": 71.50900000000001, "r_x3": 264.54, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": " statement that is shown in Figure 3-1 is used to define and ", "orig": " statement that is shown in Figure 3-1 is used to define and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 341.703, "r_y1": 92.72199999999998, "r_x2": 341.703, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "initially enable or disable the row access rules.", "orig": "initially enable or disable the row access rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules."}, {"label": "caption", "id": 4, "page_no": 9, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 136.8, "t": 414.138, "r": 341.985, "b": 422.463, "coord_origin": "TOPLEFT"}, "confidence": 0.938783586025238, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 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"from_ocr": false}], "children": []}, "text": "Column mask"}, {"label": "text", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 455.089, "r": 542.776, "b": 500.301, "coord_origin": "TOPLEFT"}, "confidence": 0.9843975901603699, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 464.302, "r_x1": 542.776, "r_y1": 464.302, "r_x2": 542.776, "r_y2": 455.089, "r_x3": 136.8, "r_y3": 455.089, "coord_origin": "TOPLEFT"}, "text": "A column mask is a database object that manifests a column value access control rule for a ", "orig": "A column mask is a database object that manifests a column value access control rule for a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 476.302, "r_x1": 537.85, "r_y1": 476.302, "r_x2": 537.85, "r_y2": 467.089, "r_x3": 136.8, "r_y3": 467.089, "coord_origin": 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It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. 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For example, a teller can see only the last four digits of a tax identification number."}, {"label": "picture", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 135.97177124023438, "t": 107.41075134277344, "r": 545.4180908203125, "b": 410.60931396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9788318872451782, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 149.37099999999998, "r_x1": 251.916, "r_y1": 149.37099999999998, "r_x2": 251.916, "r_y2": 140.20799999999997, "r_x3": 148.134, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION ", "orig": "CREATE PERMISSION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.867, "r_y0": 149.36, "r_x1": 257.586, "r_y1": 149.36, "r_x2": 257.586, "r_y2": 140.20799999999997, "r_x3": 251.867, "r_y3": 140.20799999999997, 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"text", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 455.089, "r": 542.776, "b": 500.301, "coord_origin": "TOPLEFT"}, "confidence": 0.9843975901603699, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 464.302, "r_x1": 542.776, "r_y1": 464.302, "r_x2": 542.776, "r_y2": 455.089, "r_x3": 136.8, "r_y3": 455.089, "coord_origin": "TOPLEFT"}, "text": "A column mask is a database object that manifests a column value access control rule for a ", "orig": "A column mask is a database object that manifests a column value access control rule for a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 476.302, "r_x1": 537.85, "r_y1": 476.302, "r_x2": 537.85, "r_y2": 467.089, "r_x3": 136.8, "r_y3": 467.089, "coord_origin": "TOPLEFT"}, "text": "specific column in a specific table. It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. For example, a teller can see only the last four digits of a tax ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 500.301, "r_x1": 231.178, "r_y1": 500.301, "r_x2": 231.178, "r_y2": 491.088, "r_x3": 136.8, "r_y3": 491.088, "coord_origin": "TOPLEFT"}, "text": "identification number.", "orig": "identification number.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number."}, {"label": "picture", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 135.97177124023438, "t": 107.41075134277344, "r": 545.4180908203125, "b": 410.60931396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9788318872451782, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 149.37099999999998, "r_x1": 251.916, "r_y1": 149.37099999999998, "r_x2": 251.916, "r_y2": 140.20799999999997, "r_x3": 148.134, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION ", "orig": "CREATE PERMISSION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.867, "r_y0": 149.36, "r_x1": 257.586, "r_y1": 149.36, "r_x2": 257.586, "r_y2": 140.20799999999997, "r_x3": 251.867, "r_y3": 140.20799999999997, 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"r_x1": 530.748, "r_y1": 129.22400000000005, "r_x2": 530.748, "r_y2": 121.88599999999997, "r_x3": 346.565, "r_y3": 121.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Names the row permission for row access control", "orig": "Names the row permission for row access control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 190.75400000000002, "r_x1": 163.458, "r_y1": 190.75400000000002, "r_x2": 163.458, "r_y2": 181.591, "r_x3": 148.134, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "ON", "orig": "ON", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.584, "r_y0": 190.74300000000005, "r_x1": 174.303, "r_y1": 190.74300000000005, "r_x2": 174.303, "r_y2": 181.591, "r_x3": 168.584, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.309, "r_y0": 190.75400000000002, "r_x1": 226.875, "r_y1": 190.75400000000002, "r_x2": 226.875, "r_y2": 181.591, "r_x3": 174.309, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "table name", "orig": "table name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.865, "r_y0": 190.74300000000005, "r_x1": 232.584, "r_y1": 190.74300000000005, "r_x2": 232.584, "r_y2": 181.591, "r_x3": 226.865, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 311.32, "r_y0": 174.05600000000004, "r_x1": 529.937, "r_y1": 174.05600000000004, "r_x2": 529.937, "r_y2": 166.71799999999996, "r_x3": 311.32, "r_y3": 166.71799999999996, "coord_origin": "TOPLEFT"}, "text": "Identifies the table on which the row permission is created", "orig": "Identifies the table on which the row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 232.13699999999994, "r_x1": 163.113, "r_y1": 232.13699999999994, "r_x2": 163.113, "r_y2": 222.97400000000005, "r_x3": 148.134, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "AS ", "orig": "AS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.687, "r_y0": 232.12599999999998, "r_x1": 171.406, "r_y1": 232.12599999999998, "r_x2": 171.406, "r_y2": 222.97400000000005, "r_x3": 165.687, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": 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permission is created", "orig": "Indicates that a row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.597, "r_y0": 274.06500000000005, "r_x1": 530.955, "r_y1": 274.06500000000005, "r_x2": 530.955, "r_y2": 266.726, "r_x3": 321.597, "r_y3": 266.726, "coord_origin": "TOPLEFT"}, "text": "Specifies a condition that can be true, false, or unknown", "orig": "Specifies a condition that can be true, false, or unknown", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 301.109, "r_x1": 183.43, "r_y1": 301.109, "r_x2": 183.43, "r_y2": 291.945, "r_x3": 148.134, "r_y3": 291.945, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, 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"TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9071078896522522, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "15", "orig": "15", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "15"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.702, "r_x1": 538.503, "r_y1": 223.702, "r_x2": 538.503, "r_y2": 214.48900000000003, "r_x3": 136.8, "r_y3": 214.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.245, "r_y0": 197.88300000000004, "r_x1": 425.656, "r_y1": 197.88300000000004, "r_x2": 425.656, "r_y2": 189.558, "r_x3": 230.245, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "The authorization ID that initiated the connection.", "orig": "The authorization ID that initiated the connection.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.732, "r_y0": 395.376, "r_x1": 216.376, "r_y1": 395.376, "r_x2": 216.376, "r_y2": 387.442, "r_x3": 140.732, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": "Signed on as ALICE", "orig": "Signed on as ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.708, "r_y0": 395.376, "r_x1": 169.958, "r_y1": 395.376, "r_x2": 169.958, "r_y2": 387.442, "r_x3": 167.708, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.516, "r_y0": 395.376, "r_x1": 182.765, "r_y1": 395.376, "r_x2": 182.765, "r_y2": 387.442, "r_x3": 180.516, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.498, "r_y0": 395.376, "r_x1": 193.748, "r_y1": 395.376, "r_x2": 193.748, "r_y2": 387.442, "r_x3": 191.498, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 419.266, "r_x1": 191.711, "r_y1": 419.266, "r_x2": 191.711, "r_y2": 411.332, "r_x3": 138.476, "r_y3": 411.332, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 431.211, "r_x1": 232.566, "r_y1": 431.211, "r_x2": 232.566, "r_y2": 423.277, "r_x3": 138.476, "r_y3": 423.277, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = ALICE", "orig": "CURRENT USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 455.1, "r_x1": 183.266, "r_y1": 455.1, "r_x2": 183.266, "r_y2": 447.167, "r_x3": 138.476, "r_y3": 447.167, "coord_origin": "TOPLEFT"}, "text": "CALL proc1", "orig": "CALL proc1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 481.976, "r_x1": 174.059, "r_y1": 481.976, "r_x2": 174.059, "r_y2": 474.043, "r_x3": 148.43, "r_y3": 474.043, "coord_origin": "TOPLEFT"}, "text": "Proc1:", "orig": "Proc1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.522, "r_y0": 493.921, "r_x1": 209.1, "r_y1": 493.921, "r_x2": 209.1, "r_y2": 485.988, "r_x3": 157.522, "r_y3": 485.988, "coord_origin": "TOPLEFT"}, "text": "Owner = JOE", "orig": "Owner = JOE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.522, "r_y0": 505.866, "r_x1": 281.693, "r_y1": 505.866, "r_x2": 281.693, "r_y2": 497.933, "r_x3": 157.522, "r_y3": 497.933, "coord_origin": "TOPLEFT"}, "text": "SET OPTION USRPRF=*OWNER", "orig": "SET OPTION USRPRF=*OWNER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 529.756, "r_x1": 201.665, "r_y1": 529.756, "r_x2": 201.665, "r_y2": 521.823, "r_x3": 148.43, "r_y3": 521.823, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 541.701, "r_x1": 234.579, "r_y1": 541.701, "r_x2": 234.579, "r_y2": 533.768, "r_x3": 148.43, "r_y3": 533.768, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = JOE", "orig": "CURRENT USER = JOE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 574.55, "r_x1": 191.711, "r_y1": 574.55, "r_x2": 191.711, "r_y2": 566.616, "r_x3": 138.476, "r_y3": 566.616, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 586.495, "r_x1": 232.566, "r_y1": 586.495, "r_x2": 232.566, "r_y2": 578.561, "r_x3": 138.476, "r_y3": 578.561, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = ALICE", "orig": "CURRENT USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9130511283874512, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 412.174, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.7875156402587891, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49800000000005, "r": 372.618, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48900000000003, "r": 538.503, "b": 235.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.702, "r_x1": 538.503, "r_y1": 223.702, "r_x2": 538.503, "r_y2": 214.48900000000003, "r_x3": 136.8, "r_y3": 214.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52800000000002, "r": 411.326, "b": 252.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50800000000004, "r": 453.245, "b": 269.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48800000000006, "r": 541.459, "b": 298.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.527, "r": 547.584, "b": 339.74, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.801, "t": 347.507, "r": 547.483, "b": 368.719, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.718, "r": 341.274, "b": 605.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 625.5550000000001, "r": 247.016, "b": 637.543, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.7090000000001, "r": 518.01, "b": 672.922, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.728, "r": 532.347, "b": 718.941, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.245, "r_y0": 197.88300000000004, "r_x1": 425.656, "r_y1": 197.88300000000004, "r_x2": 425.656, "r_y2": 189.558, "r_x3": 230.245, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "The authorization ID that initiated the connection.", "orig": "The authorization ID that initiated the connection.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 39, "label": "text", "bbox": {"l": 142.8, "t": 110.53800000000001, "r": 209.69, "b": 118.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 40, "label": 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"coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 142.8, "t": 140.539, "r": 212.711, "b": 148.86400000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 230.22, "t": 129.49800000000005, "r": 468.01, "b": 137.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 142.8, "t": 159.558, "r": 216.656, "b": 167.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 45, "label": "text", "bbox": {"l": 230.198, "t": 159.558, "r": 536.173, "b": 167.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 46, "label": "text", "bbox": {"l": 230.221, "t": 170.538, "r": 447.381, "b": 178.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 142.801, "t": 189.558, "r": 209.743, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 48, "label": "text", "bbox": {"l": 230.245, "t": 189.558, "r": 425.656, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.245, "r_y0": 197.88300000000004, "r_x1": 425.656, "r_y1": 197.88300000000004, "r_x2": 425.656, "r_y2": 189.558, "r_x3": 230.245, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "The authorization ID that initiated the connection.", "orig": "The authorization ID that initiated the connection.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53800000000001, "r": 209.69, "b": 118.86300000000006, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.189, "t": 110.53800000000001, "r": 319.933, "b": 118.86300000000006, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 129.49800000000005, "r": 212.711, "b": 148.86400000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.22, "t": 129.49800000000005, "r": 468.01, "b": 137.82299999999998, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 159.558, "r": 216.656, "b": 167.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.198, "t": 159.558, "r": 536.173, "b": 178.86300000000006, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. 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Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9130511283874512, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "19"}, {"label": "caption", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 412.174, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.7875156402587891, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49800000000005, "r": 372.618, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48900000000003, "r": 538.503, "b": 235.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.702, "r_x1": 538.503, "r_y1": 223.702, "r_x2": 538.503, "r_y2": 214.48900000000003, "r_x3": 136.8, "r_y3": 214.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52800000000002, "r": 411.326, "b": 252.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50800000000004, "r": 453.245, "b": 269.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48800000000006, "r": 541.459, "b": 298.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.527, "r": 547.584, "b": 339.74, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.801, "t": 347.507, "r": 547.483, "b": 368.719, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.718, "r": 341.274, "b": 605.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 625.5550000000001, "r": 247.016, "b": 637.543, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.7090000000001, "r": 518.01, "b": 672.922, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.728, "r": 532.347, "b": 718.941, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 142.8, "t": 140.539, "r": 212.711, "b": 148.86400000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 230.22, "t": 129.49800000000005, "r": 468.01, "b": 137.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 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"label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 412.174, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.7875156402587891, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49800000000005, "r": 372.618, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48900000000003, "r": 538.503, "b": 235.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.702, "r_x1": 538.503, "r_y1": 223.702, "r_x2": 538.503, "r_y2": 214.48900000000003, "r_x3": 136.8, "r_y3": 214.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52800000000002, "r": 411.326, "b": 252.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50800000000004, "r": 453.245, "b": 269.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48800000000006, "r": 541.459, "b": 298.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.527, "r": 547.584, "b": 339.74, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.801, "t": 347.507, "r": 547.483, "b": 368.719, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.718, "r": 341.274, "b": 605.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 625.5550000000001, "r": 247.016, "b": 637.543, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.7090000000001, "r": 518.01, "b": 672.922, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.728, "r": 532.347, "b": 718.941, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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157.522, "r_y0": 493.921, "r_x1": 209.1, "r_y1": 493.921, "r_x2": 209.1, "r_y2": 485.988, "r_x3": 157.522, "r_y3": 485.988, "coord_origin": "TOPLEFT"}, "text": "Owner = JOE", "orig": "Owner = JOE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 50, "label": "text", "bbox": {"l": 157.522, "t": 497.933, "r": 281.693, "b": 505.866, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.522, "r_y0": 505.866, "r_x1": 281.693, "r_y1": 505.866, "r_x2": 281.693, "r_y2": 497.933, "r_x3": 157.522, "r_y3": 497.933, "coord_origin": "TOPLEFT"}, "text": "SET OPTION USRPRF=*OWNER", "orig": "SET OPTION USRPRF=*OWNER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 148.43, "t": 521.823, "r": 201.665, "b": 529.756, "coord_origin": "TOPLEFT"}, "confidence": 0.5350866913795471, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 529.756, "r_x1": 201.665, "r_y1": 529.756, "r_x2": 201.665, "r_y2": 521.823, "r_x3": 148.43, "r_y3": 521.823, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 148.43, "t": 533.768, "r": 234.579, "b": 541.701, "coord_origin": "TOPLEFT"}, "confidence": 0.5008324384689331, "cells": [{"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 541.701, "r_x1": 234.579, "r_y1": 541.701, "r_x2": 234.579, "r_y2": 533.768, "r_x3": 148.43, "r_y3": 533.768, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = JOE", "orig": "CURRENT USER = JOE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 138.476, "t": 566.616, "r": 232.566, "b": 586.495, "coord_origin": "TOPLEFT"}, "confidence": 0.5024971961975098, "cells": [{"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 574.55, "r_x1": 191.711, "r_y1": 574.55, "r_x2": 191.711, "r_y2": 566.616, "r_x3": 138.476, "r_y3": 566.616, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 586.495, "r_x1": 232.566, "r_y1": 586.495, "r_x2": 232.566, "r_y2": 578.561, "r_x3": 138.476, "r_y3": 578.561, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = ALICE", "orig": "CURRENT USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9130511283874512, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "19"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 118.86300000000006, "r_x1": 134.993, "r_y1": 118.86300000000006, "r_x2": 134.993, "r_y2": 110.53800000000001, "r_x3": 70.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Global variable", "orig": "Global variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, 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754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9187920093536377, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9505079388618469, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 342.519, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8370978832244873, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "caption", "bbox": {"l": 64.8, "t": 93.49800000000005, "r": 201.196, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 322.201, "r": 384.345, "b": 336.964, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.529, "r": 547.543, "b": 435.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 547.386, "b": 481.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, "r": 458.427, "b": 503.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 511.546, "r": 406.048, "b": 520.759, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 396.957, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 536.577, "b": 566.719, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.2, "t": 574.9929999999999, "r": 451.016, "b": 641.3530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 118.86300000000006, "r_x1": 134.993, "r_y1": 118.86300000000006, "r_x2": 134.993, "r_y2": 110.53800000000001, "r_x3": 70.8, "r_y3": 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"TOPLEFT"}, "text": "Host name of the current client as returned by the system", "orig": "Host name of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 156.84299999999996, "r_x1": 140.671, "r_y1": 156.84299999999996, "r_x2": 140.671, "r_y2": 148.51800000000003, "r_x3": 70.8, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "CLIENT_IPADDR", "orig": "CLIENT_IPADDR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.872, "r_y0": 156.84299999999996, "r_x1": 267.083, "r_y1": 156.84299999999996, "r_x2": 267.083, "r_y2": 148.51800000000003, "r_x3": 202.872, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(128)", "orig": "VARCHAR(128)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.845, "r_y0": 156.84299999999996, "r_x1": 509.611, "r_y1": 156.84299999999996, "r_x2": 509.611, "r_y2": 148.51800000000003, "r_x3": 281.845, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "IP address of the current client as returned by the system", "orig": "IP address of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 175.86300000000006, "r_x1": 134.965, "r_y1": 175.86300000000006, "r_x2": 134.965, "r_y2": 167.538, "r_x3": 70.8, "r_y3": 167.538, "coord_origin": "TOPLEFT"}, "text": "CLIENT_PORT ", "orig": "CLIENT_PORT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.903, "r_y0": 175.86300000000006, "r_x1": 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"coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 547.386, "b": 481.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, "r": 458.427, "b": 503.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 511.546, "r": 406.048, "b": 520.759, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 396.957, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 536.577, "b": 566.719, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.2, "t": 574.9929999999999, "r": 451.016, "b": 641.3530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 118.86300000000006, "r_x1": 134.993, "r_y1": 118.86300000000006, "r_x2": 134.993, "r_y2": 110.53800000000001, "r_x3": 70.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Global variable", "orig": "Global variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.889, "r_y0": 118.86300000000006, "r_x1": 223.349, "r_y1": 118.86300000000006, "r_x2": 223.349, "r_y2": 110.53800000000001, "r_x3": 202.889, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Type", "orig": "Type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.825, "r_y0": 118.86300000000006, "r_x1": 331.345, "r_y1": 118.86300000000006, "r_x2": 331.345, "r_y2": 110.53800000000001, "r_x3": 281.825, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Description", "orig": "Description", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 137.82299999999998, "r_x1": 132.729, "r_y1": 137.82299999999998, "r_x2": 132.729, "r_y2": 129.49800000000005, "r_x3": 70.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "CLIENT_HOST", "orig": "CLIENT_HOST", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.89, "r_y0": 137.82299999999998, "r_x1": 267.085, "r_y1": 137.82299999999998, "r_x2": 267.085, "r_y2": 129.49800000000005, "r_x3": 202.89, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(255)", "orig": "VARCHAR(255)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.847, "r_y0": 137.82299999999998, "r_x1": 510.176, "r_y1": 137.82299999999998, "r_x2": 510.176, "r_y2": 129.49800000000005, "r_x3": 281.847, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Host name of the current client as returned by the system", "orig": "Host name of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 156.84299999999996, "r_x1": 140.671, "r_y1": 156.84299999999996, "r_x2": 140.671, "r_y2": 148.51800000000003, "r_x3": 70.8, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "CLIENT_IPADDR", "orig": "CLIENT_IPADDR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.872, "r_y0": 156.84299999999996, "r_x1": 267.083, "r_y1": 156.84299999999996, "r_x2": 267.083, "r_y2": 148.51800000000003, "r_x3": 202.872, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(128)", "orig": "VARCHAR(128)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.845, "r_y0": 156.84299999999996, "r_x1": 509.611, "r_y1": 156.84299999999996, "r_x2": 509.611, "r_y2": 148.51800000000003, "r_x3": 281.845, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "IP address of the current client as returned by the system", "orig": "IP address of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 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10, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of the currently running routine", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 342.519, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8370978832244873, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.8, "t": 93.49800000000005, "r": 201.196, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 322.201, "r": 384.345, "b": 336.964, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.529, "r": 547.543, "b": 435.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 547.386, "b": 481.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, "r": 458.427, "b": 503.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 511.546, "r": 406.048, "b": 520.759, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 396.957, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 536.577, "b": 566.719, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.2, "t": 574.9929999999999, "r": 451.016, "b": 641.3530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 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Row and Column Access Control ", "orig": "Chapter 3. 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DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . 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DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 564.375, "r_x1": 141.78, "r_y1": 564.375, "r_x2": 141.78, "r_y2": 555.975, "r_x3": 136.8, "r_y3": 555.975, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 521.574, "b": 260.355, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.35699999999997, "r_x1": 311.697, "r_y1": 116.35699999999997, "r_x2": 311.697, "r_y2": 107.957, "r_x3": 136.8, "r_y3": 107.957, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 164.356, "r_x1": 311.697, "r_y1": 164.356, "r_x2": 311.697, "r_y2": 155.95600000000002, "r_x3": 136.8, "r_y3": 155.95600000000002, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 547.281, "b": 296.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.039, "t": 304.485, "r": 469.118, "b": 313.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.039, "t": 321.525, "r": 403.932, "b": 330.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 545.118, "b": 359.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 529.442, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 526.555, "b": 684.373, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "27"}, {"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 521.574, "b": 260.355, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.35699999999997, "r_x1": 311.697, "r_y1": 116.35699999999997, "r_x2": 311.697, "r_y2": 107.957, "r_x3": 136.8, "r_y3": 107.957, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 164.356, "r_x1": 311.697, "r_y1": 164.356, "r_x2": 311.697, "r_y2": 155.95600000000002, "r_x3": 136.8, "r_y3": 155.95600000000002, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 547.281, "b": 296.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.039, "t": 304.485, "r": 469.118, "b": 313.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.039, "t": 321.525, "r": 403.932, "b": 330.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 545.118, "b": 359.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 529.442, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 526.555, "b": 684.373, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 521.574, "b": 260.355, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.35699999999997, "r_x1": 311.697, "r_y1": 116.35699999999997, "r_x2": 311.697, "r_y2": 107.957, "r_x3": 136.8, "r_y3": 107.957, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 164.356, "r_x1": 311.697, "r_y1": 164.356, "r_x2": 311.697, "r_y2": 155.95600000000002, "r_x3": 136.8, "r_y3": 155.95600000000002, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 547.281, "b": 296.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.039, "t": 304.485, "r": 469.118, "b": 313.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.039, "t": 321.525, "r": 403.932, "b": 330.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 545.118, "b": 359.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 529.442, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 526.555, "b": 684.373, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "27"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.699, "r_y0": 401.72, "r_x1": 446.468, "r_y1": 401.72, "r_x2": 446.468, "r_y2": 392.507, "r_x3": 443.699, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 522.418, "r_y0": 401.72, "r_x1": 525.187, "r_y1": 401.72, "r_x2": 525.187, "r_y2": 392.507, "r_x3": 522.418, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 449.917, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.8, "t": 173.538, "r": 293.146, "b": 181.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.8, "t": 202.375, "r": 203.983, "b": 214.36300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.529, "r": 547.209, "b": 261.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 409.443, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.957, "r": 376.677, "b": 329.357, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 136.8, "t": 332.957, "r": 291.718, "b": 341.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 136.8, "t": 344.956, "r": 271.678, "b": 353.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.956, "r": 291.718, "b": 365.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.508, "r": 540.81, "b": 413.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.8, "t": 649.038, "r": 347.441, "b": 657.363, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 449.917, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.8, "t": 173.538, "r": 293.146, "b": 181.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.8, "t": 202.375, "r": 203.983, "b": 214.36300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.529, "r": 547.209, "b": 261.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 409.443, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.957, "r": 376.677, "b": 329.357, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Column Access Control (masks) */"}, {"label": "text", "id": 31, "page_no": 13, "cluster": {"id": 31, "label": "text", "bbox": {"l": 136.8, "t": 332.957, "r": 291.718, "b": 341.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 32, "page_no": 13, "cluster": {"id": 32, "label": "text", "bbox": {"l": 136.8, "t": 344.956, "r": 271.678, "b": 353.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.956, "r": 291.718, "b": 365.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.508, "r": 540.81, "b": 413.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.8, "t": 649.038, "r": 347.441, "b": 657.363, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 449.917, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.8, "t": 173.538, "r": 293.146, "b": 181.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.8, "t": 202.375, "r": 203.983, "b": 214.36300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.529, "r": 547.209, "b": 261.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 409.443, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.957, "r": 376.677, "b": 329.357, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Column Access Control (masks) */"}, {"label": "text", "id": 31, "page_no": 13, "cluster": {"id": 31, "label": "text", "bbox": {"l": 136.8, "t": 332.957, "r": 291.718, "b": 341.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 32, "page_no": 13, "cluster": {"id": 32, "label": "text", "bbox": {"l": 136.8, "t": 344.956, "r": 271.678, "b": 353.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.956, "r": 291.718, "b": 365.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.508, "r": 540.81, "b": 413.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.8, "t": 649.038, "r": 347.441, "b": 657.363, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. The index being advised is for the ORDER BY clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 675.843, "r_x1": 227.119, "r_y1": 675.843, "r_x2": 227.119, "r_y2": 667.518, "r_x3": 64.8, "r_y3": 667.518, "coord_origin": "TOPLEFT"}, "text": "Figure 4-69 Index advice with no RCAC", "orig": "Figure 4-69 Index advice with no RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538, "r": 523.576, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.95576012134552, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 514.058, "b": 104.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.558, "r": 327.111, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.569, "r": 547.308, "b": 539.781, "coord_origin": "TOPLEFT"}, "confidence": 0.9766736626625061, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. The index being advised is for the ORDER BY clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 64.8, "t": 667.518, "r": 227.119, "b": 675.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9514285922050476, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 675.843, "r_x1": 227.119, "r_y1": 675.843, "r_x2": 227.119, "r_y2": 667.518, "r_x3": 64.8, "r_y3": 667.518, "coord_origin": "TOPLEFT"}, "text": "Figure 4-69 Index advice with no RCAC", "orig": "Figure 4-69 Index advice with no RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.2491226196289, "r": 545.4508666992188, "b": 477.5412292480469, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797719120979309, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 4, "page_no": 14, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538, "r": 523.576, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.95576012134552, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "77"}, {"label": "list_item", "id": 3, "page_no": 14, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 514.058, "b": 104.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause."}, {"label": "caption", "id": 5, "page_no": 14, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.558, "r": 327.111, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"label": "list_item", "id": 2, "page_no": 14, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.569, "r": 547.308, "b": 539.781, "coord_origin": "TOPLEFT"}, "confidence": 0.9766736626625061, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. The index being advised is for the ORDER BY clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause."}, {"label": "caption", "id": 6, "page_no": 14, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 64.8, "t": 667.518, "r": 227.119, "b": 675.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9514285922050476, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 675.843, "r_x1": 227.119, "r_y1": 675.843, "r_x2": 227.119, "r_y2": 667.518, "r_x3": 64.8, "r_y3": 667.518, "coord_origin": "TOPLEFT"}, "text": "Figure 4-69 Index advice with no RCAC", "orig": "Figure 4-69 Index advice with no RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4-69 Index advice with no RCAC"}, {"label": "picture", "id": 0, "page_no": 14, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.2491226196289, "r": 545.4508666992188, "b": 477.5412292480469, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 14, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797719120979309, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "list_item", "id": 3, "page_no": 14, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 514.058, "b": 104.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause."}, {"label": "caption", "id": 5, "page_no": 14, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.558, "r": 327.111, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"label": "list_item", "id": 2, "page_no": 14, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.569, "r": 547.308, "b": 539.781, "coord_origin": "TOPLEFT"}, "confidence": 0.9766736626625061, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. The index being advised is for the ORDER BY clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause."}, {"label": "caption", "id": 6, "page_no": 14, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 64.8, "t": 667.518, "r": 227.119, "b": 675.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9514285922050476, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 675.843, "r_x1": 227.119, "r_y1": 675.843, "r_x2": 227.119, "r_y2": 667.518, "r_x3": 64.8, "r_y3": 667.518, "coord_origin": "TOPLEFT"}, "text": "Figure 4-69 Index advice with no RCAC", "orig": "Figure 4-69 Index advice with no RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4-69 Index advice with no RCAC"}, {"label": "picture", "id": 0, "page_no": 14, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.2491226196289, "r": 545.4508666992188, "b": 477.5412292480469, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 14, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797719120979309, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 4, "page_no": 14, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538, "r": 523.576, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.95576012134552, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "77"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 83.982, "r_y1": 763.863, "r_x2": 83.982, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "124 ", "orig": "124 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.94, "r_y0": 763.863, "r_x1": 339.654, "r_y1": 763.863, "r_x2": 339.654, "r_y2": 755.538, "r_x3": 98.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 519.518, "r_x1": 123.24, "r_y1": 519.518, "r_x2": 123.24, "r_y2": 511.927, "r_x3": 64.8, "r_y3": 511.927, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 530.558, "r_x1": 96.24, "r_y1": 530.558, "r_x2": 96.24, "r_y2": 522.967, "r_x3": 64.8, "r_y3": 522.967, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 541.538, "r_x1": 124.14, "r_y1": 541.538, "r_x2": 124.14, "r_y2": 533.947, "r_x3": 79.2, "r_y3": 533.947, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 563.558, "r_x1": 500.697, "r_y1": 563.558, "r_x2": 500.697, "r_y2": 555.968, "r_x3": 64.8, "r_y3": 555.968, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 574.538, "r_x1": 281.399, "r_y1": 574.538, "r_x2": 281.399, "r_y2": 566.948, "r_x3": 79.2, "r_y3": 566.948, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 585.518, "r_x1": 137.64, "r_y1": 585.518, "r_x2": 137.64, "r_y2": 577.928, "r_x3": 79.2, "r_y3": 577.928, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 596.558, "r_x1": 361.378, "r_y1": 596.558, "r_x2": 361.378, "r_y2": 588.968, "r_x3": 64.8, "r_y3": 588.968, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 629.559, "r_x1": 258.059, "r_y1": 629.559, "r_x2": 258.059, "r_y2": 621.968, "r_x3": 64.8, "r_y3": 621.968, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 83.982, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8994001150131226, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 83.982, "r_y1": 763.863, "r_x2": 83.982, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "124 ", "orig": "124 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.94, "t": 755.538, "r": 339.654, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.94, "r_y0": 763.863, "r_x1": 339.654, "r_y1": 763.863, "r_x2": 339.654, "r_y2": 755.538, "r_x3": 98.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "code", "bbox": {"l": 64.8, "t": 71.94299999999998, "r": 500.697, "b": 706.539, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 519.518, "r_x1": 123.24, "r_y1": 519.518, "r_x2": 123.24, "r_y2": 511.927, "r_x3": 64.8, "r_y3": 511.927, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 530.558, "r_x1": 96.24, "r_y1": 530.558, "r_x2": 96.24, "r_y2": 522.967, "r_x3": 64.8, "r_y3": 522.967, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 541.538, "r_x1": 124.14, "r_y1": 541.538, "r_x2": 124.14, "r_y2": 533.947, "r_x3": 79.2, "r_y3": 533.947, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 563.558, "r_x1": 500.697, "r_y1": 563.558, "r_x2": 500.697, "r_y2": 555.968, "r_x3": 64.8, "r_y3": 555.968, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 574.538, "r_x1": 281.399, "r_y1": 574.538, "r_x2": 281.399, "r_y2": 566.948, "r_x3": 79.2, "r_y3": 566.948, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 585.518, "r_x1": 137.64, "r_y1": 585.518, "r_x2": 137.64, "r_y2": 577.928, "r_x3": 79.2, "r_y3": 577.928, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 596.558, "r_x1": 361.378, "r_y1": 596.558, "r_x2": 361.378, "r_y2": 588.968, "r_x3": 64.8, "r_y3": 588.968, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 629.559, "r_x1": 258.059, "r_y1": 629.559, "r_x2": 258.059, "r_y2": 621.968, "r_x3": 64.8, "r_y3": 621.968, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 83.982, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8994001150131226, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 83.982, "r_y1": 763.863, "r_x2": 83.982, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "124 ", "orig": "124 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.94, "t": 755.538, "r": 339.654, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.94, "r_y0": 763.863, "r_x1": 339.654, "r_y1": 763.863, "r_x2": 339.654, "r_y2": 755.538, "r_x3": 98.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.8, "t": 71.94299999999998, "r": 500.697, "b": 706.539, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 519.518, "r_x1": 123.24, "r_y1": 519.518, "r_x2": 123.24, "r_y2": 511.927, "r_x3": 64.8, "r_y3": 511.927, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 530.558, "r_x1": 96.24, "r_y1": 530.558, "r_x2": 96.24, "r_y2": 522.967, "r_x3": 64.8, "r_y3": 522.967, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 541.538, "r_x1": 124.14, "r_y1": 541.538, "r_x2": 124.14, "r_y2": 533.947, "r_x3": 79.2, "r_y3": 533.947, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 563.558, "r_x1": 500.697, "r_y1": 563.558, "r_x2": 500.697, "r_y2": 555.968, "r_x3": 64.8, "r_y3": 555.968, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 574.538, "r_x1": 281.399, "r_y1": 574.538, "r_x2": 281.399, "r_y2": 566.948, "r_x3": 79.2, "r_y3": 566.948, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 585.518, "r_x1": 137.64, "r_y1": 585.518, "r_x2": 137.64, "r_y2": 577.928, "r_x3": 79.2, "r_y3": 577.928, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 596.558, "r_x1": 361.378, "r_y1": 596.558, "r_x2": 361.378, "r_y2": 588.968, "r_x3": 64.8, "r_y3": 588.968, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 629.559, "r_x1": 258.059, "r_y1": 629.559, "r_x2": 258.059, "r_y2": 621.968, "r_x3": 64.8, "r_y3": 621.968, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.8, "t": 71.94299999999998, "r": 500.697, "b": 706.539, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 519.518, "r_x1": 123.24, "r_y1": 519.518, "r_x2": 123.24, "r_y2": 511.927, "r_x3": 64.8, "r_y3": 511.927, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 530.558, "r_x1": 96.24, "r_y1": 530.558, "r_x2": 96.24, "r_y2": 522.967, "r_x3": 64.8, "r_y3": 522.967, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 541.538, "r_x1": 124.14, "r_y1": 541.538, "r_x2": 124.14, "r_y2": 533.947, "r_x3": 79.2, "r_y3": 533.947, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 563.558, "r_x1": 500.697, "r_y1": 563.558, "r_x2": 500.697, "r_y2": 555.968, "r_x3": 64.8, "r_y3": 555.968, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 574.538, "r_x1": 281.399, "r_y1": 574.538, "r_x2": 281.399, "r_y2": 566.948, "r_x3": 79.2, "r_y3": 566.948, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 585.518, "r_x1": 137.64, "r_y1": 585.518, "r_x2": 137.64, "r_y2": 577.928, "r_x3": 79.2, "r_y3": 577.928, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 596.558, "r_x1": 361.378, "r_y1": 596.558, "r_x2": 361.378, "r_y2": 588.968, "r_x3": 64.8, "r_y3": 588.968, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 629.559, "r_x1": 258.059, "r_y1": 629.559, "r_x2": 258.059, "r_y2": 621.968, "r_x3": 64.8, "r_y3": 621.968, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . 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This ", "orig": "access to data in a comprehensive and transparent way. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 299.592, "r_x1": 414.421, "r_y1": 299.592, "r_x2": 414.421, "r_y2": 290.728, "r_x3": 152.94, "r_y3": 290.728, "coord_origin": "TOPLEFT"}, "text": "publication helps you understand the capabilities of RCAC and provides ", "orig": "publication helps you understand the capabilities of RCAC and provides ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 311.592, "r_x1": 414.225, "r_y1": 311.592, "r_x2": 414.225, "r_y2": 302.728, "r_x3": 152.94, "r_y3": 302.728, "coord_origin": "TOPLEFT"}, "text": "examples of defining, creating, and implementing the row permissions ", "orig": "examples of defining, creating, and implementing the row permissions ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 323.592, "r_x1": 362.316, "r_y1": 323.592, "r_x2": 362.316, "r_y2": 314.727, "r_x3": 152.94, "r_y3": 314.727, "coord_origin": "TOPLEFT"}, "text": "and column masks in a relational database environment.", "orig": "and column masks in a relational database environment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 340.572, "r_x1": 414.464, "r_y1": 340.572, "r_x2": 414.464, "r_y2": 331.707, "r_x3": 152.94, "r_y3": 331.707, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application ", "orig": "This paper is intended for database engineers, data-centric application ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 352.571, "r_x1": 407.6, "r_y1": 352.571, "r_x2": 407.6, "r_y2": 343.707, "r_x3": 152.94, "r_y3": 343.707, "coord_origin": "TOPLEFT"}, "text": "developers, and security officers who want to design and implement ", "orig": "developers, and security officers who want to design and implement ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 364.571, "r_x1": 399.286, "r_y1": 364.571, "r_x2": 399.286, "r_y2": 355.707, "r_x3": 152.94, "r_y3": 355.707, "coord_origin": "TOPLEFT"}, "text": "RCAC as a part of their data control and governance policy. A solid ", "orig": "RCAC as a part of their data control and governance policy. 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A solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 376.571, "r_x1": 414.05, "r_y1": 376.571, "r_x2": 414.05, "r_y2": 367.707, "r_x3": 152.94, "r_y3": 367.707, "coord_origin": "TOPLEFT"}, "text": "background in IBM i object level security, DB2 for i relational database ", "orig": "background in IBM i object level security, DB2 for i relational database ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 388.571, "r_x1": 268.856, "r_y1": 388.571, "r_x2": 268.856, "r_y2": 379.706, "r_x3": 152.94, "r_y3": 379.706, "coord_origin": "TOPLEFT"}, "text": "concepts, and SQL is assumed.", "orig": "concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. 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أن تؤدي البرمجة بلغة +تحسين الإنتاجية وحل المشكلات من خلال البرمجة بلغة R و Python +تعتبر البرمجة بلغة R و Python من الأدوات القوية التي يمكن أن تعزز الإنتاجية وتساعد في إيجاد حلول فعالة للمشكلات. يمتلك كل من R و Python ميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل على المحللين والعلماء إجراء تحليلات معقدة بطريقة سريعة وفعالة. إذا كان لديك عقلية تحليلية، فإن استخدام هذه اللغات يمكن أن يسهم بشكل كبير في تحسين نتائج العمل . +عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج الأنماط والتوجهات منها. يمكن للمبرمجين استخدام R و Python لتنفيذ عمليات تحليلية متقدمة، مثل النمذجة الإحصائية وتحليل البيانات الكبيرة. هذا ليس فقط يوفر الوقت، بل يمكن أن يؤدي أيضًا إلى اتخاذ قرارات أكثر دقة بناء ً على استنتاجات قائمة على البيانات . +علاوة على ذلك، توفر كل من R و Python مكتبات وأدوات غنية تدعم مجموعة واسعة م ن التطبيقات، من التحليل البياني إلى التعلم الآلي. يمكن للمستخدمين الاستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكلات المختلفة. على سبيل المثال، يمكن استخدام مكتبة pandas في Python لإدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرسم البياني والتحليل الإ حصائي، مما يجعلها مثالية للباحثين والمحللين . +في النهاية، يمكن أن تؤدي البرمجة بلغة R و Python مع عقلية تحليلية إلى تحسين الإنتاجية وتوفير حلول مبتكرة للمشكلات المعقدة. إن القدرة على تحليل البيانات بشكل فعال وتطبيق الأساليب البرمجية المناسبة يمكن أن تكون له ا تأثيرات إيجابية بعيدة المدى على الأداء الشخصي والمهني . \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/right_to_left_01.json b/tests/data/groundtruth/docling_v1/right_to_left_01.json index dc07ce6d..0b39b570 100644 --- a/tests/data/groundtruth/docling_v1/right_to_left_01.json +++ b/tests/data/groundtruth/docling_v1/right_to_left_01.json @@ -1 +1 @@ -{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": 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"width": 612.0}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/right_to_left_01.md b/tests/data/groundtruth/docling_v1/right_to_left_01.md index f3717bd4..36b32048 100644 --- a/tests/data/groundtruth/docling_v1/right_to_left_01.md +++ b/tests/data/groundtruth/docling_v1/right_to_left_01.md @@ -1,9 +1,9 @@ -## تحسين الإنتاجية وحل المشكلات من خلال البرمجة بلغة +## تحسين الإنتاجية وحل المشكلات من خلال البرمجة بلغة R و Python -تعتبر البرمجة بلغة +تعتبر البرمجة بلغة R و Python من الأدوات القوية التي يمكن أن تعزز الإنتاجية وتساعد في إيجاد حلول فعالة للمشكلات. يمتلك كل من R و Python ميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل على المحللين والعلماء إجراء تحليلات معقدة بطريقة سريعة وفعالة. إذا كان لديك عقلية تحليلية، فإن استخدام هذه اللغات يمكن أن يسهم بشكل كبير في تحسين نتائج العمل . -عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج +عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج الأنماط والتوجهات منها. يمكن للمبرمجين استخدام R و Python لتنفيذ عمليات تحليلية متقدمة، مثل النمذجة الإحصائية وتحليل البيانات الكبيرة. هذا ليس فقط يوفر الوقت، بل يمكن أن يؤدي أيضًا إلى اتخاذ قرارات أكثر دقة بناء ً على استنتاجات قائمة على البيانات . -علاوة على ذلك، توفر كل من +علاوة على ذلك، توفر كل من R و Python مكتبات وأدوات غنية تدعم مجموعة واسعة م ن التطبيقات، من التحليل البياني إلى التعلم الآلي. يمكن للمستخدمين الاستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكلات المختلفة. على سبيل المثال، يمكن استخدام مكتبة pandas في Python لإدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرسم البياني والتحليل الإ حصائي، مما يجعلها مثالية للباحثين والمحللين . -في النهاية، يمكن أن تؤدي البرمجة بلغة \ No newline at end of file +في النهاية، يمكن أن تؤدي البرمجة بلغة R و Python مع عقلية تحليلية إلى تحسين الإنتاجية وتوفير حلول مبتكرة للمشكلات المعقدة. إن القدرة على تحليل البيانات بشكل فعال 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الحكوموة المصورية لوال ال لور -مايــــــــة اممــــــــن -المصــــــــــــــــــــر -تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ +11 +وعليه، فإن الحكومة المصرية تضع صوو عييهاوخ لوال المر اوة الم باوة تكايف السيد رئيس الجماورية لاخ بخلعمل ياى تح يق يدد من الأ هودا ياى رعساخ : وضع ماف بهخء الإنسخن المصري ياى رعس قخئموة الأولويو خت، لخصووة فووا مجووخحت الصووحة واللعاوويل، العموول ياووى تح يووق معوودحت نمووو قويوووة ومسووولدامة و وووخماة فوووا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددات الأمون ال ووما المصوري فوا ضووء اللحوديخت الإقايميوة والدوليو ة، ومواصوواة واووود تلوووير الماووخر ة السيخسووية، واسوولمرار ملخبعووة ما و و خت الأمووون واحسووول رار ومكخفحوووة الإرهوووخ ، تلووووير ما وووخت ال خفوووة والوووويا الوووو،ها، والبلوووخ الوووديها المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل الموا،هة والسام المجلمعا . +ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور ( 2024 -2026 ) تح يق عربعة عهدا اسلراتيجية رئيسة، وها ياى الهحو الآتا : +مايــــــــة اممــــــــن القومي المصـر بنــــاء ا نســــا المصــــــــــــــــــــر بنـــــاء ا تصـــــاع تنابســــــــــــــــــــــي تحقيق اظستق رار السياســــــــــــــــــــــــي +تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ د باوكل رئووويس ياوووى مسووولادفخت ر يوووة مصووور 2023 ، وتوصووويخت واسوووخت الحووووار الوو،ها، ومسولادفخت الوو ارات، والبرنوخما الوو،ها ليصوا خت الايك ايوة، ومبلاف احسلراتيجيخت الو،هية .
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+1,11 @@ -وعليه، +11 -ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور +وعليه، فإن الحكومة المصرية تضع صوو عييهاوخ لوال المر اوة الم باوة تكايف السيد رئيس الجماورية لاخ بخلعمل ياى تح يق يدد من الأ هودا ياى رعساخ : وضع ماف بهخء الإنسخن المصري ياى رعس قخئموة الأولويو خت، لخصووة فووا مجووخحت الصووحة واللعاوويل، العموول ياووى تح يووق معوودحت نمووو قويوووة ومسووولدامة و وووخماة فوووا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددات الأمون ال ووما المصوري فوا ضووء اللحوديخت الإقايميوة والدوليو ة، ومواصوواة واووود تلوووير الماووخر ة السيخسووية، واسوولمرار ملخبعووة ما و و خت الأمووون واحسووول رار ومكخفحوووة الإرهوووخ ، تلووووير ما وووخت ال خفوووة والوووويا الوووو،ها، والبلوووخ الوووديها المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل الموا،هة والسام المجلمعا . -مايــــــــة اممــــــــن +ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور ( 2024 -2026 ) تح يق عربعة عهدا اسلراتيجية رئيسة، وها ياى الهحو الآتا : -المصــــــــــــــــــــر +مايــــــــة اممــــــــن القومي المصـر بنــــاء ا نســــا المصــــــــــــــــــــر بنـــــاء ا تصـــــاع تنابســــــــــــــــــــــي تحقيق اظستق رار السياســــــــــــــــــــــــي -تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ +تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ د باوكل رئووويس ياوووى مسووولادفخت ر يوووة مصووور 2023 ، وتوصووويخت واسوووخت الحووووار الوو،ها، ومسولادفخت الوو ارات، والبرنوخما الوو،ها ليصوا خت الايك ايوة، ومبلاف احسلراتيجيخت الو،هية . \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/right_to_left_02.pages.json b/tests/data/groundtruth/docling_v1/right_to_left_02.pages.json index 93b7e9c7..d64dd1c9 100644 --- a/tests/data/groundtruth/docling_v1/right_to_left_02.pages.json +++ b/tests/data/groundtruth/docling_v1/right_to_left_02.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 595.2, "height": 841.92}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 47.952, "r_y0": 818.132, "r_x1": 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1f0aec64..79fad5bf 100644 --- a/tests/data/groundtruth/docling_v1/right_to_left_03.doctags.txt +++ b/tests/data/groundtruth/docling_v1/right_to_left_03.doctags.txt @@ -1,32 +1,32 @@ -اميد +اميد نامه پذيرش در بازار اصلی -کالا ی داخلی
-2-5 -شمشه و شمشال توليد شده به روش ريخته گری +2-5 -استاندارد ک الا نام استاندارد -20300 +شمشه و شمشال توليد شده به روش ريخته گری پيوسته مورد مصرف در فولادهای سازه ای - مطابق آناليز پيوست شماره استاندارد ملی +20300 استاندارد اجباری است؟ -سازمان ملی استاندارد ايران مرجع صادرکننده استاندارد -آيا توليدکننده محصول، استاندارد مذکور را اخذ -3 -19 +سازمان ملی استاندارد ايران +آيا توليدکننده محصول، استاندارد مذکور را اخذ نموده است؟ +3 -پذيرش در بورس تاريخ ارائه مدارک -04 +19 / 09 / 1403 تاريخ پذيرش +04 / 10 / 1403 +شماره جلسه کميته عرضه 436 -شماره جلسه -05 تاريخ درج اميدنامه -کارگزاری +05 / 10 / 1403 مشاور پذيرش -بر -نحوة تعيين قيمت پايه پس از پذيرش کالا در -حداقل -حداقل درصد عرضه از توليد -5% +کارگزاری آ رمون بورس +نحوة تعيين قيمت پايه پس از پذيرش کالا در بورس +بر اساس قيمت های جهانی +حداقل درصد عرضه از توليد / کل فروش / فروش داخلی +حداقل 50 % از توليد ساليانه يا 47.500 تن خطای مجاز تحويل +5% آخرين محموله قابل تحويل \ No newline at end of file diff --git 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"_s3_data": null, "identifiers": null} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v1/right_to_left_03.md b/tests/data/groundtruth/docling_v1/right_to_left_03.md index 3382aaa2..66250ebd 100644 --- a/tests/data/groundtruth/docling_v1/right_to_left_03.md +++ b/tests/data/groundtruth/docling_v1/right_to_left_03.md @@ -1,55 +1,55 @@ -## اميد +## اميد نامه پذيرش در بازار اصلی -کالا ی داخلی -## 2-5 - -شمشه و شمشال توليد شده به روش ريخته گری +## 2-5 -استاندارد ک الا نام استاندارد -20300 +شمشه و شمشال توليد شده به روش ريخته گری پيوسته مورد مصرف در فولادهای سازه ای - مطابق آناليز پيوست شماره استاندارد ملی -استاندارد اجباری است؟ +20300 -سازمان ملی استاندارد ايران +استاندارد اجباری است؟ مرجع صادرکننده استاندارد -آيا توليدکننده محصول، استاندارد مذکور را اخذ +سازمان ملی استاندارد ايران -## 3 +آيا توليدکننده محصول، استاندارد مذکور را اخذ نموده است؟ -19 +## 3 -پذيرش در بورس تاريخ ارائه مدارک -04 +19 / 09 / 1403 تاريخ پذيرش +04 / 10 / 1403 + +شماره جلسه کميته عرضه + 436 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"left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Page 7"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt b/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt index 0146026a..237d0349 100644 --- a/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt +++ b/tests/data/groundtruth/docling_v2/2203.01017v2.doctags.txt @@ -1,111 +1,110 @@ arXiv:2203.01017v2 [cs.CV] 11 Mar 2022 TableFormer: Table Structure Understanding with Transformers. -Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar -{ +Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research +{ ahn,nli,mly,taa @zurich.ibm.com } Abstract -Tables organize valuable content in a concise and com- -1. Introduction -The occurrence of tables a. Picture of a table: +Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. +1. Introduction +The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues. - -b. Red-annotation of bounding boxes, +1 +b. Red-annotation of bounding boxes, Blue-predictions by TableFormer c. Structure predicted by TableFormer: -Figure 1: - -Recently, -The first problem is called table-location and has been +Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. +01 2 134 35678 291011121314151617181920 +Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. +The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be 1 -considered as a solved problem, given enough ground-truth -The second problem is called table-structure decompo- -In this paper, we want to address these weaknesses and -To meet the design criteria listed above, we developed a -• -• -• -• +considered as a solved problem, given enough ground-truth data to train on. +The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. +In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image. +To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows: +· We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. +· Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works. +· We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity. +· An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility. -The paper is structured as follows. -1 -its results & performance in Sec. 5. As a conclusion, we de- +The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe +1 https://github.com/IBM/SynthTabNet +its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. 2. Previous work and State of the Art -Identifying the structure of a table has been an outstand- -Before the rising -Image-to-Text networks +Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc. +Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification. +Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder → text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder → dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the 2 tag-decoder which is constrained to the table-tags. -In -Graph -Hybrid Deep Learning-Rule-Based approach +In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper. +Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]. +Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. 3. Datasets -We rely on large-scale datasets such as PubTabNet [37], -Figure 2: +We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im- +Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets balance in the previous datasets. -The PubTabNet dataset contains 509k tables delivered as -Due to the heterogeneity across the dataset formats, it +The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits. +Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small 3 -amount of such tables, and kept only those ones ranging -The availability of the bounding boxes for all table cells -As it is illustrated in Fig. 2, the table distributions from -Motivated by those observations we aimed at generating -In this regard, we have prepared four synthetic datasets, -Table -one adopts a colorful appearance with high contrast and the +amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns). +The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. +As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. +Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. +In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third +TagsBboxSizeFormatPubTabNet33509kPNGFinTabNet33112kPDFTableBank37145kJPEGCombined-Tabnet(*)33400kPNGCombined(**)33500kPNGSynthTabNet33600kPNGTable 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. +one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples. Tab. 1 summarizes the various attributes of the datasets. 4. The TableFormer model -Given the image of a table, TableFormer is able to pre- +Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required. 4.1. Model architecture. -We now describe in detail the proposed method, which -CNN Backbone Network. +We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. +CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per- 4 -Figure 3: -Figure 4: -forming classification, and adding an adaptive pooling layer -Structure Decoder. -The -Cell BBox Decoder. -The encoding generated by the +Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure. +Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes. +forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder . +Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images. +The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score. +Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query. +The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at- 5 -tention encoding is then multiplied to the encoded image to -The output -Loss Functions. -The loss used to train the TableFormer can be defined as - -where +tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence. +The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer. +Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. +The loss used to train the TableFormer can be defined as following: + +where λ ∈ [0, 1], and λ iou , λ l 1 ∈ R are hyper-parameters. 5. Experimental Results 5.1. Implementation Details -TableFormer uses ResNet-18 as the - -Although input constraints are used also by other methods, runtime performance and lower memory footprint of Table- -The Transformer Encoder consists of two 'Transformer -For training, TableFormer is trained with 3 Adam opti- -TableFormer is implemented with PyTorch and Torchvi- +TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: + +Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions. +The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5. +For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. +TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. 5.2. Generalization -TableFormer is evaluated on three major publicly avail- -We also share our baseline results on the challenging +TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively. +We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized. 6 5.3. Datasets and Metrics -The Tree-Edit-Distance-Based Similarity (TEDS) met- - -where +The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as: + +where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T . 5.4. Quantitative Analysis -Structure. - -Table 2: +Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. +ModelDatasetSimpleTEDS ComplexAllEDDPTN91.188.789.9GTEPTN--93.01TableFormerPTN98.595.096.75EDDFTN88.492.0890.6GTEFTN--87.14GTE (FT)FTN--91.02TableFormerFTN97.596.096.8EDDTB86.0-86.0TableFormerTB89.6-89.6TableFormerSTN96.995.796.7 +Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). FT: Model was trained on PubTabNet then finetuned. -Cell Detection. -our -Table 3: -Cell Content. -Table 4: +Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. +ModelDatasetmAPmAP (PP)EDD+BBoxPubTabNet79.282.7TableFormerPubTabNet82.186.8TableFormerSynthTabNet87.7-Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. +Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. +ModelSimpleTEDS ComplexAllTabula78.057.867.9Traprange60.849.955.4Camelot80.066.073.0Acrobat Pro68.961.865.3EDD91.285.488.3TableFormer95.490.193.6Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. 7 a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells @@ -113,134 +112,129 @@ Japanese language (previously unseen by TableFormer): Example table from FinTabNet: - -b. Structure predicted by TableFormer, with superimposed matched PDF cell text: - - -Text is aligned to match original for ease of viewing -Figure 5: -Figure 6: +b. Structure predicted by TableFormer, with superimposed matched PDF cell text: +論文ファイル参考文献出典ファイル数 英語日本語英語日本語Association for Computational Linguistics(ACL2003)656501500Computational Linguistics(COLING2002)14014001500電気情報通信学会 2003 年総合大会1508142223147情報処理学会第 65 回全国大会 (2003)1771176150236第 17 回人工知能学会全国大会 (2003)2085203152244自然言語処理研究会第 146 〜 155 回98296150232WWW から収集した論文107733414796計9452946511122955Text is aligned to match original for ease of viewing +Shares (in millions)Weighted Average Grant Date Fair ValueRSUsPSUsRSUsPSUsNonvested on January 11.10.390.10 $$ 91.19Granted0.50.1117.44122.41Vested(0.5)(0.1)87.0881.14Canceled or forfeited(0.1)-102.0192.18Nonvested on December 311.00.3104.85 $$ 104.51 +Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table. +Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset. + 5.5. Qualitative Analysis -We showcase - 6. Future Work & Conclusion -In this paper, we presented TableFormer an end-to-end +We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type. +In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets. References -[1] +[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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Data preparation -As a first step of our data preparation process, we have -We have developed a technique that tries -Figure 7 illustrates the distribution of the tables across +As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length. +We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. +Figure 7 illustrates the distribution of the tables across different dimensions per dataset. 1.2. Synthetic datasets -Aiming to train and evaluate our models in a broader -2. -The process of generating a synthetic dataset can be de- -1. -2. -3. Generate content: Based on the dataset -4. -5. +Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). +The process of generating a synthetic dataset can be decomposed into the following steps: +1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). +2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. +3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. +4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table. +5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process. -Although TableFormer can predict the table structure and +2. Prediction post-processing for PDF documents +Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons: 11 -Figure 7: -• -• +Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity. +· TableFormer output does not include the table cell content. +· There are occasional inaccuracies in the predictions of the bounding boxes. -However, it is possible to mitigate those limitations by -Here is a step-by-step description of the prediction post- -1. Get the minimal grid dimensions - number of rows and -2. -3. -3.a. -4. +dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. +However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes. +Here is a step-by-step description of the prediction postprocessing: +1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure. +2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches. +3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones. +3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column. +4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: -where -5. + +where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point. +5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me- +6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes. +7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells. +8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score. +9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan. -dian cell size for all table cells. -6. -7. -8. -9. +9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row). +9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row. +9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column). +9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column. +9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or- -9a. -9b. -9c. -9d. Intersect the orphan's bounding box with the column -9e. If the table cell under the identified row and column - - 12 phan cell. -9f. -Aditional images with examples of TableFormer predic- +9f. Otherwise create a new structural cell and match it wit the orphan cell. +Aditional images with examples of TableFormer predictions and post-processing can be found below. -Figure 8: -Figure 9: +Figure 8: Example of a table with multi-line header. +Figure 9: Example of a table with big empty distance between cells. -Figure 10: +Figure 10: Example of a complex table with empty cells. @@ -249,37 +243,38 @@ -Figure 11: +Figure 11: Simple table with different style and empty cells. -Figure 12: +Figure 12: Simple table predictions and post processing. -Figure 13: -Figure 14: +Figure 13: Table predictions example on colorful table. +Figure 14: Example with multi-line text. 14 -Figure 15: + - -Figure 16: +Figure 15: Example with triangular table. + +Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact. 15 -Figure 17: +Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure. 16 \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.json b/tests/data/groundtruth/docling_v2/2203.01017v2.json index 94b114e2..5b5597c7 100644 --- a/tests/data/groundtruth/docling_v2/2203.01017v2.json +++ b/tests/data/groundtruth/docling_v2/2203.01017v2.json @@ -1 +1 @@ -{"schema_name": "DoclingDocument", "version": "1.2.0", "name": "2203.01017v2", "origin": {"mimetype": "application/pdf", "binary_hash": 10763566541725197878, "filename": "2203.01017v2.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/groups/0"}, {"cref": "#/texts/4"}, {"cref": "#/texts/5"}, {"cref": 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"b": 683.608, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "The second problem is called table-structure decompo-", "text": "The second problem is called table-structure decompo-"}, {"self_ref": "#/texts/17", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 62.067, "t": 560.2, "r": 286.365, "b": 551.648, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "In this paper, we want to address these weaknesses and", "text": "In this paper, we want to address these weaknesses and"}, {"self_ref": "#/texts/18", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 62.067, "t": 416.285, "r": 286.365, "b": 407.733, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "To meet the design criteria listed above, we developed a", "text": "To meet the design criteria listed above, we developed a"}, {"self_ref": 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"marker": "-"}, {"self_ref": "#/texts/22", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569, "t": 186.32799999999997, "r": 65.056, "b": 177.77600000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "\u2022", "text": "\u2022", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 62.067, "t": 141.13300000000004, "r": 204.452, "b": 132.58100000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "The paper is structured as follows.", "text": "The paper is structured as follows."}, {"self_ref": "#/texts/24", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 60.971, "t": 87.70399999999995, "r": 63.96, "b": 82.57299999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/25", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.862, "t": 716.523, "r": 545.115, "b": 707.971, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text": "its results & performance in Sec. 5. As a conclusion, we de-"}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 308.862, "t": 670.268, "r": 498.28, "b": 659.52, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "2. Previous work and State of the Art", "text": "2. Previous work and State of the Art", "level": 1}, {"self_ref": "#/texts/27", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 320.817, "t": 649.51, "r": 545.115, "b": 640.958, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Identifying the structure of a table has been an outstand-", "text": "Identifying the structure of a table has been an outstand-"}, {"self_ref": "#/texts/28", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 320.817, "t": 458.162, "r": 391.492, "b": 449.61, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Before the rising", "text": "Before the rising"}, {"self_ref": "#/texts/29", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 320.817, "t": 338.932, "r": 423.262, "b": 329.976, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Image-to-Text networks", "text": "Image-to-Text networks"}, {"self_ref": "#/texts/30", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/31", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.112, "t": 716.523, "r": 250.151, "b": 707.971, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "tag-decoder which is constrained to the table-tags.", "text": "tag-decoder which is constrained to the table-tags."}, {"self_ref": "#/texts/32", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 62.067, "t": 704.512, "r": 70.366, "b": 695.96, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "In", "text": "In"}, {"self_ref": "#/texts/33", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 62.067, "t": 513.561, "r": 90.301, "b": 504.605, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Graph", "text": "Graph"}, {"self_ref": "#/texts/34", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 62.067, "t": 298.311, "r": 252.881, "b": 289.355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Hybrid Deep Learning-Rule-Based approach", "text": "Hybrid Deep Learning-Rule-Based approach"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 50.112, "t": 156.05500000000006, "r": 105.225, "b": 145.30700000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "3. Datasets", "text": "3. Datasets", "level": 1}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 62.067, "t": 135.30600000000004, "r": 286.365, "b": 126.75400000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text": "We rely on large-scale datasets such as PubTabNet [37],"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 308.862, "t": 523.895, "r": 346.062, "b": 515.343, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Figure 2:", "text": "Figure 2:"}, {"self_ref": "#/texts/38", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 380.799, "t": 711.846, "r": 486.849, "b": 703.533, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "PubTabNet + FinTabNet", "text": "PubTabNet + FinTabNet", "level": 1}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.862, "t": 474.258, "r": 437.27, "b": 465.706, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "balance in the previous datasets.", "text": "balance in the previous datasets."}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 320.817, "t": 460.2, "r": 545.115, "b": 451.648, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "The PubTabNet dataset contains 509k tables delivered as", "text": "The PubTabNet dataset contains 509k tables delivered as"}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 320.817, "t": 159.21699999999998, "r": 545.115, "b": 150.66499999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Due to the heterogeneity across the dataset formats, it", "text": "Due to the heterogeneity across the dataset formats, it"}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.112, "t": 716.523, "r": 286.365, "b": 707.971, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "amount of such tables, and kept only those ones ranging", "text": "amount of such tables, and kept only those ones ranging"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 62.067, "t": 690.771, "r": 286.365, "b": 682.219, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "The availability of the bounding boxes for all table cells", "text": "The availability of the bounding boxes for all table cells"}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 62.067, "t": 473.736, "r": 286.365, "b": 465.184, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "As it is illustrated in Fig. 2, the table distributions from", "text": "As it is illustrated in Fig. 2, the table distributions from"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 62.067, "t": 352.342, "r": 286.365, "b": 343.79, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Motivated by those observations we aimed at generating", "text": "Motivated by those observations we aimed at generating"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 62.067, "t": 159.21699999999998, "r": 286.365, "b": 150.66499999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "In this regard, we have prepared four synthetic datasets,", "text": "In this regard, we have prepared four synthetic datasets,"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/tables/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 624.07, "r": 330.75, "b": 615.518, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 542.111, "r": 545.115, "b": 533.559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "one adopts a colorful appearance with high contrast and the", "text": "one adopts a colorful appearance with high contrast and the"}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.817, "t": 493.959, "r": 542.744, "b": 485.407, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"self_ref": "#/texts/51", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 470.816, "r": 444.936, "b": 460.068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4. The TableFormer model", "text": "4. The TableFormer model", "level": 1}, {"self_ref": "#/texts/52", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.817, "t": 449.792, "r": 545.115, "b": 441.24, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Given the image of a table, TableFormer is able to pre-", "text": "Given the image of a table, TableFormer is able to pre-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 334.306, "r": 420.161, "b": 324.454, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4.1. Model architecture.", "text": "4.1. Model architecture.", "level": 1}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.817, "t": 314.966, "r": 545.115, "b": 306.414, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "We now describe in detail the proposed method, which", "text": "We now describe in detail the proposed method, which"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.817, "t": 123.73900000000003, "r": 431.91, "b": 114.78300000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "CNN Backbone Network.", "text": "CNN Backbone Network."}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.112, "t": 587.626, "r": 86.884, "b": 579.074, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Figure 3:", "text": "Figure 3:"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.112, "t": 263.82899999999995, "r": 85.878, "b": 255.27700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Figure 4:", "text": "Figure 4:"}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.862, "t": 542.197, "r": 545.115, "b": 533.645, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "forming classification, and adding an adaptive pooling layer", "text": "forming classification, and adding an adaptive pooling layer"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 320.817, "t": 494.66, "r": 400.866, "b": 485.704, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Structure Decoder.", "text": "Structure Decoder."}, {"self_ref": "#/texts/61", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 320.817, "t": 374.617, "r": 336.309, "b": 366.065, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "The", "text": "The"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 320.817, "t": 243.39499999999998, "r": 404.762, "b": 234.43899999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Cell BBox Decoder.", "text": "Cell BBox Decoder."}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 320.817, "t": 135.30600000000004, "r": 444.343, "b": 126.75400000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "The encoding generated by the", "text": "The encoding generated by the"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 716.523, "r": 286.365, "b": 707.971, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "tention encoding is then multiplied to the encoded image to", "text": "tention encoding is then multiplied to the encoded image to"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 62.067, "t": 632.107, "r": 108.154, "b": 623.555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "The output", "text": "The output"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 62.067, "t": 548.078, "r": 129.215, "b": 539.122, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Loss Functions.", "text": "Loss Functions."}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 62.067, "t": 343.721, "r": 286.365, "b": 335.169, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "The loss used to train the TableFormer can be defined as", "text": "The loss used to train the TableFormer can be defined as"}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 125.715, "t": 298.609, "r": 128.688, "b": 289.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "l", "text": ""}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 260.422, "r": 74.451, "b": 251.87, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "where", "text": "where"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 236.08299999999997, "r": 171.983, "b": 225.33500000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5. Experimental Results", "text": "5. Experimental Results", "level": 1}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 215.736, "r": 179.175, "b": 205.88400000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "5.1. Implementation Details", "text": "5.1. Implementation Details", "level": 1}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 62.067, "t": 195.99700000000007, "r": 202.978, "b": 187.44499999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "TableFormer uses ResNet-18 as the", "text": "TableFormer uses ResNet-18 as the"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 91.661, "t": 137.18600000000004, "r": 186.017, "b": 128.63400000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Image width and height", "text": ""}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 99.44100000000003, "r": 286.365, "b": 90.88900000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}, {"page_no": 6, "bbox": {"l": 50.112, "t": 99.44100000000003, "r": 286.365, "b": 90.88900000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [59, 115]}], "orig": "Although input constraints are used also by other methods, runtime performance and lower memory footprint of Table-", "text": "Although input constraints are used also by other methods, runtime performance and lower memory footprint of Table-"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 320.817, "t": 675.502, "r": 545.115, "b": 666.95, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "The Transformer Encoder consists of two 'Transformer", "text": "The Transformer Encoder consists of two 'Transformer"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 320.817, "t": 455.154, "r": 545.115, "b": 446.602, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "For training, TableFormer is trained with 3 Adam opti-", "text": "For training, TableFormer is trained with 3 Adam opti-"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 320.817, "t": 354.357, "r": 545.115, "b": 345.805, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text": "TableFormer is implemented with PyTorch and Torchvi-"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 308.862, "t": 212.44600000000003, "r": 397.443, "b": 202.59400000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "5.2. Generalization", "text": "5.2. Generalization", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 320.817, "t": 188.28200000000004, "r": 545.115, "b": 179.73000000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "TableFormer is evaluated on three major publicly avail-", "text": "TableFormer is evaluated on three major publicly avail-"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 320.817, "t": 111.39599999999996, "r": 545.115, "b": 102.84400000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "We also share our baseline results on the challenging", "text": "We also share our baseline results on the challenging"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 717.599, "r": 167.898, "b": 707.747, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.3. 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Details on the datasets", "text": "1. Details on the datasets", "level": 1}, {"self_ref": "#/texts/155", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 629.076, "r": 426.929, "b": 620.524, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "ances in regard to their size,", "text": "ances in regard to their size,"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 611.021, "r": 150.364, "b": 601.169, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "1.1. Data preparation", "text": "1.1. Data preparation", "level": 1}, {"self_ref": "#/texts/157", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 62.067, "t": 591.811, "r": 286.365, "b": 583.259, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "As a first step of our data preparation process, we have", "text": "As a first step of our data preparation process, we have"}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 62.067, "t": 400.326, "r": 236.243, "b": 391.774, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "We have developed a technique that tries", "text": "We have developed a technique that tries"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 62.067, "t": 161.02099999999996, "r": 286.365, "b": 152.46900000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Figure 7 illustrates the distribution of the tables across", "text": "Figure 7 illustrates the distribution of the tables across"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 129.61, "r": 153.608, "b": 119.75800000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "1.2. Synthetic datasets", "text": "1.2. Synthetic datasets", "level": 1}, {"self_ref": "#/texts/161", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 62.067, "t": 110.39999999999998, "r": 286.365, "b": 101.84799999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Aiming to train and evaluate our models in a broader", "text": "Aiming to train and evaluate our models in a broader"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 169.70900000000006, "r": 317.828, "b": 158.961, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2.", "level": 1}, {"self_ref": "#/texts/163", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 320.817, "t": 580.496, "r": 545.115, "b": 571.944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "The process of generating a synthetic dataset can be de-", "text": "The process of generating a synthetic dataset can be de-"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 320.817, "t": 555.826, "r": 328.289, "b": 547.274, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 320.817, "t": 471.381, "r": 328.289, "b": 462.829, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/166", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 320.817, "t": 339.115, "r": 485.758, "b": 330.563, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "3. Generate content: Based on the dataset", "text": "3. 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1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 142.477, "t": 644.992, "r": 452.75, "b": 620.783, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "text": "Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 208.123, "t": 615.438, "r": 378.733, "b": 607.73, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "{ ahn,nli,mly,taa @zurich.ibm.com }", "text": "{ ahn,nli,mly,taa @zurich.ibm.com }", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 145.995, "t": 576.517, "r": 190.48, "b": 565.769, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Abstract", "text": "Abstract", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 315.567, "t": 573.651, "r": 408.441, "b": 565.338, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "a. Picture of a table:", "text": "a. Picture of a table:", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 50.112, "t": 550.605, "r": 286.365, "b": 279.00299999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1320]}], "orig": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 50.112, "t": 252.05700000000002, "r": 126.948, "b": 241.30899999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "1. Introduction", "text": "1. Introduction", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 50.112, "t": 230.94799999999998, "r": 286.365, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 712]}], "orig": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 451.946, "t": 556.523, "r": 457.951, "b": 546.56, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 331.197, "t": 522.518, "r": 337.202, "b": 512.554, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/11", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 384.033, "t": 539.191, "r": 390.038, "b": 529.228, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/12", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 1, "bbox": {"l": 315.567, "t": 477.963, "r": 486.402, "b": 458.85, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "b. 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"BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.862, "t": 207.322, "r": 545.115, "b": 127.03899999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 363]}], "orig": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 308.862, "t": 123.351, "r": 545.115, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 229]}], "orig": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.112, "t": 716.523, "r": 286.365, "b": 696.016, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "considered as a solved problem, given enough ground-truth data to train on.", "text": "considered as a solved problem, given enough ground-truth data to train on.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.112, "t": 692.16, "r": 286.365, "b": 564.056, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.112, "t": 560.2, "r": 286.365, "b": 420.141, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 643]}], "orig": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.112, "t": 416.285, "r": 286.367, "b": 359.913, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows:", "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/70", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569, "t": 347.568, "r": 286.365, "b": 302.763, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569, "t": 289.966, "r": 286.365, "b": 245.15999999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 181]}], "orig": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569, "t": 232.36300000000006, "r": 286.365, "b": 199.51300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 61.569, "t": 186.32799999999997, "r": 286.365, "b": 153.865, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 50.112, "t": 141.13300000000004, "r": 286.365, "b": 96.71600000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 60.971, "t": 87.70399999999995, "r": 183.731, "b": 79.34699999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "1 https://github.com/IBM/SynthTabNet", "text": "1 https://github.com/IBM/SynthTabNet", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.862, "t": 716.523, "r": 545.115, "b": 684.061, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 166]}], "orig": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 308.862, "t": 670.268, "r": 498.28, "b": 659.52, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "2. Previous work and State of the Art", "text": "2. Previous work and State of the Art", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.862, "t": 649.51, "r": 545.115, "b": 461.631, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 901]}], "orig": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.862, "t": 458.162, "r": 545.115, "b": 342.013, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 552]}], "orig": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "text": "Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 308.862, "t": 338.932, "r": 545.117, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1262]}], "orig": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder \u2192 text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder \u2192 dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder \u2192 text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder \u2192 dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.112, "t": 716.523, "r": 250.151, "b": 707.971, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "tag-decoder which is constrained to the table-tags.", "text": "tag-decoder which is constrained to the table-tags.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.112, "t": 704.512, "r": 286.365, "b": 516.632, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 864]}], "orig": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.112, "t": 513.561, "r": 286.365, "b": 301.383, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1007]}], "orig": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18].", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.112, "t": 298.311, "r": 286.366, "b": 169.81899999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 618]}], "orig": "Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "text": "Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 50.112, "t": 156.05500000000006, "r": 105.225, "b": 145.30700000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "3. Datasets", "text": "3. Datasets", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 50.112, "t": 135.30600000000004, "r": 286.365, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/88", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 308.862, "t": 523.895, "r": 545.115, "b": 503.388, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "text": "Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/89", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 380.799, "t": 711.846, "r": 486.849, "b": 703.533, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "PubTabNet + FinTabNet", "text": "PubTabNet + FinTabNet", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 396.768, "t": 549.631, "r": 469.787, "b": 541.318, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Rows / Columns", "text": "Rows / Columns", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 320.977, "t": 558.349, "r": 324.793, "b": 552.807, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 410.483, "t": 558.349, "r": 418.113, "b": 552.807, "coord_origin": "BOTTOMLEFT"}, 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"text": "0", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/108", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.88, "t": 683.448, "r": 547.613, "b": 676.52, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "10K", "text": "10K", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/109", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.774, "t": 660.934, "r": 542.739, "b": 654.006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8K", "text": "8K", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/110", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.799, "t": 637.792, "r": 542.764, "b": 630.864, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6K", "text": "6K", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/111", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.571, "t": 614.957, "r": 542.536, "b": 608.029, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4K", "text": "4K", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/112", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 532.146, "t": 592.069, "r": 542.111, "b": 585.141, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2K", "text": "2K", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/113", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.862, "t": 474.258, "r": 437.27, "b": 465.706, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "balance in the previous datasets.", "text": "balance in the previous datasets.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/114", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.862, "t": 460.2, "r": 545.115, "b": 164.72399999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1400]}], "orig": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/115", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 308.862, "t": 159.21699999999998, "r": 545.115, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 406]}], "orig": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/116", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 3, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/117", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.112, "t": 716.523, "r": 286.365, "b": 696.016, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/118", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.112, "t": 690.771, "r": 286.365, "b": 478.981, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 983]}], "orig": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/119", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.112, "t": 473.736, "r": 286.365, "b": 357.587, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 571]}], "orig": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/120", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.112, "t": 352.342, "r": 286.367, "b": 164.462, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 941]}], "orig": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/121", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 50.112, "t": 159.21699999999998, "r": 286.365, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/122", "parent": {"cref": "#/tables/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 624.249, "r": 545.115, "b": 567.697, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 267]}], "orig": "Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "text": "Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/123", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 542.111, "r": 545.115, "b": 497.694, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 210]}], "orig": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/124", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 320.817, "t": 493.959, "r": 542.744, "b": 485.407, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text": "Tab. 1 summarizes the various attributes of the datasets.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/125", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 470.816, "r": 444.936, "b": 460.068, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4. The TableFormer model", "text": "4. The TableFormer model", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/126", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 449.792, "r": 545.115, "b": 345.599, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 504]}], "orig": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/127", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 334.306, "r": 420.161, "b": 324.454, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "4.1. Model architecture.", "text": "4.1. Model architecture.", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/128", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 314.966, "r": 545.116, "b": 127.08600000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 907]}], "orig": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 308.862, "t": 123.73900000000003, "r": 545.115, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 223]}], "orig": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.112, "t": 588.014, "r": 545.108, "b": 567.119, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 212]}], "orig": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.", "text": "Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. 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"formatting": null, "hyperlink": null}, {"self_ref": "#/texts/184", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 454.083, "t": 701.358, "r": 457.486, "b": 695.712, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/185", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 50.112, "t": 264.217, "r": 286.366, "b": 111.81500000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 745]}], "orig": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.", "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. 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"orig": "x4", "text": "x4", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/222", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 221.456, "t": 518.866, "r": 276.471, "b": 514.452, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CELL BBOX DECODER", "text": "CELL BBOX DECODER", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/223", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 151.652, "t": 467.356, "r": 197.29, "b": 463.163, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Masked Multi-Head", "text": "Masked Multi-Head", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/224", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 163.433, "t": 461.356, "r": 184.19, "b": 457.163, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Attention", "text": "Attention", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/225", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.862, "t": 542.197, "r": 545.115, "b": 497.779, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 227]}], "orig": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder .", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/226", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.862, "t": 494.66, "r": 545.115, "b": 378.124, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images.", "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/227", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.862, "t": 374.617, "r": 545.115, "b": 246.51300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 592]}], "orig": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/228", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.862, "t": 243.39499999999998, "r": 545.115, "b": 138.813, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 483]}], "orig": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 308.862, "t": 135.485, "r": 545.115, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 286]}], "orig": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 716.523, "r": 286.365, "b": 636.24, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 380]}], "orig": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/232", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 632.107, "r": 286.365, "b": 551.823, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 371]}], "orig": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/233", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 548.078, "r": 286.366, "b": 347.855, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 965]}], "orig": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/234", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 343.721, "r": 286.365, "b": 323.214, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "The loss used to train the TableFormer can be defined as following:", "text": "The loss used to train the TableFormer can be defined as following:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/235", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 124.33, "t": 298.609, "r": 286.362, "b": 274.03600000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "l box = \u03bb iou iou l + \u03bb l 1 l = \u03bbl s +(1 -\u03bb l ) box (1)", "text": "", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/236", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 260.74, "r": 281.597, "b": 251.111, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "where \u03bb \u2208 [0, 1], and \u03bb iou , \u03bb l 1 \u2208 R are hyper-parameters.", "text": "where \u03bb \u2208 [0, 1], and \u03bb iou , \u03bb l 1 \u2208 R are hyper-parameters.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/237", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 236.08299999999997, "r": 171.983, "b": 225.33500000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5. Experimental Results", "text": "5. Experimental Results", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 215.736, "r": 179.175, "b": 205.88400000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "5.1. Implementation Details", "text": "5.1. Implementation Details", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/239", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 196.17599999999993, "r": 286.365, "b": 151.57899999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 6, "bbox": {"l": 91.661, "t": 137.50400000000002, "r": 286.362, "b": 113.69000000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "Image width and height \u2264 1024 pixels Structural tags length \u2264 512 tokens. (2)", "text": "", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 50.112, "t": 99.44100000000003, "r": 286.365, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}, {"page_no": 6, "bbox": {"l": 50.112, "t": 99.44100000000003, "r": 286.365, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [118, 274]}], "orig": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/242", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862, "t": 675.502, "r": 545.115, "b": 463.712, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1024]}], "orig": "The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "text": "The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/243", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862, "t": 455.154, "r": 545.115, "b": 362.916, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 419]}], "orig": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/244", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862, "t": 354.357, "r": 545.115, "b": 238.20900000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 528]}], "orig": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/245", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 308.862, "t": 212.44600000000003, "r": 397.443, "b": 202.59400000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "5.2. Generalization", "text": "5.2. Generalization", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/246", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862, "t": 188.28200000000004, "r": 545.115, "b": 119.95399999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/247", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 308.862, "t": 111.39599999999996, "r": 545.115, "b": 78.93299999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 155]}], "orig": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/248", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/249", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 717.599, "r": 167.898, "b": 707.747, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.3. Datasets and Metrics", "text": "5.3. Datasets and Metrics", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/250", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 698.381, "r": 286.365, "b": 653.963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/251", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 7, "bbox": {"l": 86.219, "t": 641.572, "r": 286.362, "b": 618.368, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "TEDS( T , T a b ) = 1 -EditDist ( T , T a b ) max( | T a | , | T b | ) (3)", "text": "", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/252", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 610.887, "r": 286.363, "b": 578.107, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 154]}], "orig": "where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "text": "where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/253", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 567.181, "r": 170.452, "b": 557.329, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "5.4. Quantitative Analysis", "text": "5.4. Quantitative Analysis", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/254", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 548.35, "r": 286.365, "b": 395.948, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 723]}], "orig": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "text": "Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/255", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 199.298, "r": 286.365, "b": 178.79099999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "text": "Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/256", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 175.38800000000003, "r": 261.787, "b": 166.836, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "FT: Model was trained on PubTabNet then finetuned.", "text": "FT: Model was trained on PubTabNet then finetuned.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/257", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 50.112, "t": 147.64999999999998, "r": 286.366, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 346]}, {"page_no": 7, "bbox": {"l": 50.112, "t": 147.64999999999998, "r": 286.366, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [347, 1084]}], "orig": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/258", "parent": {"cref": "#/tables/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.862, "t": 475.282, "r": 545.115, "b": 454.775, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/259", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 308.862, "t": 424.32, "r": 545.116, "b": 271.918, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 715]}], "orig": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/260", "parent": {"cref": "#/tables/5"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 308.862, "t": 134.87, "r": 545.115, "b": 102.40800000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 148]}], "orig": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/261", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 295.121, "t": 57.597999999999956, "r": 300.102, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/262", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 53.286, "t": 713.005, "r": 499.556, "b": 705.523, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/263", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.812, "t": 697.411, "r": 284.346, "b": 689.929, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Japanese language (previously unseen by TableFormer):", "text": "Japanese language (previously unseen by TableFormer):", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/264", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 304.831, "t": 697.411, "r": 431.091, "b": 689.929, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "Example table from FinTabNet:", "text": "Example table from FinTabNet:", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/265", "parent": {"cref": "#/pictures/7"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 53.812, "t": 583.459, "r": 385.934, "b": 575.977, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/266", "parent": {"cref": "#/tables/6"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 380.427, "t": 499.449, "r": 549.422, "b": 493.464, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Text is aligned to match original for ease of viewing", "text": "Text is aligned to match original for ease of viewing", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/267", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 62.595, "t": 333.003, "r": 532.63, "b": 324.451, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "text": "Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/268", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.715, "t": 410.04, "r": 85.657, "b": 405.607, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "Ground Truth", "text": "Ground Truth", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/269", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 50.112, "t": 470.854, "r": 545.114, "b": 426.436, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/270", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 220.263, "t": 410.04, "r": 342.078, "b": 405.607, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "Red - PDF cells, Green - predicted bounding boxes", "text": "Red - PDF cells, Green - predicted bounding boxes", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/271", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 437.379, "t": 390.891, "r": 443.699, "b": 385.314, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16", "formatting": null, "hyperlink": null}, 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{"self_ref": "#/texts/323", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 424.427, "t": 384.669, "r": 430.746, "b": 379.092, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/324", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 502.869, "t": 380.45, "r": 509.189, "b": 374.872, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "29", "text": "29", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/325", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 384.354, "t": 410.04, "r": 430.993, "b": 405.607, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Predicted Structure", "text": "Predicted Structure", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/326", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 50.112, "t": 300.605, "r": 163.756, "b": 290.753, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "5.5. Qualitative Analysis", "text": "5.5. Qualitative Analysis", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/327", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.862, "t": 301.291, "r": 460.848, "b": 290.543, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "6. Future Work & Conclusion", "text": "6. Future Work & Conclusion", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/328", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 50.112, "t": 254.85799999999995, "r": 286.365, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 866]}], "orig": "We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "text": "We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/329", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 308.862, "t": 278.83799999999997, "r": 545.115, "b": 138.77999999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/330", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 308.862, "t": 119.90099999999995, "r": 364.406, "b": 109.15300000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/331", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 313.345, "t": 97.79600000000005, "r": 545.113, "b": 79.13999999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 121]}], "orig": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/374", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 292.63, "t": 57.597999999999956, "r": 302.593, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/375", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 132.842, "t": 681.425, "r": 465.376, "b": 656.516, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/376", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 630.839, "r": 175.964, "b": 620.091, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1. Details on the datasets", "text": "1. Details on the datasets", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/377", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 611.021, "r": 150.364, "b": 601.169, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "1.1. Data preparation", "text": "1.1. Data preparation", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/378", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 591.811, "r": 286.365, "b": 403.931, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 931]}], "orig": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length.", "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/379", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 400.326, "r": 286.365, "b": 164.62599999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1149]}], "orig": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/380", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 161.02099999999996, "r": 286.365, "b": 140.51300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 92]}], "orig": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/381", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 129.61, "r": 153.608, "b": 119.75800000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "1.2. Synthetic datasets", "text": "1.2. Synthetic datasets", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/382", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 50.112, "t": 110.39999999999998, "r": 286.365, "b": 77.93799999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}, {"page_no": 11, "bbox": {"l": 50.112, "t": 110.39999999999998, "r": 286.365, "b": 77.93799999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [168, 389]}], "orig": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/383", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 580.496, "r": 545.115, "b": 559.989, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "The process of generating a synthetic dataset can be decomposed into the following steps:", "text": "The process of generating a synthetic dataset can be decomposed into the following steps:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/384", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 555.826, "r": 545.115, "b": 475.543, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 373]}], "orig": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/385", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 471.381, "r": 545.115, "b": 343.277, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/386", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 339.294, "r": 545.115, "b": 294.697, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/387", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 290.535, "r": 545.115, "b": 246.11699999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 218]}], "orig": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/388", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 241.95500000000004, "r": 545.115, "b": 185.582, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 238]}], "orig": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/389", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 169.70900000000006, "r": 545.109, "b": 145.01300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "2. Prediction post-processing for PDF documents", "text": "2. Prediction post-processing for PDF documents", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/390", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 308.862, "t": 134.30999999999995, "r": 545.115, "b": 77.93700000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/391", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 292.631, "t": 57.597999999999956, "r": 302.594, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/392", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 626.229, "r": 545.114, "b": 605.722, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 245]}], "orig": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/393", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 119.391, "t": 714.457, "r": 151.946, "b": 708.804, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "PubTabNet", "text": "PubTabNet", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/394", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 53.346, "t": 716.576, "r": 59.327, "b": 710.923, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "b.", "text": "b.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/395", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 289.579, "t": 714.309, "r": 319.827, "b": 708.656, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "FinTabNet", "text": "FinTabNet", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/396", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 448.373, "t": 714.513, "r": 481.759, "b": 708.86, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Table Bank", "text": "Table Bank", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/397", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 82.553, "t": 650.53, "r": 105.08, "b": 645.819, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Train Val", "text": "Train Val", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/398", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 63.039, "t": 690.702, "r": 85.29, "b": 685.991, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Complex", "text": "Complex", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/399", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 67.768, "t": 667.411, "r": 85.231, "b": 662.7, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Simple", "text": "Simple", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/400", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 227.551, "t": 689.266, "r": 249.803, "b": 684.555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Complex", "text": "Complex", "formatting": null, "hyperlink": null}, {"self_ref": 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"b": 701.039, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "47K", "text": "47K", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/448", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 508.543, "t": 650.429, "r": 526.006, "b": 645.718, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Simple", "text": "Simple", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/449", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 510.447, "t": 705.714, "r": 523.707, "b": 701.003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "145K", "text": "145K", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/450", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569, "t": 580.8, "r": 286.365, "b": 560.293, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "\u00b7 TableFormer output does not include the table cell content.", "text": "\u00b7 TableFormer output does not include the table cell content.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/451", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 61.569, "t": 547.66, "r": 286.365, "b": 527.153, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/452", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 580.8, "r": 545.115, "b": 536.382, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 183]}], "orig": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/453", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 512.528, "r": 286.365, "b": 396.379, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 545]}], "orig": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/454", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 392.662, "r": 286.365, "b": 372.154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Here is a step-by-step description of the prediction postprocessing:", "text": "Here is a step-by-step description of the prediction postprocessing:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/455", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 368.436, "r": 286.365, "b": 335.974, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/456", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 332.256, "r": 286.365, "b": 287.839, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 187]}], "orig": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/457", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 284.121, "r": 286.365, "b": 263.61300000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones.", "text": "3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/458", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 259.89599999999996, "r": 286.365, "b": 227.433, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 131]}], "orig": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/459", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 223.71500000000003, "r": 286.365, "b": 191.25299999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 169]}], "orig": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/460", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "formula", "prov": [{"page_no": 12, "bbox": {"l": 110.705, "t": 167.89599999999996, "r": 286.362, "b": 137.00199999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "alignment = arg min c { D c } D c = max x { c } -min x { c } (4)", "text": "", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/461", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 123.98400000000004, "r": 286.362, "b": 103.15899999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point.", "text": "where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/462", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 50.112, "t": 99.44100000000003, "r": 286.365, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 110]}], "orig": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/463", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 532.629, "r": 545.115, "b": 512.122, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/464", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 508.368, "r": 545.115, "b": 404.175, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 471]}], "orig": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/465", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 400.421, "r": 545.115, "b": 332.094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 311]}], "orig": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/466", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 328.34, "r": 545.115, "b": 224.14699999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 503]}], "orig": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/467", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 220.39300000000003, "r": 545.117, "b": 187.93100000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 113]}], "orig": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/468", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 184.178, "r": 545.115, "b": 163.66999999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/469", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 159.91700000000003, "r": 545.115, "b": 127.45500000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 117]}], "orig": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column).", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/470", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 123.70100000000002, "r": 545.115, "b": 103.19399999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 107]}], "orig": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/471", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 308.862, "t": 99.44100000000003, "r": 545.115, "b": 78.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 118]}], "orig": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/472", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 292.631, "t": 57.597999999999956, "r": 302.594, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/473", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.112, "t": 716.523, "r": 88.847, "b": 707.971, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "phan cell.", "text": "phan cell.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/474", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.112, "t": 704.568, "r": 286.365, "b": 684.061, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "text": "9f. Otherwise create a new structural cell and match it wit the orphan cell.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/475", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 50.112, "t": 680.837, "r": 286.365, "b": 660.294, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "text": "Aditional images with examples of TableFormer predictions and post-processing can be found below.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/476", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 63.341, "t": 289.675, "r": 273.133, "b": 281.123, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 8: Example of a table with multi-line header.", "text": "Figure 8: Example of a table with multi-line header.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/477", "parent": {"cref": "#/tables/12"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 308.862, "t": 485.133, "r": 545.115, "b": 464.626, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 9: Example of a table with big empty distance between cells.", "text": "Figure 9: Example of a table with big empty distance between cells.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/478", "parent": {"cref": "#/tables/16"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 312.343, "t": 111.23800000000006, "r": 541.632, "b": 102.68600000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 10: Example of a complex table with empty cells.", "text": "Figure 10: Example of a complex table with empty cells.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/479", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 292.631, "t": 57.597999999999956, "r": 302.594, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/480", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 50.112, "t": 434.961, "r": 286.365, "b": 414.454, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "Figure 11: Simple table with different style and empty cells.", "text": "Figure 11: Simple table with different style and empty cells.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/481", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 54.619, "t": 119.91300000000001, "r": 281.856, "b": 111.36099999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Figure 12: Simple table predictions and post processing.", "text": "Figure 12: Simple table predictions and post processing.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/482", "parent": {"cref": "#/pictures/16"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 315.79, "t": 420.047, "r": 538.185, "b": 411.495, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 55]}], "orig": "Figure 13: Table predictions example on colorful table.", "text": "Figure 13: Table predictions example on colorful table.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/483", "parent": {"cref": "#/tables/27"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 344.985, "t": 108.18499999999995, "r": 508.989, "b": 99.63300000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Figure 14: Example with multi-line text.", "text": "Figure 14: Example with multi-line text.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/484", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 292.631, "t": 57.597999999999956, "r": 302.594, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "14", "text": "14", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/485", "parent": {"cref": "#/tables/33"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 84.233, "t": 147.38, "r": 252.242, "b": 138.82799999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Figure 15: Example with triangular table.", "text": "Figure 15: Example with triangular table.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/486", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 308.862, "t": 138.79600000000005, "r": 545.115, "b": 118.28899999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "text": "Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/487", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 292.631, "t": 57.597999999999956, "r": 302.594, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/488", "parent": {"cref": "#/pictures/23"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 16, "bbox": {"l": 50.112, "t": 283.394, "r": 545.114, "b": 262.88699999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "text": "Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 292.631, "t": 57.597999999999956, "r": 302.594, "b": 49.04600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "16", "text": "16", "formatting": null, "hyperlink": null}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/9"}, {"cref": "#/texts/10"}, {"cref": "#/texts/11"}], "content_layer": "body", "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 315.65362548828125, "t": 563.276611328125, "r": 537.1475219726562, "b": 489.1985778808594, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [], "references": [], "footnotes": [], "image": null, "annotations": []}, 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"image": null, "page_no": 11}, "12": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 12}, "13": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 13}, "14": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 14}, "15": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 15}, "16": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 16}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.md b/tests/data/groundtruth/docling_v2/2203.01017v2.md index d38c5607..c3ada48f 100644 --- a/tests/data/groundtruth/docling_v2/2203.01017v2.md +++ b/tests/data/groundtruth/docling_v2/2203.01017v2.md @@ -1,184 +1,226 @@ ## TableFormer: Table Structure Understanding with Transformers. -## Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar +## Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research -{ +{ ahn,nli,mly,taa @zurich.ibm.com } ## Abstract -Tables organize valuable content in a concise and com- +## a. Picture of a table: + +Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. ## 1. Introduction -The occurrence of tables - -## a. Picture of a table: +The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues. -- b. Red-annotation of bounding boxes, +- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer - c. Structure predicted by TableFormer: -Figure 1: +Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. -Recently, +| 0 | 1 2 1 | 1 2 1 | 1 2 1 | 1 2 1 | +|-----|---------|---------|---------|---------| +| 3 | 4 3 | 5 | 6 | 7 | +| 8 2 | 9 | 10 | 11 | 12 | +| 13 | | 14 | 15 | 16 | +| 17 | 18 | | 19 | 20 | -The first problem is called table-location and has been +Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. -considered as a solved problem, given enough ground-truth +The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be -The second problem is called table-structure decompo- +considered as a solved problem, given enough ground-truth data to train on. -In this paper, we want to address these weaknesses and +The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. -To meet the design criteria listed above, we developed a +In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image. -- • -- • -- • -- • +To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows: -The paper is structured as follows. +- · We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. +- · Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works. +- · We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity. +- · An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility. -its results & performance in Sec. 5. As a conclusion, we de- +The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe + +its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. ## 2. Previous work and State of the Art -Identifying the structure of a table has been an outstand- +Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc. -Before the rising +Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification. -Image-to-Text networks +Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder → text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder → dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the tag-decoder which is constrained to the table-tags. -In +In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper. -Graph +Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]. -Hybrid Deep Learning-Rule-Based approach +Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. ## 3. Datasets -We rely on large-scale datasets such as PubTabNet [37], +We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im- -Figure 2: +Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets balance in the previous datasets. -The PubTabNet dataset contains 509k tables delivered as +The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits. -Due to the heterogeneity across the dataset formats, it +Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small -amount of such tables, and kept only those ones ranging +amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns). -The availability of the bounding boxes for all table cells +The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. -As it is illustrated in Fig. 2, the table distributions from +As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. -Motivated by those observations we aimed at generating +Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. -In this regard, we have prepared four synthetic datasets, +In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third -Table +Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. -one adopts a colorful appearance with high contrast and the +| | Tags | Bbox | Size | Format | +|--------------------|--------|--------|--------|----------| +| PubTabNet | 3 | 3 | 509k | PNG | +| FinTabNet | 3 | 3 | 112k | PDF | +| TableBank | 3 | 7 | 145k | JPEG | +| Combined-Tabnet(*) | 3 | 3 | 400k | PNG | +| Combined(**) | 3 | 3 | 500k | PNG | +| SynthTabNet | 3 | 3 | 600k | PNG | + +one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples. Tab. 1 summarizes the various attributes of the datasets. ## 4. The TableFormer model -Given the image of a table, TableFormer is able to pre- +Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required. ## 4.1. Model architecture. -We now describe in detail the proposed method, which +We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. -CNN Backbone Network. +CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per- -Figure 3: +Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure. -Figure 4: +Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes. -forming classification, and adding an adaptive pooling layer +forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder . -Structure Decoder. +Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images. -The +The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score. -Cell BBox Decoder. +Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query. -The encoding generated by the +The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at- -tention encoding is then multiplied to the encoded image to +tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence. -The output +The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer. -Loss Functions. +Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. -The loss used to train the TableFormer can be defined as +The loss used to train the TableFormer can be defined as following: -where +where λ ∈ [0, 1], and λ iou , λ l 1 ∈ R are hyper-parameters. ## 5. Experimental Results ## 5.1. Implementation Details -TableFormer uses ResNet-18 as the +TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: -Although input constraints are used also by other methods, runtime performance and lower memory footprint of Table- +Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions. -The Transformer Encoder consists of two 'Transformer +The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5. -For training, TableFormer is trained with 3 Adam opti- +For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. -TableFormer is implemented with PyTorch and Torchvi- +TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. ## 5.2. Generalization -TableFormer is evaluated on three major publicly avail- +TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively. -We also share our baseline results on the challenging +We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized. ## 5.3. Datasets and Metrics -The Tree-Edit-Distance-Based Similarity (TEDS) met- +The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as: -where +where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T . ## 5.4. Quantitative Analysis -Structure. +Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. -Table 2: +| Model | Dataset | Simple | TEDS Complex | All | +|-------------|-----------|----------|----------------|-------| +| EDD | PTN | 91.1 | 88.7 | 89.9 | +| GTE | PTN | - | - | 93.01 | +| TableFormer | PTN | 98.5 | 95.0 | 96.75 | +| EDD | FTN | 88.4 | 92.08 | 90.6 | +| GTE | FTN | - | - | 87.14 | +| GTE (FT) | FTN | - | - | 91.02 | +| TableFormer | FTN | 97.5 | 96.0 | 96.8 | +| EDD | TB | 86.0 | - | 86 | +| TableFormer | TB | 89.6 | - | 89.6 | +| TableFormer | STN | 96.9 | 95.7 | 96.7 | + +Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). FT: Model was trained on PubTabNet then finetuned. -Cell Detection. +Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. -our +Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. -Table 3: +| Model | Dataset | mAP | mAP (PP) | +|-------------|-------------|-------|------------| +| EDD+BBox | PubTabNet | 79.2 | 82.7 | +| TableFormer | PubTabNet | 82.1 | 86.8 | +| TableFormer | SynthTabNet | 87.7 | - | -Cell Content. +Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. -Table 4: +Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. + +| Model | Simple | TEDS Complex | All | +|-------------|----------|----------------|-------| +| Tabula | 78 | 57.8 | 67.9 | +| Traprange | 60.8 | 49.9 | 55.4 | +| Camelot | 80 | 66 | 73 | +| Acrobat Pro | 68.9 | 61.8 | 65.3 | +| EDD | 91.2 | 85.4 | 88.3 | +| TableFormer | 95.4 | 90.1 | 93.6 | - a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells @@ -188,152 +230,173 @@ Table 4: - - -Figure 5: +b. Structure predicted by TableFormer, with superimposed matched PDF cell text: -Figure 6: +Text is aligned to match original for ease of viewing + +| | | 論文ファイル | 論文ファイル | 参考文献 | 参考文献 | +|----------------------------------------------------|-----------------|----------------|----------------|------------|------------| +| 出典 | ファイル数 英語 | | 日本語 | 英語 | 日本語 | +| Association for Computational Linguistics(ACL2003) | 65 | 65 | 0 | 150 | 0 | +| Computational Linguistics(COLING2002) | 140 | 140 | 0 | 150 | 0 | +| 電気情報通信学会 2003 年総合大会 | 150 | 8 | 142 | 223 | 147 | +| 情報処理学会第 65 回全国大会 (2003) | 177 | 1 | 176 | 150 | 236 | +| 第 17 回人工知能学会全国大会 (2003) | 208 | 5 | 203 | 152 | 244 | +| 自然言語処理研究会第 146 〜 155 回 | 98 | 2 | 96 | 150 | 232 | +| WWW から収集した論文 | 107 | 73 | 34 | 147 | 96 | +| 計 | 945 | 294 | 651 | 1122 | 955 | + +| | Shares (in millions) | Shares (in millions) | Weighted Average Grant Date Fair Value | Weighted Average Grant Date Fair Value | +|--------------------------|------------------------|------------------------|------------------------------------------|------------------------------------------| +| | RSUs | PSUs | RSUs | PSUs | +| Nonvested on January 1 | 1.1 | 0.3 | 90.10 $ | $ 91.19 | +| Granted | 0.5 | 0.1 | 117.44 | 122.41 | +| Vested | (0.5) | (0.1) | 87.08 | 81.14 | +| Canceled or forfeited | (0.1) | - | 102.01 | 92.18 | +| Nonvested on December 31 | 1.0 | 0.3 | 104.85 $ | $ 104.51 | + +Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table. + + + +Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset. + + ## 5.5. Qualitative Analysis -We showcase - - - ## 6. Future Work & Conclusion -In this paper, we presented TableFormer an end-to-end +We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type. + +In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1 -- [38] -- and evaluation. - -## TableFormer: Table Structure Understanding with Transformers +## TableFormer: Table Structure Understanding with Transformers Supplementary Material ## 1. Details on the datasets -ances in regard to their size, - ## 1.1. Data preparation -As a first step of our data preparation process, we have +As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length. -We have developed a technique that tries +We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. -Figure 7 illustrates the distribution of the tables across +Figure 7 illustrates the distribution of the tables across different dimensions per dataset. ## 1.2. Synthetic datasets -Aiming to train and evaluate our models in a broader +Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). -## 2. +The process of generating a synthetic dataset can be decomposed into the following steps: -The process of generating a synthetic dataset can be de- +- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). +- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. +- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. +- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table. +- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process. -- 1. -- 2. -- 3. Generate content: Based on the dataset -- 4. -- 5. +## 2. Prediction post-processing for PDF documents -Although TableFormer can predict the table structure and +Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons: -Figure 7: +Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity. -- • -- • +- · TableFormer output does not include the table cell content. +- · There are occasional inaccuracies in the predictions of the bounding boxes. -However, it is possible to mitigate those limitations by +dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. -Here is a step-by-step description of the prediction post- +However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes. -- 1. Get the minimal grid dimensions - number of rows and -- 2. -- 3. -- 3.a. -- 4. +Here is a step-by-step description of the prediction postprocessing: -where - -- 5. - -dian cell size for all table cells. - -- 6. -- 7. -- 8. -- 9. - -9a. - -- 9b. -- 9c. -- 9d. Intersect the orphan's bounding box with the column -- 9e. If the table cell under the identified row and column +- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure. +- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches. +- 3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones. +- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column. +- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: +where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point. + +- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me- +- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes. +- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells. +- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score. +- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan. + +9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row). + +- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row. +- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column). +- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column. +- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or- + phan cell. -9f. +9f. Otherwise create a new structural cell and match it wit the orphan cell. -Aditional images with examples of TableFormer predic- +Aditional images with examples of TableFormer predictions and post-processing can be found below. -Figure 9: +Figure 9: Example of a table with big empty distance between cells. -Figure 10: +Figure 10: Example of a complex table with empty cells. @@ -341,13 +404,11 @@ Figure 10: -Figure 13: +Figure 13: Table predictions example on colorful table. -Figure 14: - -Figure 15: +Figure 14: Example with multi-line text. @@ -357,12 +418,12 @@ Figure 15: -Figure 16: +Figure 15: Example with triangular table. -Figure 17: +Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json b/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json index b5d96fca..c74d2e11 100644 --- a/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json +++ b/tests/data/groundtruth/docling_v2/2203.01017v2.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 96.301, "r_y0": 119.93100000000004, "r_x1": 498.927, "r_y1": 119.93100000000004, "r_x2": 498.927, "r_y2": 107.03399999999999, "r_x3": 96.301, "r_y3": 107.03399999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers.", "orig": "TableFormer: Table Structure Understanding with Transformers.", "text_direction": "left_to_right", "confidence": 1.0, 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Graph's, etc,", "orig": "systems such as search engines, Knowledge Graph's, etc,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 285.849, "r_x1": 245.18, "r_y1": 285.849, "r_x2": 245.18, "r_y2": 277.26099999999997, "r_x3": 50.112, "r_y3": 277.26099999999997, "coord_origin": "TOPLEFT"}, "text": "since they enhance their predictive capabilities.", "orig": "since they enhance their predictive capabilities.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.496, "r_y0": 285.849, "r_x1": 286.365, "r_y1": 285.849, "r_x2": 286.365, "r_y2": 277.26099999999997, "r_x3": 251.496, "r_y3": 277.26099999999997, "coord_origin": "TOPLEFT"}, "text": "Unfortu-", "orig": "Unfortu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 297.804, "r_x1": 286.365, "r_y1": 297.804, "r_x2": 286.365, "r_y2": 289.216, "r_x3": 50.112, "r_y3": 289.216, "coord_origin": "TOPLEFT"}, "text": "nately, tables come in a large variety of shapes and sizes.", "orig": "nately, tables come in a large variety of shapes and sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 309.759, "r_x1": 103.601, "r_y1": 309.759, "r_x2": 103.601, "r_y2": 301.171, "r_x3": 50.112, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "Furthermore,", "orig": "Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.393, "r_y0": 309.759, "r_x1": 124.692, "r_y1": 309.759, "r_x2": 124.692, "r_y2": 301.171, "r_x3": 108.393, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "they", "orig": "they", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 129.026, "r_y0": 309.759, "r_x1": 286.365, "r_y1": 309.759, "r_x2": 286.365, "r_y2": 301.171, "r_x3": 129.026, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "can have complex column/row-header", "orig": "can have complex column/row-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 321.714, "r_x1": 286.365, "r_y1": 321.714, "r_x2": 286.365, "r_y2": 313.126, "r_x3": 50.112, "r_y3": 313.126, "coord_origin": "TOPLEFT"}, "text": "configurations, multiline rows, different variety of separa-", "orig": "configurations, multiline rows, different variety of separa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 333.669, "r_x1": 175.123, "r_y1": 333.669, "r_x2": 175.123, "r_y2": 325.081, "r_x3": 50.112, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "tion lines, missing entries, etc.", "orig": "tion lines, missing entries, etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.931, "r_y0": 333.669, "r_x1": 286.365, "r_y1": 333.669, "r_x2": 286.365, "r_y2": 325.081, "r_x3": 180.931, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "As such, the correct iden-", "orig": "As such, the correct iden-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 345.624, "r_x1": 85.539, "r_y1": 345.624, "r_x2": 85.539, "r_y2": 337.036, "r_x3": 50.112, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "tification", "orig": "tification", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 89.942, "r_y0": 345.624, "r_x1": 97.693, "r_y1": 345.624, "r_x2": 97.693, "r_y2": 337.036, "r_x3": 89.942, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.107, "r_y0": 345.624, "r_x1": 114.281, "r_y1": 345.624, "r_x2": 114.281, "r_y2": 337.036, "r_x3": 102.107, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 118.695, "r_y0": 345.624, "r_x1": 177.534, "r_y1": 345.624, "r_x2": 177.534, "r_y2": 337.036, "r_x3": 118.695, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.947, "r_y0": 345.624, "r_x1": 286.365, "r_y1": 345.624, "r_x2": 286.365, "r_y2": 337.036, "r_x3": 181.947, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "from an image is a non-", "orig": "from an image is a non-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 357.58, "r_x1": 95.791, "r_y1": 357.58, "r_x2": 95.791, "r_y2": 348.992, "r_x3": 50.112, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "trivial task.", "orig": "trivial task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, "r_x1": 132.443, "r_y1": 369.535, "r_x2": 132.443, "r_y2": 360.947, "r_x3": 50.112, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "identification model.", "orig": "identification model.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.777, "r_y0": 369.535, "r_x1": 286.365, "r_y1": 369.535, "r_x2": 286.365, "r_y2": 360.947, "r_x3": 136.777, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "The latter improves the latest end-to-", "orig": "The latter improves the latest end-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 381.49, "r_x1": 170.4, "r_y1": 381.49, "r_x2": 170.4, "r_y2": 372.902, "r_x3": 50.112, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "end deep learning model (i.e.", "orig": "end deep learning model (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 417.355, "r_x1": 286.365, "r_y1": 417.355, "r_x2": 286.365, "r_y2": 408.767, "r_x3": 50.112, "r_y3": 408.767, "coord_origin": "TOPLEFT"}, "text": "we can obtain the content of the table-cells from program-", "orig": "we can obtain the content of the table-cells from program-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 429.311, "r_x1": 286.365, "r_y1": 429.311, "r_x2": 286.365, "r_y2": 420.723, "r_x3": 50.112, "r_y3": 420.723, "coord_origin": "TOPLEFT"}, "text": "matic PDF's directly from the PDF source and avoid the", "orig": "matic PDF's directly from the PDF source and avoid the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 441.266, "r_x1": 207.232, "r_y1": 441.266, "r_x2": 207.232, "r_y2": 432.678, "r_x3": 50.112, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. Red-annotation of bounding boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.463, "r_y0": 333.15, "r_x1": 472.474, "r_y1": 333.15, "r_x2": 472.474, "r_y2": 324.837, "r_x3": 326.463, "r_y3": 324.837, "coord_origin": "TOPLEFT"}, "text": "Blue-predictions by TableFormer", "orig": "Blue-predictions by TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 428.838, "r_x1": 491.191, "r_y1": 428.838, "r_x2": 491.191, "r_y2": 420.525, "r_x3": 315.567, "r_y3": 420.525, "coord_origin": "TOPLEFT"}, "text": "c. Structure predicted by TableFormer:", "orig": "c. 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Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 62.067, "t": 561.052, "r": 166.266, "b": 569.604, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The occurrence of tables"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 584.678, "r": 358.077, "b": 593.23, "coord_origin": "TOPLEFT"}, "confidence": 0.9848759770393372, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 593.23, "r_x1": 358.077, "r_y1": 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204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.172, "r_x1": 286.365, "r_y1": 288.172, "r_x2": 286.365, "r_y2": 279.62, "r_x3": 50.112, "r_y3": 279.62, "coord_origin": "TOPLEFT"}, "text": "we can obtain the structure of any table, irregardless of the", "orig": "we can obtain the structure of any table, irregardless of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.128, "r_x1": 88.568, "r_y1": 300.128, "r_x2": 88.568, "r_y2": 291.576, "r_x3": 50.112, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "language.", "orig": "language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.502, "r_y0": 300.128, "r_x1": 286.365, "r_y1": 300.128, "r_x2": 286.365, "r_y2": 291.576, "r_x3": 95.502, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "Second, we want our algorithm to leverage as", "orig": "Second, we want our algorithm to leverage as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.083, "r_x1": 286.365, "r_y1": 312.083, "r_x2": 286.365, "r_y2": 303.531, "r_x3": 50.112, "r_y3": 303.531, "coord_origin": "TOPLEFT"}, "text": "much data as possible from the original PDF document. For", "orig": "much data as possible from the original PDF document. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.038, "r_x1": 286.365, "r_y1": 324.038, "r_x2": 286.365, "r_y2": 315.486, "r_x3": 50.112, "r_y3": 315.486, "coord_origin": "TOPLEFT"}, "text": "programmatic PDF documents, the text-cells can often be", "orig": "programmatic PDF documents, the text-cells can often be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.993, "r_x1": 286.365, "r_y1": 335.993, "r_x2": 286.365, "r_y2": 327.441, "r_x3": 50.112, "r_y3": 327.441, "coord_origin": "TOPLEFT"}, "text": "extracted much faster and with higher accuracy compared", "orig": "extracted much faster and with higher accuracy compared", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.948, "r_x1": 286.365, "r_y1": 347.948, "r_x2": 286.365, "r_y2": 339.396, "r_x3": 50.112, "r_y3": 339.396, "coord_origin": "TOPLEFT"}, "text": "to OCR methods. Last but not least, we want to have a di-", "orig": "to OCR methods. Last but not least, we want to have a di-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.904, "r_x1": 286.365, "r_y1": 359.904, "r_x2": 286.365, "r_y2": 351.352, "r_x3": 50.112, "r_y3": 351.352, "coord_origin": "TOPLEFT"}, "text": "rect link between the table-cell and its bounding box in the", "orig": "rect link between the table-cell and its bounding box in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.859, "r_x1": 76.951, "r_y1": 371.859, "r_x2": 76.951, "r_y2": 363.307, "r_x3": 50.112, "r_y3": 363.307, "coord_origin": "TOPLEFT"}, "text": "image.", "orig": "image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 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"r_y2": 399.625, "r_x3": 249.718, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "In partic-", "orig": "In partic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.132, "r_x1": 67.696, "r_y1": 420.132, "r_x2": 67.696, "r_y2": 411.58, "r_x3": 50.112, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "ular,", "orig": "ular,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.322, "r_y0": 420.132, "r_x1": 286.365, "r_y1": 420.132, "r_x2": 286.365, "r_y2": 411.58, "r_x3": 71.322, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "our contributions in this work can be summarised as", "orig": "our contributions in this work can be summarised as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.087, "r_x1": 82.52, "r_y1": 432.087, "r_x2": 82.52, "r_y2": 423.535, "r_x3": 50.112, "r_y3": 423.535, "coord_origin": "TOPLEFT"}, "text": "follows:", "orig": "follows:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 453.372, "r_x1": 65.056, "r_y1": 453.372, "r_x2": 65.056, "r_y2": 444.82, "r_x3": 61.569, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 453.372, "r_x1": 117.101, "r_y1": 453.372, "r_x2": 117.101, "r_y2": 444.82, "r_x3": 70.037, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "We propose", "orig": "We propose", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": 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structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.845, "r_y0": 342.39, "r_x1": 436.802, "r_y1": 342.39, "r_x2": 436.802, "r_y2": 333.838, "r_x3": 395.845, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "popularity", "orig": "popularity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.145, "r_y0": 342.39, "r_x1": 449.444, "r_y1": 342.39, "r_x2": 449.444, "r_y2": 333.838, "r_x3": 441.145, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.798, "r_y0": 342.39, "r_x1": 545.115, "r_y1": 342.39, "r_x2": 545.115, "r_y2": 333.838, "r_x3": 453.798, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "deep neural networks,", "orig": "deep neural networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 354.345, "r_x1": 545.115, "r_y1": 354.345, "r_x2": 545.115, "r_y2": 345.793, "r_x3": 308.862, "r_y3": 345.793, "coord_origin": "TOPLEFT"}, "text": "the community relied heavily on heuristic and/or statistical", "orig": "the community relied heavily on heuristic and/or statistical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 366.3, "r_x1": 545.115, "r_y1": 366.3, "r_x2": 545.115, "r_y2": 357.748, "r_x3": 308.862, "r_y3": 357.748, "coord_origin": "TOPLEFT"}, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "orig": "methods to do table structure identification [3, 7, 11, 5, 13,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 378.256, "r_x1": 324.633, "r_y1": 378.256, "r_x2": 324.633, "r_y2": 369.704, "r_x3": 308.862, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "28].", "orig": "28].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.634, "r_y0": 378.256, "r_x1": 545.115, "r_y1": 378.256, "r_x2": 545.115, "r_y2": 369.704, "r_x3": 329.634, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "Although such methods work well on constrained ta-", "orig": "Although such methods work well on constrained ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 390.211, "r_x1": 545.115, "r_y1": 390.211, "r_x2": 545.115, "r_y2": 381.659, "r_x3": 308.862, "r_y3": 381.659, "coord_origin": "TOPLEFT"}, "text": "bles [12], a more data-driven approach can be applied due", "orig": "bles [12], a more data-driven approach can be applied due", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 402.166, "r_x1": 545.115, "r_y1": 402.166, "r_x2": 545.115, "r_y2": 393.614, "r_x3": 308.862, "r_y3": 393.614, "coord_origin": "TOPLEFT"}, "text": "to the advent of convolutional neural networks (CNNs) and", "orig": "to the advent of convolutional neural networks (CNNs) and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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ar-", "orig": "edge, there are currently two different types of network ar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 438.032, "r_x1": 545.115, "r_y1": 438.032, "r_x2": 545.115, "r_y2": 429.48, "r_x3": 308.862, "r_y3": 429.48, "coord_origin": "TOPLEFT"}, "text": "chitecture that are being pursued for state-of-the-art table-", "orig": "chitecture that are being pursued for state-of-the-art table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 449.987, "r_x1": 401.285, "r_y1": 449.987, "r_x2": 401.285, "r_y2": 441.435, "r_x3": 308.862, "r_y3": 441.435, "coord_origin": "TOPLEFT"}, "text": "structure identification.", "orig": "structure identification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, 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"r_x2": 459.218, "r_y2": 489.321, "r_x3": 308.862, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "tags [37, 17] or LaTeX symbols[10].", "orig": "tags [37, 17] or LaTeX symbols[10].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.115, "r_y0": 497.873, "r_x1": 545.115, "r_y1": 497.873, "r_x2": 545.115, "r_y2": 489.321, "r_x3": 465.115, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "The choice of sym-", "orig": "The choice of sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.828, "r_x1": 545.115, "r_y1": 509.828, "r_x2": 545.115, "r_y2": 501.276, "r_x3": 308.862, "r_y3": 501.276, "coord_origin": "TOPLEFT"}, "text": "bols is ultimately not very important, since one can be trans-", "orig": "bols is ultimately not very important, since one can be trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.784, "r_x1": 396.692, "r_y1": 521.784, "r_x2": 396.692, "r_y2": 513.232, "r_x3": 308.862, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "formed into the other.", "orig": "formed into the other.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.265, "r_y0": 521.784, "r_x1": 545.115, "r_y1": 521.784, "r_x2": 545.115, "r_y2": 513.232, "r_x3": 401.265, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "There are however subtle variations", "orig": "There are however subtle variations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.739, "r_x1": 433.484, "r_y1": 533.739, "r_x2": 433.484, "r_y2": 525.187, "r_x3": 308.862, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "in the Image-to-Text networks.", "orig": "in the Image-to-Text networks.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 438.187, "r_y0": 533.739, "r_x1": 545.115, "r_y1": 533.739, "r_x2": 545.115, "r_y2": 525.187, "r_x3": 438.187, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "The easiest network archi-", "orig": "The easiest network archi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.694, "r_x1": 420.941, "r_y1": 545.694, "r_x2": 420.941, "r_y2": 537.142, "r_x3": 308.862, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "tectures are 'image-encoder", "orig": "tectures are 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.595, "r_y0": 545.388, "r_x1": 433.558, "r_y1": 545.388, "r_x2": 433.558, "r_y2": 536.8240000000001, "r_x3": 423.595, "r_y3": 536.8240000000001, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.212, "r_y0": 545.694, "r_x1": 545.113, "r_y1": 545.694, "r_x2": 545.113, "r_y2": 537.142, "r_x3": 436.212, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "text-decoder' (IETD), sim-", "orig": "text-decoder' (IETD), sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.649, "r_x1": 545.115, "r_y1": 557.649, "r_x2": 545.115, "r_y2": 549.097, "r_x3": 308.862, "r_y3": 549.097, "coord_origin": "TOPLEFT"}, "text": "ilar to network architectures that try to provide captions to", "orig": "ilar to network architectures that try to provide captions to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.604, "r_x1": 545.115, "r_y1": 569.604, "r_x2": 545.115, "r_y2": 561.052, "r_x3": 308.862, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "images [32]. In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.045, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 364.045, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "to generate the textual content of each table", "orig": "to generate the textual content of each table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 325.739, "r_y1": 701.111, "r_x2": 325.739, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.746, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 331.746, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "The network architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "considered as a solved problem, given enough ground-truth", "orig": "considered as a solved problem, given enough ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 99.84000000000003, "r": 286.365, "b": 108.39200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 142.49, "r": 545.115, "b": 151.04200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 231.79999999999995, "r": 286.365, "b": 240.35199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 333.838, "r": 391.492, "b": 342.39, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 375.715, "r": 286.365, "b": 384.267, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 61.569, "t": 444.82, "r": 65.056, "b": 453.372, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 453.372, "r_x1": 65.056, "r_y1": 453.372, "r_x2": 65.056, "r_y2": 444.82, "r_x3": 61.569, "r_y3": 444.82, 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As a conclusion, we de-"}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 99.84000000000003, "r": 286.365, "b": 108.39200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The second problem is called table-structure decompo-"}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 142.49, "r": 545.115, "b": 151.04200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Identifying the structure of a table has been an outstand-"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 231.79999999999995, "r": 286.365, "b": 240.35199999999998, "coord_origin": 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333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Before the rising"}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 375.715, "r": 286.365, "b": 384.267, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To meet the design criteria listed above, we developed a"}, 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As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we de-"}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 99.84000000000003, "r": 286.365, "b": 108.39200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The second problem is called table-structure decompo-"}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 142.49, "r": 545.115, "b": 151.04200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Identifying the structure of a table has been an outstand-"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 231.79999999999995, "r": 286.365, "b": 240.35199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we want to address these weaknesses and"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 333.838, "r": 391.492, "b": 342.39, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Before the rising"}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 375.715, "r": 286.365, "b": 384.267, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To meet the design criteria listed above, we developed a"}, 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table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.90599999999995, "r_x1": 286.365, "r_y1": 131.90599999999995, "r_x2": 286.365, "r_y2": 123.35400000000004, "r_x3": 50.112, "r_y3": 123.35400000000004, "coord_origin": "TOPLEFT"}, "text": "In the case of IETD, this OCR engine is implicit in the de-", "orig": "In the case of IETD, this OCR engine is implicit in the de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.861, "r_x1": 286.365, "r_y1": 143.861, "r_x2": 286.365, "r_y2": 135.30899999999997, "r_x3": 50.112, "r_y3": 135.30899999999997, "coord_origin": "TOPLEFT"}, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.849, "r_y0": 311.29, "r_x1": 286.365, "r_y1": 311.29, "r_x2": 286.365, "r_y2": 302.738, "r_x3": 138.849, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "Note that one table cell can consti-", "orig": "Note that one table cell can consti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.245, "r_x1": 286.365, "r_y1": 323.245, "r_x2": 286.365, "r_y2": 314.693, "r_x3": 50.112, "r_y3": 314.693, "coord_origin": "TOPLEFT"}, "text": "tute out of multiple text-cells. To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.11, "r_x1": 286.365, "r_y1": 359.11, "r_x2": 286.365, "r_y2": 350.558, "r_x3": 50.112, "r_y3": 350.558, "coord_origin": "TOPLEFT"}, "text": "node is then associated with en embedding vector coming", "orig": "node is then associated with en embedding vector coming", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.066, "r_x1": 286.365, "r_y1": 371.066, "r_x2": 286.365, "r_y2": 362.514, "r_x3": 50.112, "r_y3": 362.514, "coord_origin": "TOPLEFT"}, "text": "from the encoded image, its coordinates and the encoded", "orig": "from the encoded image, its coordinates and the encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.021, "r_x1": 67.397, "r_y1": 383.021, "r_x2": 67.397, "r_y2": 374.469, "r_x3": 50.112, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "text.", "orig": "text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 72.976, "r_y0": 383.021, "r_x1": 286.365, "r_y1": 383.021, "r_x2": 286.365, "r_y2": 374.469, "r_x3": 72.976, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "Furthermore, nodes that represent adjacent text-cells", "orig": "Furthermore, nodes that represent adjacent text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 394.976, "r_x1": 92.602, "r_y1": 394.976, "r_x2": 92.602, "r_y2": 386.424, "r_x3": 50.112, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "are linked.", "orig": "are linked.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.305, "r_y0": 394.976, "r_x1": 286.365, "r_y1": 394.976, "r_x2": 286.365, "r_y2": 386.424, "r_x3": 97.305, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "Graph Convolutional Networks (GCN's) based", "orig": "Graph Convolutional Networks (GCN's) based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 406.931, "r_x1": 286.365, "r_y1": 406.931, "r_x2": 286.365, "r_y2": 398.379, "r_x3": 50.112, "r_y3": 398.379, "coord_origin": "TOPLEFT"}, "text": "methods take the image as an input, but also the position of", "orig": "methods take the image as an input, but also the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 418.886, "r_x1": 286.365, "r_y1": 418.886, "r_x2": 286.365, "r_y2": 410.334, "r_x3": 50.112, "r_y3": 410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based 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"TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", 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"rule-sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.975, "r_y0": 574.36, "r_x1": 256.726, "r_y1": 574.36, "r_x2": 256.726, "r_y2": 565.808, "r_x3": 248.975, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.459, "r_y0": 574.36, "r_x1": 286.365, "r_y1": 574.36, "r_x2": 286.365, "r_y2": 565.808, "r_x3": 261.459, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "obtain", "orig": "obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.315, "r_x1": 122.64, "r_y1": 586.315, "r_x2": 122.64, "r_y2": 577.763, "r_x3": 50.112, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "its table-structure.", "orig": "its table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.482, "r_y0": 586.315, "r_x1": 286.365, "r_y1": 586.315, "r_x2": 286.365, "r_y2": 577.763, "r_x3": 127.482, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "Currently, this approach achieves state-", "orig": "Currently, this approach achieves state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 598.27, "r_x1": 87.731, "r_y1": 598.27, "r_x2": 87.731, "r_y2": 589.718, "r_x3": 50.112, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "of-the-art", "orig": "of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.074, "r_y0": 598.27, "r_x1": 120.577, "r_y1": 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"r_y1": 598.27, "r_x2": 165.987, "r_y2": 589.718, "r_x3": 153.255, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "not", "orig": "not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.331, "r_y0": 598.27, "r_x1": 286.365, "r_y1": 598.27, "r_x2": 286.365, "r_y2": 589.718, "r_x3": 170.331, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "an end-to-end deep-learning", "orig": "an end-to-end deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 610.225, "r_x1": 82.49, "r_y1": 610.225, "r_x2": 82.49, "r_y2": 601.673, "r_x3": 50.112, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.173, "r_y0": 610.225, "r_x1": 286.365, "r_y1": 610.225, "r_x2": 286.365, "r_y2": 601.673, "r_x3": 87.173, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "As such, new rules need to be written if different", "orig": "As such, new rules need to be written if different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 622.181, "r_x1": 175.989, "r_y1": 622.181, "r_x2": 175.989, "r_y2": 613.629, "r_x3": 50.112, "r_y3": 613.629, "coord_origin": "TOPLEFT"}, "text": "types of tables are encountered.", "orig": "types of tables are encountered.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 646.693, "r_x1": 105.225, "r_y1": 646.693, "r_x2": 105.225, "r_y2": 635.9449999999999, "r_x3": 50.112, "r_y3": 635.9449999999999, "coord_origin": "TOPLEFT"}, "text": "3. Datasets", "orig": "3. Datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 62.067, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "We rely on large-scale datasets such as PubTabNet [37],", "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "orig": "FinTabNet [36], and TableBank [17] datasets to train and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, 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"orig": "pearance styles and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.688, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 173.688, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "We also introduce our own", "orig": "We also introduce our own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "synthetically generated SynthTabNet dataset to fix an im-", "orig": "synthetically generated SynthTabNet dataset to fix an im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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datasets", "orig": "dimensions in PubTabNet + FinTabNet datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 326.294, "r_x1": 437.27, "r_y1": 326.294, "r_x2": 437.27, "r_y2": 317.742, "r_x3": 308.862, "r_y3": 317.742, "coord_origin": "TOPLEFT"}, "text": "balance in the previous datasets.", "orig": "balance in the previous datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 340.352, "r_x1": 545.115, "r_y1": 340.352, "r_x2": 545.115, "r_y2": 331.8, "r_x3": 320.817, "r_y3": 331.8, "coord_origin": "TOPLEFT"}, "text": "The PubTabNet dataset contains 509k tables delivered as", "orig": "The PubTabNet dataset contains 509k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, 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The annotations consist of the table", "orig": "annotated PNG images. The annotations consist of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 364.263, "r_x1": 545.115, "r_y1": 364.263, "r_x2": 545.115, "r_y2": 355.711, "r_x3": 308.862, "r_y3": 355.711, "coord_origin": "TOPLEFT"}, "text": "structure represented in HTML format, the tokenized text", "orig": "structure represented in HTML format, the tokenized text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.218, "r_x1": 545.115, "r_y1": 376.218, "r_x2": 545.115, "r_y2": 367.666, "r_x3": 308.862, "r_y3": 367.666, "coord_origin": "TOPLEFT"}, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "orig": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.173, "r_x1": 545.115, "r_y1": 388.173, "r_x2": 545.115, "r_y2": 379.621, "r_x3": 308.862, "r_y3": 379.621, "coord_origin": "TOPLEFT"}, "text": "pearance style of PubTabNet. Depending on its complexity,", "orig": "pearance style of PubTabNet. Depending on its complexity,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.128, "r_x1": 545.115, "r_y1": 400.128, "r_x2": 545.115, "r_y2": 391.576, "r_x3": 308.862, "r_y3": 391.576, "coord_origin": "TOPLEFT"}, "text": "a table is characterized as 'simple' when it does not contain", "orig": "a table is characterized as 'simple' when it does not contain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.083, "r_x1": 545.115, "r_y1": 412.083, "r_x2": 545.115, "r_y2": 403.531, "r_x3": 308.862, "r_y3": 403.531, "coord_origin": "TOPLEFT"}, "text": "row spans or column spans, otherwise it is 'complex'. The", "orig": "row spans or column spans, otherwise it is 'complex'. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.038, "r_x1": 545.115, "r_y1": 424.038, "r_x2": 545.115, "r_y2": 415.486, "r_x3": 308.862, "r_y3": 415.486, "coord_origin": "TOPLEFT"}, "text": "dataset is divided into Train and Val splits (roughly 98% and", "orig": "dataset is divided into Train and Val splits (roughly 98% and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.994, "r_x1": 545.115, "r_y1": 435.994, "r_x2": 545.115, "r_y2": 427.442, "r_x3": 308.862, "r_y3": 427.442, "coord_origin": "TOPLEFT"}, "text": "2%). The Train split consists of 54% simple and 46% com-", "orig": "2%). The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized 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"from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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Datasets", "orig": "3. 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Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 656.694, "r": 286.365, "b": 665.246, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 62.067, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "We rely on large-scale datasets such as PubTabNet [37],", "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37],"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "amount of such tables, and kept only those ones ranging", "orig": "amount of such tables, and kept only those ones ranging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 212.283, "r_y1": 95.98400000000004, "r_x2": 212.283, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "between 1*1 and 20*10 (rows/columns).", "orig": "between 1*1 and 20*10 (rows/columns).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 109.78099999999995, "r_x1": 286.365, "r_y1": 109.78099999999995, "r_x2": 286.365, "r_y2": 101.22900000000004, "r_x3": 62.067, "r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 121.73599999999999, "r_x1": 176.458, "r_y1": 121.73599999999999, "r_x2": 176.458, "r_y2": 113.18399999999997, "r_x3": 50.112, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "is essential to train our models.", "orig": "is essential to train our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.971, "r_y0": 121.73599999999999, "r_x1": 286.365, "r_y1": 121.73599999999999, "r_x2": 286.365, "r_y2": 113.18399999999997, "r_x3": 180.971, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "In order to distinguish be-", "orig": "In order to distinguish be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 133.69100000000003, "r_x1": 286.365, "r_y1": 133.69100000000003, "r_x2": 286.365, "r_y2": 125.13900000000001, "r_x3": 50.112, "r_y3": 125.13900000000001, "coord_origin": "TOPLEFT"}, "text": "tween empty and non-empty bounding boxes, we have in-", "orig": "tween empty and non-empty bounding boxes, we have in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 145.64700000000005, "r_x1": 286.365, "r_y1": 145.64700000000005, "r_x2": 286.365, "r_y2": 137.09500000000003, "r_x3": 50.112, "r_y3": 137.09500000000003, "coord_origin": "TOPLEFT"}, "text": "troduced a binary class in the annotation. Unfortunately, the", "orig": "troduced a binary class in the annotation. Unfortunately, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 157.60199999999998, "r_x1": 286.365, "r_y1": 157.60199999999998, "r_x2": 286.365, "r_y2": 149.04999999999995, "r_x3": 50.112, "r_y3": 149.04999999999995, "coord_origin": "TOPLEFT"}, "text": "original datasets either omit the bounding boxes for whole", "orig": "original datasets either omit the bounding boxes for whole", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 169.55700000000002, "r_x1": 94.127, "r_y1": 169.55700000000002, "r_x2": 94.127, "r_y2": 161.005, "r_x3": 50.112, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "tables (e.g.", "orig": "tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.959, "r_y0": 169.55700000000002, "r_x1": 286.365, "r_y1": 169.55700000000002, "r_x2": 286.365, "r_y2": 161.005, "r_x3": 98.959, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "TableBank) or they narrow their scope only to", "orig": "TableBank) or they narrow their scope only to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 181.51199999999994, "r_x1": 116.951, "r_y1": 181.51199999999994, "r_x2": 116.951, "r_y2": 172.96000000000004, "r_x3": 50.112, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "non-empty cells.", "orig": "non-empty cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 121.345, "r_y0": 181.51199999999994, "r_x1": 286.365, "r_y1": 181.51199999999994, "r_x2": 286.365, "r_y2": 172.96000000000004, "r_x3": 121.345, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Therefore, it was imperative to introduce", "orig": "Therefore, it was imperative to introduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 193.46699999999998, "r_x1": 286.365, "r_y1": 193.46699999999998, "r_x2": 286.365, "r_y2": 184.91499999999996, "r_x3": 50.112, "r_y3": 184.91499999999996, "coord_origin": "TOPLEFT"}, "text": "a data pre-processing procedure that generates the missing", "orig": "a data pre-processing procedure that generates the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 205.423, "r_x1": 286.365, "r_y1": 205.423, "r_x2": 286.365, "r_y2": 196.87099999999998, "r_x3": 50.112, "r_y3": 196.87099999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes out of the annotation information. This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, 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generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. The corpora to gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 50.112, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "ate the table text consists of the most frequent terms appear-", "orig": "ate the table text consists of the most frequent terms appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "ing in PubTabNet and FinTabNet together with randomly", "orig": "ing in PubTabNet and FinTabNet together with randomly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 109.519, "r_y1": 689.156, "r_x2": 109.519, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "generated text.", "orig": "generated text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.337, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 115.337, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "The first two synthetic datasets have been", "orig": "The first two synthetic datasets have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "fine-tuned to mimic the appearance of the original datasets", "orig": "fine-tuned to mimic the appearance of the original datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "but encompass more complicated table structures. The third", "orig": "but encompass more complicated table structures. 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545.115, "r_y2": 249.889, "r_x3": 308.862, "r_y3": 249.889, "coord_origin": "TOPLEFT"}, "text": "one adopts a colorful appearance with high contrast and the", "orig": "one adopts a colorful appearance with high contrast and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.39599999999996, "r_x1": 545.115, "r_y1": 270.39599999999996, "r_x2": 545.115, "r_y2": 261.84400000000005, "r_x3": 308.862, "r_y3": 261.84400000000005, "coord_origin": "TOPLEFT"}, "text": "last one contains tables with sparse content. Lastly, we have", "orig": "last one contains tables with sparse content. Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 319.153, "r_y0": 497.541, "r_x1": 509.021, "r_y1": 497.541, "r_x2": 509.021, "r_y2": 488.989, "r_x3": 319.153, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "composed of three main components, see Fig.", "orig": "composed of three main components, see Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 515.586, "r_y0": 497.541, "r_x1": 523.058, "r_y1": 497.541, "r_x2": 523.058, "r_y2": 488.989, "r_x3": 515.586, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 497.541, "r_x1": 545.115, "r_y1": 497.541, "r_x2": 545.115, "r_y2": 488.989, "r_x3": 529.623, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.452, "r_x1": 319.93, "r_y1": 521.452, "r_x2": 319.93, "r_y2": 512.9, "r_x3": 308.862, "r_y3": 512.9, "coord_origin": "TOPLEFT"}, "text": "tor", "orig": "tor", "text_direction": 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As for spanning cells, such as row", "orig": "the Cell BBox Decoder. As for spanning cells, such as row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.183, "r_x1": 483.118, "r_y1": 593.183, "r_x2": 483.118, "r_y2": 584.631, "r_x3": 308.862, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "or column span, the tag is broken down to '", "orig": "or column span, the tag is broken down to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.119, "r_y0": 593.019, "r_x1": 490.868, "r_y1": 593.019, "r_x2": 490.868, "r_y2": 584.313, "r_x3": 483.119, "r_y3": 584.313, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.868, "r_y0": 593.183, "r_x1": 545.114, "r_y1": 593.183, "r_x2": 545.114, "r_y2": 584.631, "r_x3": 490.868, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "', 'rowspan='", "orig": "', 'rowspan='", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.138, "r_x1": 545.115, "r_y1": 605.138, "r_x2": 545.115, "r_y2": 596.586, "r_x3": 308.862, "r_y3": 596.586, "coord_origin": "TOPLEFT"}, "text": "or 'colspan=', with the number of spanning cells (attribute),", "orig": "or 'colspan=', with the number of spanning cells (attribute),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.093, "r_x1": 329.644, "r_y1": 617.093, "r_x2": 329.644, "r_y2": 608.5409999999999, "r_x3": 308.862, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "and '", "orig": "and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.646, "r_y0": 616.929, "r_x1": 337.395, "r_y1": 616.929, "r_x2": 337.395, "r_y2": 608.223, "r_x3": 329.646, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.394, "r_y0": 617.093, "r_x1": 343.202, "r_y1": 617.093, "r_x2": 343.202, "r_y2": 608.5409999999999, "r_x3": 337.394, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "'.", "orig": "'.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.064, "r_y0": 617.093, "r_x1": 468.591, "r_y1": 617.093, "r_x2": 468.591, "r_y2": 608.5409999999999, "r_x3": 348.064, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "The hidden state attached to '", "orig": "The hidden state attached to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 468.595, "r_y0": 616.929, "r_x1": 476.344, "r_y1": 616.929, "r_x2": 476.344, "r_y2": 608.223, "r_x3": 468.595, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.344, "r_y0": 617.093, "r_x1": 545.116, "r_y1": 617.093, "r_x2": 545.116, "r_y2": 608.5409999999999, "r_x3": 476.344, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "' is passed to the", "orig": "' is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.048, "r_x1": 545.115, "r_y1": 629.048, "r_x2": 545.115, "r_y2": 620.496, "r_x3": 308.862, "r_y3": 620.496, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "orig": "Cell BBox Decoder. A shared feed forward network (FFN)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.0029999999999, "r_x1": 533.2, "r_y1": 641.0029999999999, "r_x2": 533.2, "r_y2": 632.451, "r_x3": 308.862, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "receives the hidden states from the Structure Decoder,", "orig": "receives the hidden states from the Structure Decoder,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.364, "r_y0": 641.0029999999999, "r_x1": 545.115, "r_y1": 641.0029999999999, "r_x2": 545.115, "r_y2": 632.451, "r_x3": 537.364, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.9590000000001, "r_x1": 545.115, "r_y1": 652.9590000000001, "r_x2": 545.115, "r_y2": 644.407, "r_x3": 308.862, "r_y3": 644.407, "coord_origin": "TOPLEFT"}, "text": "provide the final detection predictions of the bounding box", "orig": "provide the final detection predictions of the bounding box", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.914, "r_x1": 449.424, "r_y1": 664.914, "r_x2": 449.424, "r_y2": 656.362, "r_x3": 308.862, "r_y3": 656.362, "coord_origin": "TOPLEFT"}, "text": "coordinates and their classification.", "orig": "coordinates and their classification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.217, "r_x1": 431.91, "r_y1": 677.217, "r_x2": 431.91, "r_y2": 668.261, "r_x3": 320.817, "r_y3": 668.261, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network.", "orig": "CNN Backbone Network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.499, "r_y0": 677.201, "r_x1": 545.113, "r_y1": 677.201, "r_x2": 545.113, "r_y2": 668.649, "r_x3": 439.499, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "A ResNet-18 CNN is the", "orig": "A ResNet-18 CNN is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "backbone that receives the table image and encodes it as a", "orig": "backbone that receives the table image and encodes it as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "vector of predefined length. The network has been modified", "orig": "vector of predefined length. The network has been modified", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "by removing the linear and pooling layer, as we are not per-", "orig": "by removing the linear and pooling layer, as we are not per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, 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"r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 308.862, "t": 167.92999999999995, "r": 330.75, "b": 176.48199999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9667726755142212, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 176.48199999999997, "r_x1": 330.75, "r_y1": 176.48199999999997, "r_x2": 330.75, "r_y2": 167.92999999999995, "r_x3": 308.862, "r_y3": 167.92999999999995, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.889, "r": 545.115, "b": 258.44100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.978954017162323, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.44100000000003, "r_x1": 545.115, "r_y1": 258.44100000000003, "r_x2": 545.115, "r_y2": 249.889, "r_x3": 308.862, "r_y3": 249.889, "coord_origin": "TOPLEFT"}, "text": "one adopts a colorful appearance with high contrast and the", "orig": "one adopts a colorful appearance with high contrast and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 320.817, "t": 298.041, "r": 542.744, "b": 306.593, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 318.264, "r": 286.365, "b": 326.816, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": -1, "rgba": 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The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 342.208, "r": 545.115, "b": 350.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 439.658, "r": 286.365, "b": 448.21, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 477.034, "r": 545.115, "b": 485.586, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 62.067, "t": 632.783, "r": 286.365, "b": 641.335, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 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various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 318.264, "r": 286.365, "b": 326.816, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As it is illustrated in Fig. 2, the table 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The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 342.208, "r": 545.115, "b": 350.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Given the image of a table, TableFormer is able to pre-"}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 439.658, "r": 286.365, "b": 448.21, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Motivated by those observations we aimed at generating"}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 477.034, "r": 545.115, "b": 485.586, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We now describe in detail the proposed method, which"}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 62.067, "t": 632.783, "r": 286.365, "b": 641.335, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": -1, "rgba": {"r": 0, 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summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 318.264, "r": 286.365, "b": 326.816, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from"}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. 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As our model utilizes a transformer architecture, the", "orig": "tions). 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Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": 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The FFN consists", "orig": "network (FFN). 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As for the", "orig": "structure tokens. As for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 161.318, "r_y0": 288.585, "r_x1": 238.797, "r_y1": 288.585, "r_x2": 238.797, "r_y2": 279.99699999999996, "r_x3": 161.318, "r_y3": 279.99699999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.521, "r_y0": 288.728, "r_x1": 286.363, "r_y1": 288.728, "r_x2": 286.363, "r_y2": 280.176, "r_x3": 241.521, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "it is trained", "orig": "it is trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.683, "r_x1": 211.377, "r_y1": 300.683, "r_x2": 211.377, "r_y2": 292.131, 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In", "orig": ") to be scale invariant as explained in [25]. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.549, "r_x1": 286.365, "r_y1": 336.549, "r_x2": 286.365, "r_y2": 327.997, "r_x3": 50.112, "r_y3": 327.997, "coord_origin": "TOPLEFT"}, "text": "comparison to DETR, we do not use the Hungarian algo-", "orig": "comparison to DETR, we do not use the Hungarian algo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.504, "r_x1": 286.365, "r_y1": 348.504, "r_x2": 286.365, "r_y2": 339.952, "r_x3": 50.112, "r_y3": 339.952, "coord_origin": "TOPLEFT"}, "text": "rithm [15] to match the predicted bounding boxes with the", "orig": "rithm [15] to match the predicted bounding boxes with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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Additionally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 640.421, "r_x1": 207.033, "r_y1": 640.421, "r_x2": 207.033, "r_y2": 631.869, "r_x3": 50.112, "r_y3": 631.869, "coord_origin": "TOPLEFT"}, "text": "enforce the following input constraints:", "orig": "enforce the following input constraints:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.661, "r_y0": 663.366, "r_x1": 186.017, "r_y1": 663.366, "r_x2": 186.017, "r_y2": 654.814, "r_x3": 91.661, "r_y3": 654.814, "coord_origin": "TOPLEFT"}, "text": "Image width and height", "orig": "Image width and height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.507, "r_y0": 663.06, "r_x1": 196.256, "r_y1": 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0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "such as EDD, ours are less restrictive due to the improved", "orig": "such as EDD, ours are less restrictive due to the improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, 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As for the", "orig": "forward network of 1024, and 4 attention heads. 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545.115, "r_y2": 236.04899999999998, "r_x3": 308.862, "r_y3": 236.04899999999998, "coord_origin": "TOPLEFT"}, "text": "ages, which contain mostly lines and text, unlike the more", "orig": "ages, which contain mostly lines and text, unlike the more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.557, "r_x1": 496.169, "r_y1": 256.557, "r_x2": 496.169, "r_y2": 248.005, "r_x3": 308.862, "r_y3": 248.005, "coord_origin": "TOPLEFT"}, "text": "elaborate content present in other scopes (e.g.", "orig": "elaborate content present in other scopes (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 501.897, "r_y0": 256.557, "r_x1": 545.115, "r_y1": 256.557, "r_x2": 545.115, "r_y2": 248.005, "r_x3": 501.897, "r_y3": 248.005, "coord_origin": "TOPLEFT"}, "text": "the COCO", "orig": "the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.51199999999994, "r_x1": 342.336, "r_y1": 268.51199999999994, "r_x2": 342.336, "r_y2": 259.96000000000004, "r_x3": 308.862, "r_y3": 259.96000000000004, "coord_origin": "TOPLEFT"}, "text": "dataset).", "orig": "dataset).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.952, "r_y0": 268.51199999999994, "r_x1": 545.115, "r_y1": 268.51199999999994, "r_x2": 545.115, "r_y2": 259.96000000000004, "r_x3": 348.952, "r_y3": 259.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Moreover, we have added ResNet blocks to the", "orig": "Moreover, we have added ResNet blocks to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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545.115, "r_y2": 295.825, "r_x3": 308.862, "r_y3": 295.825, "coord_origin": "TOPLEFT"}, "text": "learned weights which would damage the other prediction", "orig": "learned weights which would damage the other prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.332, "r_x1": 545.115, "r_y1": 316.332, "r_x2": 545.115, "r_y2": 307.78, "r_x3": 308.862, "r_y3": 307.78, "coord_origin": "TOPLEFT"}, "text": "task (structure vs bounding boxes), but learn task specific", "orig": "task (structure vs bounding boxes), but learn task specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.288, "r_x1": 532.483, "r_y1": 328.288, "r_x2": 532.483, "r_y2": 319.736, "r_x3": 308.862, "r_y3": 319.736, "coord_origin": "TOPLEFT"}, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "orig": "weights instead. Lastly our dropout layers are set to 0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 357.353, "r_x1": 403.736, "r_y1": 357.353, "r_x2": 403.736, "r_y2": 348.801, "r_x3": 308.862, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": "mizers, each one for the", "orig": "mizers, each one for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.076, "r_y0": 357.21, "r_x1": 545.112, "r_y1": 357.21, "r_x2": 545.112, "r_y2": 348.622, "r_x3": 406.076, "r_y3": 348.622, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network Structure", "orig": "CNN Backbone Network Structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 503.539, "r_y0": 357.353, "r_x1": 506.03, "r_y1": 357.353, "r_x2": 506.03, "r_y2": 348.801, "r_x3": 503.539, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 369.165, "r_x1": 343.163, "r_y1": 369.165, "r_x2": 343.163, "r_y2": 360.577, "r_x3": 308.862, "r_y3": 360.577, "coord_origin": "TOPLEFT"}, "text": "Decoder", "orig": "Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 343.163, "r_y0": 369.308, "r_x1": 362.202, "r_y1": 369.308, "r_x2": 362.202, "r_y2": 360.756, "r_x3": 343.163, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.286, "r_y0": 369.165, "r_x1": 440.938, "r_y1": 369.165, "r_x2": 440.938, "r_y2": 360.577, "r_x3": 364.286, "r_y3": 360.577, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.939, "r_y0": 369.308, "r_x1": 443.43, "r_y1": 369.308, "r_x2": 443.43, "r_y2": 360.756, "r_x3": 440.939, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.379, "r_y0": 369.308, "r_x1": 545.108, "r_y1": 369.308, "r_x2": 545.108, "r_y2": 360.756, "r_x3": 446.379, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": "Taking the PubTabNet as", "orig": "Taking the PubTabNet as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 381.263, "r_x1": 545.115, "r_y1": 381.263, "r_x2": 545.115, "r_y2": 372.711, "r_x3": 308.862, "r_y3": 372.711, "coord_origin": "TOPLEFT"}, "text": "an example for our parameter set up, the initializing learn-", "orig": "an example for our parameter set up, the initializing learn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 393.219, "r_x1": 545.115, "r_y1": 393.219, "r_x2": 545.115, "r_y2": 384.667, "r_x3": 308.862, "r_y3": 384.667, "coord_origin": "TOPLEFT"}, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "orig": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 405.01, "r_x1": 314.673, "r_y1": 405.01, "r_x2": 314.673, "r_y2": 396.304, "r_x3": 308.862, "r_y3": 396.304, "coord_origin": "TOPLEFT"}, "text": "\u03bb", "orig": "\u03bb", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.658, "r_y0": 405.174, "r_x1": 329.726, "r_y1": 405.174, "r_x2": 329.726, "r_y2": 396.622, "r_x3": 318.658, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.711, "r_y0": 405.174, "r_x1": 341.462, "r_y1": 405.174, "r_x2": 341.462, "r_y2": 396.622, "r_x3": 333.711, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.447, "r_y0": 405.174, "r_x1": 360.391, "r_y1": 405.174, "r_x2": 360.391, "r_y2": 396.622, "r_x3": 345.447, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "0.5.", "orig": "0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.963, "r_y0": 405.174, "r_x1": 415.166, "r_y1": 405.174, "r_x2": 415.166, "r_y2": 396.622, "r_x3": 367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 429.084, "r_x1": 360.966, "r_y1": 429.084, "r_x2": 360.966, "r_y2": 420.532, "r_x3": 308.862, "r_y3": 420.532, "coord_origin": "TOPLEFT"}, "text": "convergence.", "orig": "convergence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, "r_y3": 461.553, "coord_origin": "TOPLEFT"}, "text": "undergoes a single forward pass through the", "orig": "undergoes a single forward pass through the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.078, "r_y0": 469.962, "r_x1": 545.111, "r_y1": 469.962, "r_x2": 545.111, "r_y2": 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This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 624.225, "r_x1": 545.115, "r_y1": 624.225, "r_x2": 545.115, "r_y2": 615.673, "r_x3": 308.862, "r_y3": 615.673, "coord_origin": "TOPLEFT"}, "text": "able datasets of different nature to prove the generalization", "orig": "able datasets of different nature to prove the generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 636.1800000000001, "r_x1": 545.115, "r_y1": 636.1800000000001, "r_x2": 545.115, "r_y2": 627.6279999999999, "r_x3": 308.862, "r_y3": 627.6279999999999, "coord_origin": "TOPLEFT"}, "text": "and effectiveness of our model. The datasets used for eval-", "orig": "and effectiveness of our model. The datasets used for eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 648.135, "r_x1": 545.115, "r_y1": 648.135, "r_x2": 545.115, "r_y2": 639.583, "r_x3": 308.862, "r_y3": 639.583, "coord_origin": "TOPLEFT"}, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "orig": "uation are the PubTabNet, FinTabNet and TableBank which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 660.091, "r_x1": 545.115, "r_y1": 660.091, "r_x2": 545.115, "r_y2": 651.539, "r_x3": 308.862, "r_y3": 651.539, "coord_origin": "TOPLEFT"}, "text": "stem from the scientific, financial and general domains re-", "orig": "stem from the scientific, financial and general domains re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.057, "r_y0": 701.111, "r_x1": 396.214, "r_y1": 701.111, "r_x2": 396.214, "r_y2": 692.559, "r_x3": 366.057, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.406, "r_y0": 701.111, "r_x1": 453.449, "r_y1": 701.111, "r_x2": 453.449, "r_y2": 692.559, "r_x3": 406.406, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Throughout", "orig": "Throughout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.311, "r_y0": 701.111, "r_x1": 471.591, "r_y1": 701.111, "r_x2": 471.591, "r_y2": 692.559, 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495.94, "r_y2": 704.515, "r_x3": 308.862, "r_y3": 704.515, "coord_origin": "TOPLEFT"}, "text": "same parameters stated in Sec. 5.1 are utilized.", "orig": "same parameters stated in Sec. 5.1 are utilized.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "runtime performance and lower memory footprint of Table-", "orig": "runtime performance and lower memory footprint of Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 116.49800000000005, "r": 545.115, "b": 125.04999999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9880930781364441, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 125.04999999999995, "r_x1": 545.115, "r_y1": 125.04999999999995, "r_x2": 545.115, "r_y2": 116.49800000000005, "r_x3": 320.817, "r_y3": 116.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The Transformer Encoder consists of two 'Transformer", "orig": "The Transformer Encoder consists of two 'Transformer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 62.067, "t": 159.89300000000003, "r": 108.154, "b": 168.44500000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 168.44500000000005, "r_x1": 108.154, "r_y1": 168.44500000000005, "r_x2": 108.154, "r_y2": 159.89300000000003, "r_x3": 62.067, "r_y3": 159.89300000000003, "coord_origin": "TOPLEFT"}, "text": "The output", "orig": "The output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 243.92200000000003, "r": 129.215, "b": 252.87800000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 320.817, "t": 336.846, "r": 545.115, "b": 345.398, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 437.643, "r": 545.115, "b": 446.195, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with 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Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 596.0029999999999, "r": 202.978, "b": 604.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 604.5550000000001, "r_x1": 202.978, "r_y1": 604.5550000000001, "r_x2": 202.978, "r_y2": 596.0029999999999, "r_x3": 62.067, "r_y3": 596.0029999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer uses ResNet-18 as the", "orig": "TableFormer uses ResNet-18 as the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 603.718, "r": 545.115, "b": 612.27, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 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"orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 84.029, 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Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 596.0029999999999, "r": 202.978, "b": 604.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 604.5550000000001, "r_x1": 202.978, "r_y1": 604.5550000000001, "r_x2": 202.978, "r_y2": 596.0029999999999, "r_x3": 62.067, "r_y3": 596.0029999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer uses ResNet-18 as the", "orig": "TableFormer uses ResNet-18 as the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer uses ResNet-18 as the"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 603.718, "r": 545.115, "b": 612.27, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": -1, "rgba": {"r": 0, "g": 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286.365, "b": 701.111, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although input constraints are used also by other methods,"}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, 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encoded image to"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "runtime performance and lower memory footprint of Table-", "orig": "runtime performance and lower memory footprint of Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "runtime performance and lower memory footprint of Table-"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 116.49800000000005, "r": 545.115, "b": 125.04999999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9880930781364441, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 125.04999999999995, "r_x1": 545.115, "r_y1": 125.04999999999995, "r_x2": 545.115, "r_y2": 116.49800000000005, "r_x3": 320.817, "r_y3": 116.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The Transformer Encoder consists of two 'Transformer", "orig": "The Transformer Encoder consists of two 'Transformer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The Transformer Encoder consists of two 'Transformer"}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 62.067, "t": 159.89300000000003, "r": 108.154, "b": 168.44500000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 168.44500000000005, "r_x1": 108.154, "r_y1": 168.44500000000005, "r_x2": 108.154, "r_y2": 159.89300000000003, "r_x3": 62.067, "r_y3": 159.89300000000003, "coord_origin": "TOPLEFT"}, "text": "The output", "orig": "The output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The output"}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 62.067, "t": 243.92200000000003, "r": 129.215, "b": 252.87800000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Loss Functions."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.817, "t": 336.846, "r": 545.115, "b": 345.398, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam opti-"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 437.643, "r": 545.115, "b": 446.195, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvi-"}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 62.067, "t": 448.279, "r": 286.365, "b": 456.831, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 456.831, "r_x1": 286.365, "r_y1": 456.831, "r_x2": 286.365, "r_y2": 448.279, "r_x3": 62.067, "r_y3": 448.279, "coord_origin": "TOPLEFT"}, "text": "The loss used to train the TableFormer can be defined as", "orig": "The loss used to train the TableFormer can be defined as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The loss used to train the TableFormer can be defined as"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 125.715, "t": 493.391, "r": 128.688, "b": 502.097, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.715, "r_y0": 502.097, "r_x1": 128.688, "r_y1": 502.097, "r_x2": 128.688, "r_y2": 493.391, "r_x3": 125.715, "r_y3": 493.391, "coord_origin": "TOPLEFT"}, "text": "l", "orig": "l", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "l"}, {"label": "text", "id": 16, "page_no": 5, "cluster": {"id": 16, "label": "text", "bbox": {"l": 50.112, "t": 531.578, "r": 74.451, "b": 540.13, "coord_origin": "TOPLEFT"}, "confidence": 0.9363929629325867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 540.13, "r_x1": 74.451, "r_y1": 540.13, "r_x2": 74.451, "r_y2": 531.578, "r_x3": 50.112, "r_y3": 531.578, "coord_origin": "TOPLEFT"}, "text": "where", "orig": "where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "where"}, {"label": "section_header", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 555.917, "r": 171.983, "b": 566.665, "coord_origin": "TOPLEFT"}, "confidence": 0.9554555416107178, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 566.665, "r_x1": 171.983, "r_y1": 566.665, "r_x2": 171.983, "r_y2": 555.917, "r_x3": 50.112, "r_y3": 555.917, "coord_origin": "TOPLEFT"}, "text": "5. Experimental Results", "orig": "5. Experimental Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Experimental Results"}, {"label": "section_header", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 576.264, "r": 179.175, "b": 586.116, "coord_origin": "TOPLEFT"}, "confidence": 0.9538503289222717, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.116, "r_x1": 179.175, "r_y1": 586.116, "r_x2": 179.175, "r_y2": 576.264, "r_x3": 50.112, "r_y3": 576.264, "coord_origin": "TOPLEFT"}, "text": "5.1. Implementation Details", "orig": "5.1. Implementation Details", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1. Implementation Details"}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.862, "t": 579.554, "r": 397.443, "b": 589.406, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 62.067, "t": 596.0029999999999, "r": 202.978, "b": 604.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 604.5550000000001, "r_x1": 202.978, "r_y1": 604.5550000000001, "r_x2": 202.978, "r_y2": 596.0029999999999, "r_x3": 62.067, "r_y3": 596.0029999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer uses ResNet-18 as the", "orig": "TableFormer uses ResNet-18 as the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer uses ResNet-18 as the"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 320.817, "t": 603.718, "r": 545.115, "b": 612.27, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is evaluated on three major publicly avail-"}, {"label": "formula", "id": 18, "page_no": 5, "cluster": {"id": 18, "label": "formula", "bbox": {"l": 91.661, "t": 654.814, "r": 186.017, "b": 663.366, "coord_origin": "TOPLEFT"}, "confidence": 0.844183623790741, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.661, "r_y0": 663.366, "r_x1": 186.017, "r_y1": 663.366, "r_x2": 186.017, "r_y2": 654.814, "r_x3": 91.661, "r_y3": 654.814, "coord_origin": "TOPLEFT"}, "text": "Image width and height", "orig": "Image width and height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Image width and height"}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 320.817, "t": 680.604, "r": 545.115, "b": 689.156, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 320.817, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "We also share our baseline results on the challenging", "orig": "We also share our baseline results on the challenging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We also share our baseline results on the challenging"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 701.111, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although input constraints are used also by other methods,"}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.25300000000004, "r_x1": 167.898, "r_y1": 84.25300000000004, "r_x2": 167.898, "r_y2": 74.40099999999995, "r_x3": 50.112, "r_y3": 74.40099999999995, "coord_origin": "TOPLEFT"}, "text": "5.3. 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"r_x3": 50.112, "r_y3": 279.903, "coord_origin": "TOPLEFT"}, "text": "All the more, our model outperforms pre-trained methods.", "orig": "All the more, our model outperforms pre-trained methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.411, "r_x1": 286.365, "r_y1": 300.411, "r_x2": 286.365, "r_y2": 291.859, "r_x3": 50.112, "r_y3": 291.859, "coord_origin": "TOPLEFT"}, "text": "During the evaluation we do not apply any table filtering.", "orig": "During the evaluation we do not apply any table filtering.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.366, "r_x1": 286.365, "r_y1": 312.366, "r_x2": 286.365, "r_y2": 303.814, "r_x3": 50.112, "r_y3": 303.814, "coord_origin": "TOPLEFT"}, "text": "We also provide our baseline results on the SynthTabNet", "orig": "We also provide our baseline results on the SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.321, "r_x1": 80.269, "r_y1": 324.321, "r_x2": 80.269, "r_y2": 315.769, "r_x3": 50.112, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 85.579, "r_y0": 324.321, "r_x1": 91.666, "r_y1": 324.321, "r_x2": 91.666, "r_y2": 315.769, "r_x3": 85.579, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "It", "orig": "It", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 94.894, "r_y0": 324.321, "r_x1": 257.812, "r_y1": 324.321, "r_x2": 257.812, "r_y2": 315.769, "r_x3": 94.894, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "has been observed that large tables (e.g.", "orig": "has been observed that large tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.122, "r_y0": 324.321, "r_x1": 286.365, "r_y1": 324.321, "r_x2": 286.365, "r_y2": 315.769, "r_x3": 263.122, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "tables", "orig": "tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.276, "r_x1": 286.365, "r_y1": 336.276, "r_x2": 286.365, "r_y2": 327.724, "r_x3": 50.112, "r_y3": 327.724, "coord_origin": "TOPLEFT"}, "text": "that occupy half of the page or more) yield poor predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.203, "r_y0": 360.186, "r_x1": 134.147, "r_y1": 360.186, "r_x2": 134.147, "r_y2": 351.634, "r_x3": 119.203, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "that", "orig": "that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.952, "r_y0": 360.186, "r_x1": 286.365, "r_y1": 360.186, "r_x2": 286.365, "r_y2": 351.634, "r_x3": 137.952, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "produces downsampled images with", "orig": "produces downsampled images with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.142, "r_x1": 155.357, "r_y1": 372.142, "r_x2": 155.357, "r_y2": 363.59, "r_x3": 50.112, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "indistinguishable features.", "orig": "indistinguishable features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 396.052, "r_x1": 170.012, "r_y1": 396.052, "r_x2": 170.012, "r_y2": 387.5, "r_x3": 50.112, "r_y3": 387.5, "coord_origin": 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We make use of the", "orig": "with post-processing during inference. We make use of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 259.655, "r_y1": 689.156, "r_x2": 259.655, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "grid-like structure of tables to refine the predictions.", "orig": "grid-like structure of tables to refine the predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.65, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 263.65, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "A de-", "orig": "A de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.094, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 228.094, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "3, we evaluate", "orig": "3, we evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 322.142, "r_y1": 84.029, "r_x2": 322.142, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 325.454, "r_y0": 83.88599999999997, "r_x1": 404.567, "r_y1": 83.88599999999997, "r_x2": 404.567, "r_y2": 75.298, "r_x3": 325.454, "r_y3": 75.298, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.104, "r_y0": 84.029, "r_x1": 545.11, "r_y1": 84.029, "r_x2": 545.11, "r_y2": 75.47699999999998, "r_x3": 408.104, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "accuracy for cells with a class la-", "orig": "accuracy for cells with a class la-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "bel of 'content' only using the PASCAL VOC mAP metric", "orig": "bel of 'content' only using the PASCAL VOC mAP metric", 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"r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "are only provided. To compare the performance of our pro-", "orig": "are only provided. To compare the performance of our pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 143.80499999999995, "r_x1": 502.017, "r_y1": 143.80499999999995, "r_x2": 502.017, "r_y2": 135.25300000000004, "r_x3": 308.862, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "posed approach, we've integrated TableFormer's", "orig": "posed approach, we've integrated TableFormer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 504.473, "r_y0": 143.66200000000003, "r_x1": 545.11, "r_y1": 143.66200000000003, "r_x2": 545.11, "r_y2": 135.07399999999996, "r_x3": 504.473, "r_y3": 135.07399999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox", "orig": "Cell BBox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 155.61699999999996, "r_x1": 343.163, "r_y1": 155.61699999999996, "r_x2": 343.163, "r_y2": 147.029, "r_x3": 308.862, "r_y3": 147.029, "coord_origin": "TOPLEFT"}, "text": "Decoder", "orig": "Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.371, "r_y0": 155.76, "r_x1": 437.818, "r_y1": 155.76, "r_x2": 437.818, "r_y2": 147.20799999999997, "r_x3": 346.371, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "into EDD architecture.", "orig": "into EDD architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.371, "r_y0": 155.76, "r_x1": 545.115, "r_y1": 155.76, "r_x2": 545.115, "r_y2": 147.20799999999997, "r_x3": 442.371, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "As mentioned previously,", "orig": "As mentioned previously,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 167.71500000000003, "r_x1": 446.157, "r_y1": 167.71500000000003, "r_x2": 446.157, "r_y2": 159.163, "r_x3": 308.862, "r_y3": 159.163, "coord_origin": "TOPLEFT"}, "text": "the Structure Decoder provides the", "orig": "the Structure Decoder provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.29, "r_y0": 167.572, "r_x1": 525.042, "r_y1": 167.572, "r_x2": 525.042, "r_y2": 158.98400000000004, "r_x3": 448.29, "r_y3": 158.98400000000004, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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Tab.", "orig": "from the PDF cells rather than decoding from images. 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Additionally, we see that TableFormer is", "orig": "demonstrates TableFormer predictions on previously unseen language (Japanese). 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training set contains only English content.", "orig": "text, although the training set contains only English content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "We provide more visualizations including the intermediate", "orig": "We provide more visualizations including the intermediate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 195.217, "r_y1": 689.156, "r_x2": 195.217, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", 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Future Work & Conclusion", "orig": "6. 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"r_y2": 632.713, "r_x3": 308.862, "r_y3": 632.713, "coord_origin": "TOPLEFT"}, "text": "erated dataset that reinforces missing characteristics from", "orig": "erated dataset that reinforces missing characteristics from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.22, "r_x1": 365.858, "r_y1": 653.22, "r_x2": 365.858, "r_y2": 644.668, "r_x3": 308.862, "r_y3": 644.668, "coord_origin": "TOPLEFT"}, "text": "other datasets.", "orig": "other datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 682.847, "r_x1": 364.406, "r_y1": 682.847, "r_x2": 364.406, "r_y2": 672.099, "r_x3": 308.862, "r_y3": 672.099, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 313.345, "r_y0": 701.901, "r_x1": 323.8, "r_y1": 701.901, "r_x2": 323.8, "r_y2": 694.204, "r_x3": 313.345, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.785, "r_y0": 701.901, "r_x1": 545.109, "r_y1": 701.901, "r_x2": 545.109, "r_y2": 694.204, "r_x3": 328.785, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.781, "r_y0": 712.86, "r_x1": 545.113, "r_y1": 712.86, "r_x2": 545.113, "r_y2": 705.163, "r_x3": 328.781, "r_y3": 705.163, "coord_origin": "TOPLEFT"}, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "orig": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 21, "label": "list_item", "bbox": {"l": 53.286, "t": 78.995, "r": 499.556, "b": 86.47699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.541211724281311, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.286, "r_y0": 86.47699999999998, "r_x1": 499.556, "r_y1": 86.47699999999998, "r_x2": 499.556, "r_y2": 78.995, "r_x3": 53.286, "r_y3": 78.995, "coord_origin": "TOPLEFT"}, "text": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "orig": "a. 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Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "orig": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.812, "t": 94.58900000000006, "r": 284.346, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46452972292900085, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 102.07100000000003, "r_x1": 284.346, "r_y1": 102.07100000000003, "r_x2": 284.346, "r_y2": 94.58900000000006, "r_x3": 53.812, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Japanese language (previously unseen by TableFormer):", "orig": "Japanese language (previously unseen by TableFormer):", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.831, "t": 94.58900000000006, "r": 431.091, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4629225730895996, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 304.831, "r_y0": 102.07100000000003, "r_x1": 431.091, "r_y1": 102.07100000000003, "r_x2": 431.091, "r_y2": 94.58900000000006, "r_x3": 304.831, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Example table from FinTabNet:", "orig": "Example table from FinTabNet:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example table from FinTabNet:"}, {"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.812, "t": 208.54100000000005, "r": 385.934, "b": 216.02300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.5986505746841431, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 216.02300000000002, "r_x1": 385.934, "r_y1": 216.02300000000002, "r_x2": 385.934, "r_y2": 208.54100000000005, "r_x3": 53.812, "r_y3": 208.54100000000005, "coord_origin": "TOPLEFT"}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 380.427, "t": 292.551, "r": 549.422, "b": 298.536, "coord_origin": "TOPLEFT"}, "confidence": 0.7499915957450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.427, "r_y0": 298.536, "r_x1": 549.422, "r_y1": 298.536, "r_x2": 549.422, "r_y2": 292.551, "r_x3": 380.427, "r_y3": 292.551, "coord_origin": "TOPLEFT"}, "text": "Text is aligned to match original for ease of viewing", "orig": "Text is aligned to match original for ease of viewing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Text is aligned to match original for ease of viewing"}, {"label": "caption", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 50.112, "t": 321.146, "r": 86.864, "b": 329.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9140303134918213, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 329.698, "r_x1": 86.864, "r_y1": 329.698, "r_x2": 86.864, "r_y2": 321.146, "r_x3": 50.112, "r_y3": 321.146, "coord_origin": "TOPLEFT"}, "text": "Figure 5:", "orig": "Figure 5:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5:"}, {"label": "caption", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 62.595, "t": 458.997, "r": 98.849, "b": 467.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9153602719306946, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.595, "r_y0": 467.549, "r_x1": 98.849, "r_y1": 467.549, "r_x2": 98.849, "r_y2": 458.997, "r_x3": 62.595, "r_y3": 458.997, "coord_origin": "TOPLEFT"}, "text": "Figure 6:", "orig": "Figure 6:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 6:"}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.709, "r": 460.848, "b": 501.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 501.457, "r_x1": 460.848, "r_y1": 501.457, "r_x2": 460.848, "r_y2": 490.709, "r_x3": 308.862, "r_y3": 490.709, "coord_origin": "TOPLEFT"}, "text": "6. Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.395, "r": 163.756, "b": 501.247, "coord_origin": "TOPLEFT"}, "confidence": 0.9561247229576111, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.247, "r_x1": 163.756, "r_y1": 501.247, "r_x2": 163.756, "r_y2": 491.395, "r_x3": 50.112, "r_y3": 491.395, "coord_origin": "TOPLEFT"}, "text": "5.5. Qualitative Analysis", "orig": "5.5. Qualitative Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 513.162, "r": 545.115, "b": 521.7139999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 62.067, "t": 537.142, "r": 118.505, "b": 545.694, "coord_origin": "TOPLEFT"}, "confidence": 0.9852361679077148, 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Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "orig": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.812, "t": 94.58900000000006, "r": 284.346, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46452972292900085, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 102.07100000000003, "r_x1": 284.346, "r_y1": 102.07100000000003, "r_x2": 284.346, "r_y2": 94.58900000000006, "r_x3": 53.812, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Japanese language (previously unseen by TableFormer):", "orig": "Japanese language (previously unseen by TableFormer):", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.831, "t": 94.58900000000006, "r": 431.091, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4629225730895996, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 304.831, "r_y0": 102.07100000000003, "r_x1": 431.091, "r_y1": 102.07100000000003, "r_x2": 431.091, "r_y2": 94.58900000000006, "r_x3": 304.831, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Example table from FinTabNet:", "orig": "Example table from FinTabNet:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example table from FinTabNet:"}, {"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.812, "t": 208.54100000000005, "r": 385.934, "b": 216.02300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.5986505746841431, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 216.02300000000002, "r_x1": 385.934, "r_y1": 216.02300000000002, "r_x2": 385.934, "r_y2": 208.54100000000005, "r_x3": 53.812, "r_y3": 208.54100000000005, "coord_origin": "TOPLEFT"}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 380.427, "t": 292.551, "r": 549.422, "b": 298.536, "coord_origin": "TOPLEFT"}, "confidence": 0.7499915957450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.427, "r_y0": 298.536, "r_x1": 549.422, "r_y1": 298.536, "r_x2": 549.422, "r_y2": 292.551, "r_x3": 380.427, "r_y3": 292.551, "coord_origin": "TOPLEFT"}, "text": "Text is aligned to match original for ease of viewing", "orig": "Text is aligned to match original for ease of viewing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Text is aligned to match original for ease of viewing"}, {"label": "caption", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 50.112, "t": 321.146, "r": 86.864, "b": 329.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9140303134918213, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 329.698, "r_x1": 86.864, "r_y1": 329.698, "r_x2": 86.864, "r_y2": 321.146, "r_x3": 50.112, "r_y3": 321.146, "coord_origin": "TOPLEFT"}, "text": "Figure 5:", "orig": "Figure 5:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5:"}, {"label": "caption", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 62.595, "t": 458.997, "r": 98.849, "b": 467.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9153602719306946, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.595, "r_y0": 467.549, "r_x1": 98.849, "r_y1": 467.549, "r_x2": 98.849, "r_y2": 458.997, "r_x3": 62.595, "r_y3": 458.997, "coord_origin": "TOPLEFT"}, "text": "Figure 6:", "orig": "Figure 6:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 6:"}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.709, "r": 460.848, "b": 501.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 501.457, "r_x1": 460.848, "r_y1": 501.457, "r_x2": 460.848, "r_y2": 490.709, "r_x3": 308.862, "r_y3": 490.709, "coord_origin": "TOPLEFT"}, "text": "6. Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 50.112, "t": 491.395, "r": 163.756, "b": 501.247, "coord_origin": "TOPLEFT"}, "confidence": 0.9561247229576111, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.247, "r_x1": 163.756, "r_y1": 501.247, "r_x2": 163.756, "r_y2": 491.395, "r_x3": 50.112, "r_y3": 491.395, "coord_origin": "TOPLEFT"}, "text": "5.5. Qualitative Analysis", "orig": "5.5. Qualitative Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.5. Qualitative Analysis"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 320.817, "t": 513.162, "r": 545.115, "b": 521.7139999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 62.067, "t": 537.142, "r": 118.505, "b": 545.694, "coord_origin": "TOPLEFT"}, "confidence": 0.9852361679077148, 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Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.382, "r_y0": 244.726, "r_x1": 492.911, "r_y1": 244.726, "r_x2": 492.911, "r_y2": 236.17399999999998, "r_x3": 335.382, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "Prepare styling and content templates:", "orig": "Prepare styling and content templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.669, "r_y0": 244.726, "r_x1": 545.115, "r_y1": 244.726, "r_x2": 545.115, "r_y2": 236.17399999999998, "r_x3": 498.669, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "The styling", "orig": "The styling", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.457, "r_x1": 425.693, "r_y1": 316.457, "r_x2": 425.693, "r_y2": 307.905, "r_x3": 308.862, "r_y3": 307.905, "coord_origin": "TOPLEFT"}, "text": "PubTabNet, FinTabNet, etc.).", "orig": "PubTabNet, FinTabNet, etc.).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.692, "r_y0": 329.171, "r_x1": 435.377, "r_y1": 329.171, "r_x2": 435.377, "r_y2": 320.619, "r_x3": 332.692, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "Generate table structures:", "orig": "Generate table structures:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.342, "r_y0": 329.171, "r_x1": 545.115, "r_y1": 329.171, "r_x2": 545.115, "r_y2": 320.619, "r_x3": 439.342, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "The structure of each syn-", "orig": "The structure of each syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 341.126, "r_x1": 545.115, "r_y1": 341.126, "r_x2": 545.115, "r_y2": 332.574, "r_x3": 308.862, "r_y3": 332.574, "coord_origin": "TOPLEFT"}, "text": "thetic dataset assumes a horizontal table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, "r_y1": 353.081, "r_x2": 545.115, "r_y2": 344.529, "r_x3": 431.462, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "rows and a table body that", "orig": "rows and a table body that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 365.037, "r_x1": 545.115, "r_y1": 365.037, "r_x2": 545.115, "r_y2": 356.485, "r_x3": 308.862, "r_y3": 356.485, "coord_origin": "TOPLEFT"}, "text": "may contain a combination of row spans and column spans.", "orig": "may contain a combination of row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.992, "r_x1": 545.115, "r_y1": 376.992, "r_x2": 545.115, "r_y2": 368.44, "r_x3": 308.862, "r_y3": 368.44, "coord_origin": "TOPLEFT"}, "text": "However, spans are not allowed to cross the header - body", "orig": "However, spans are not allowed to cross the header - body", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.947, "r_x1": 348.334, "r_y1": 388.947, "r_x2": 348.334, "r_y2": 380.395, "r_x3": 308.862, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "boundary.", "orig": "boundary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 353.504, 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rows and columns, number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.857, "r_x1": 545.115, "r_y1": 412.857, "r_x2": 545.115, "r_y2": 404.305, "r_x3": 308.862, "r_y3": 404.305, "coord_origin": "TOPLEFT"}, "text": "header rows, type of spans (header only spans, row only", "orig": "header rows, type of spans (header only spans, row only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.812, "r_x1": 333.49, "r_y1": 424.812, "r_x2": 333.49, "r_y2": 416.26, "r_x3": 308.862, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "spans,", "orig": "spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.082, "r_y0": 424.812, "r_x1": 418.66, "r_y1": 424.812, "r_x2": 418.66, "r_y2": 416.26, "r_x3": 338.082, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "column only spans,", "orig": "column only spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.253, "r_y0": 424.812, "r_x1": 545.115, "r_y1": 424.812, "r_x2": 545.115, "r_y2": 416.26, "r_x3": 423.253, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "both row and column spans),", "orig": "both row and column spans),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.768, "r_x1": 545.115, "r_y1": 436.768, "r_x2": 545.115, "r_y2": 428.216, "r_x3": 308.862, "r_y3": 428.216, "coord_origin": "TOPLEFT"}, "text": "maximum span size and the ratio of the table area covered", "orig": "maximum span size and the ratio of the table area covered", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.723, "r_x1": 345.943, "r_y1": 448.723, "r_x2": 345.943, "r_y2": 440.171, "r_x3": 308.862, "r_y3": 440.171, "coord_origin": "TOPLEFT"}, "text": "by spans.", "orig": "by spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.073, "r_y0": 461.294, "r_x1": 511.864, "r_y1": 461.294, "r_x2": 511.864, "r_y2": 452.706, "r_x3": 488.073, "r_y3": 452.706, "coord_origin": "TOPLEFT"}, "text": "theme", "orig": "theme", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 511.863, "r_y0": 461.437, "r_x1": 545.108, "r_y1": 461.437, "r_x2": 545.108, "r_y2": 452.885, "r_x3": 511.863, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": ", a set of", "orig": ", a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.392, "r_x1": 545.115, "r_y1": 473.392, "r_x2": 545.115, "r_y2": 464.84, "r_x3": 308.862, "r_y3": 464.84, "coord_origin": "TOPLEFT"}, "text": "suitable content templates is chosen first. Then, this content", "orig": "suitable content templates is chosen first. Then, this content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.348, "r_x1": 545.115, "r_y1": 485.348, "r_x2": 545.115, "r_y2": 476.796, "r_x3": 308.862, "r_y3": 476.796, "coord_origin": "TOPLEFT"}, "text": "can be combined with purely random text to produce the", "orig": "can be combined with purely random text to produce the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.303, "r_x1": 379.148, "r_y1": 497.303, "r_x2": 379.148, "r_y2": 488.751, "r_x3": 308.862, "r_y3": 488.751, "coord_origin": "TOPLEFT"}, "text": "synthetic content.", "orig": "synthetic content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.217, "r_y0": 510.017, "r_x1": 434.082, "r_y1": 510.017, "r_x2": 434.082, "r_y2": 501.465, "r_x3": 334.217, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Apply styling templates:", "orig": "Apply styling templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.063, "r_y0": 510.017, "r_x1": 545.115, "r_y1": 510.017, "r_x2": 545.115, "r_y2": 501.465, "r_x3": 439.063, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Depending on the domain", "orig": "Depending on the domain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.972, "r_x1": 406.884, "r_y1": 521.972, "r_x2": 406.884, "r_y2": 513.4200000000001, "r_x3": 308.862, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "of the synthetic dataset,", "orig": "of the synthetic dataset,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.019, "r_y0": 521.972, "r_x1": 545.115, "r_y1": 521.972, "r_x2": 545.115, "r_y2": 513.4200000000001, "r_x3": 411.019, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "a set of styling templates is first", "orig": "a set of styling templates is first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.928, "r_x1": 384.299, "r_y1": 533.928, "r_x2": 384.299, "r_y2": 525.376, "r_x3": 308.862, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "manually selected.", "orig": "manually selected.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.253, "r_y0": 533.928, "r_x1": 545.115, "r_y1": 533.928, "r_x2": 545.115, "r_y2": 525.376, "r_x3": 391.253, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "Then, a style is randomly selected to", "orig": "Then, a style is randomly selected to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.883, "r_x1": 496.159, "r_y1": 545.883, "r_x2": 496.159, "r_y2": 537.331, "r_x3": 308.862, "r_y3": 537.331, "coord_origin": "TOPLEFT"}, "text": "format the appearance of the synthesized table.", "orig": "format the appearance of the synthesized table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.402, "r_y0": 558.597, "r_x1": 450.371, "r_y1": 558.597, "r_x2": 450.371, "r_y2": 550.045, "r_x3": 335.402, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "Render the complete tables:", "orig": "Render the complete tables:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.139, "r_y0": 558.597, "r_x1": 545.115, "r_y1": 558.597, "r_x2": 545.115, "r_y2": 550.045, "r_x3": 456.139, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "The synthetic table is", "orig": "The synthetic table is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 570.552, "r_x1": 334.326, "r_y1": 570.552, "r_x2": 334.326, "r_y2": 562.0, "r_x3": 308.862, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "finally", "orig": "finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.983, "r_y0": 570.552, "r_x1": 545.115, "r_y1": 570.552, "r_x2": 545.115, "r_y2": 562.0, "r_x3": 337.983, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "rendered by a web browser engine to generate the", "orig": "rendered by a web browser engine to generate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 582.5070000000001, "r_x1": 545.115, "r_y1": 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A batching technique is", "orig": "bounding boxes for each table cell. 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This happens mainly due the fol-", "orig": "original table is required. This happens mainly due the fol-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 714.063, "r_x1": 371.427, "r_y1": 714.063, "r_x2": 371.427, "r_y2": 705.511, "r_x3": 308.862, "r_y3": 705.511, "coord_origin": "TOPLEFT"}, "text": "lowing reasons:", "orig": "lowing reasons:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 121.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 426.929, "b": 171.476, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 62.067, "t": 200.18899999999996, "r": 286.365, "b": 208.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 320.817, "t": 211.50400000000002, "r": 545.115, "b": 220.05600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 320.817, "t": 236.17399999999998, "r": 328.289, "b": 244.726, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 320.817, "t": 320.619, "r": 328.289, "b": 329.171, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 391.674, "r": 236.243, "b": 400.226, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 320.817, "t": 452.885, "r": 485.758, "b": 461.437, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 320.817, "t": 501.465, "r": 328.289, "b": 510.017, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 320.817, "t": 550.045, "r": 328.289, "b": 558.597, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2909999999999, "r": 317.828, "b": 633.039, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 62.067, "t": 630.979, "r": 286.365, "b": 639.531, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 657.69, "r": 545.115, "b": 666.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. 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Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 426.929, "b": 171.476, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ances in regard to their size,"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 62.067, "t": 200.18899999999996, "r": 286.365, "b": 208.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As a first step of our data preparation process, we have"}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 320.817, "t": 211.50400000000002, "r": 545.115, "b": 220.05600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The process of generating a synthetic dataset can be de-"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 320.817, "t": 236.17399999999998, "r": 328.289, "b": 244.726, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 320.817, "t": 320.619, "r": 328.289, "b": 329.171, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 391.674, "r": 236.243, "b": 400.226, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have developed a technique that tries"}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 320.817, "t": 452.885, "r": 485.758, "b": 461.437, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Generate content: Based on the dataset"}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 320.817, "t": 501.465, "r": 328.289, "b": 510.017, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 320.817, "t": 550.045, "r": 328.289, "b": 558.597, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2909999999999, "r": 317.828, "b": 633.039, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 62.067, "t": 630.979, "r": 286.365, "b": 639.531, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 657.69, "r": 545.115, "b": 666.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although TableFormer can predict the table structure and"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 681.6, "r": 286.365, "b": 690.152, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Aiming to train and evaluate our models in a broader"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 121.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Details on the datasets"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 426.929, "b": 171.476, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ances in regard to their size,"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 62.067, "t": 200.18899999999996, "r": 286.365, "b": 208.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As a first step of our data preparation process, we have"}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 320.817, "t": 211.50400000000002, "r": 545.115, "b": 220.05600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The process of generating a synthetic dataset can be de-"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 320.817, "t": 236.17399999999998, "r": 328.289, "b": 244.726, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 320.817, "t": 320.619, "r": 328.289, "b": 329.171, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 62.067, "t": 391.674, "r": 236.243, "b": 400.226, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have developed a technique that tries"}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 320.817, "t": 452.885, "r": 485.758, "b": 461.437, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Generate content: Based on the dataset"}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 320.817, "t": 501.465, "r": 328.289, "b": 510.017, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 320.817, "t": 550.045, "r": 328.289, "b": 558.597, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2909999999999, "r": 317.828, "b": 633.039, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 62.067, "t": 630.979, "r": 286.365, "b": 639.531, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 320.817, "t": 657.69, "r": 545.115, "b": 666.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although TableFormer can predict the table structure and"}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. 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Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 62.067, "t": 681.6, "r": 286.365, "b": 690.152, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Aiming to train and evaluate our models in a broader"}], "headers": [{"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": 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"orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 219.75199999999995, "r_x1": 286.365, "r_y1": 219.75199999999995, "r_x2": 286.365, "r_y2": 211.20000000000005, "r_x3": 70.037, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer output does not include the table cell con-", "orig": "TableFormer output does not include the table cell con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 231.707, "r_x1": 87.472, "r_y1": 231.707, "r_x2": 87.472, "r_y2": 223.15499999999997, "r_x3": 70.037, "r_y3": 223.15499999999997, "coord_origin": "TOPLEFT"}, "text": "tent.", "orig": "tent.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 252.89200000000005, "r_x1": 65.056, "r_y1": 252.89200000000005, "r_x2": 65.056, "r_y2": 244.34000000000003, "r_x3": 61.569, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 252.89200000000005, "r_x1": 286.365, "r_y1": 252.89200000000005, "r_x2": 286.365, "r_y2": 244.34000000000003, "r_x3": 70.037, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "There are occasional inaccuracies in the predictions of", "orig": "There are occasional inaccuracies in the predictions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 264.847, "r_x1": 150.415, "r_y1": 264.847, "r_x2": 150.415, "r_y2": 256.29499999999996, "r_x3": 70.037, "r_y3": 256.29499999999996, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes.", "orig": "the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 288.024, "r_x1": 286.365, "r_y1": 288.024, "r_x2": 286.365, "r_y2": 279.472, "r_x3": 62.067, "r_y3": 279.472, "coord_origin": "TOPLEFT"}, "text": "However, it is possible to mitigate those limitations by", "orig": "However, it is possible to mitigate those limitations by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 299.979, "r_x1": 286.365, "r_y1": 299.979, "r_x2": 286.365, "r_y2": 291.427, "r_x3": 50.112, "r_y3": 291.427, "coord_origin": "TOPLEFT"}, "text": "combining the TableFormer predictions with the informa-", "orig": "combining the TableFormer predictions with the informa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.934, "r_x1": 286.365, "r_y1": 311.934, "r_x2": 286.365, "r_y2": 303.382, "r_x3": 50.112, "r_y3": 303.382, "coord_origin": "TOPLEFT"}, "text": "tion already present inside a programmatic PDF document.", "orig": "tion already present inside a programmatic PDF document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.889, "r_x1": 123.387, "r_y1": 323.889, "r_x2": 123.387, "r_y2": 315.337, "r_x3": 50.112, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "More specifically,", "orig": "More specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.348, "r_y0": 323.889, "r_x1": 286.365, "r_y1": 323.889, "r_x2": 286.365, "r_y2": 315.337, "r_x3": 128.348, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "PDF documents can be seen as a se-", "orig": "PDF documents can be seen as a se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.845, "r_x1": 286.365, "r_y1": 335.845, "r_x2": 286.365, "r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 516.431, "r_x1": 69.539, "r_y1": 516.431, "r_x2": 69.539, "r_y2": 507.879, "r_x3": 62.067, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.248, "r_y0": 516.431, "r_x1": 286.365, "r_y1": 516.431, "r_x2": 286.365, "r_y2": 507.879, "r_x3": 75.248, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "Use a carefully selected IOU threshold to designate", "orig": "Use a carefully selected IOU threshold to designate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 528.387, "r_x1": 226.071, "r_y1": 528.387, "r_x2": 226.071, "r_y2": 519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. 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"TOPLEFT"}, "text": "-coordinates and cell sizes.", "orig": "-coordinates and cell sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 292.184, "r_x1": 328.289, "r_y1": 292.184, "r_x2": 328.289, "r_y2": 283.632, "r_x3": 320.817, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "7.", "orig": "7.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.144, "r_y0": 292.184, "r_x1": 545.115, "r_y1": 292.184, "r_x2": 545.115, "r_y2": 283.632, "r_x3": 332.144, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "Generate a new set of pair-wise matches between the", "orig": "Generate a new set of pair-wise matches between the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.139, "r_x1": 476.562, "r_y1": 304.139, "r_x2": 476.562, "r_y2": 295.587, "r_x3": 308.862, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "corrected bounding boxes and PDF cells.", "orig": "corrected bounding boxes and PDF cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 482.082, "r_y0": 304.139, "r_x1": 545.115, "r_y1": 304.139, "r_x2": 545.115, "r_y2": 295.587, "r_x3": 482.082, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "This time use a", "orig": "This time use a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.094, "r_x1": 545.115, "r_y1": 316.094, "r_x2": 545.115, "r_y2": 307.542, "r_x3": 308.862, "r_y3": 307.542, "coord_origin": "TOPLEFT"}, "text": "modified version of the IOU metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", 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"orig": "configurations, multiline rows, different variety of separa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 333.669, "r_x1": 175.123, "r_y1": 333.669, "r_x2": 175.123, "r_y2": 325.081, "r_x3": 50.112, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "tion lines, missing entries, etc.", "orig": "tion lines, missing entries, etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.931, "r_y0": 333.669, "r_x1": 286.365, "r_y1": 333.669, "r_x2": 286.365, "r_y2": 325.081, "r_x3": 180.931, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "As such, the correct iden-", "orig": "As such, the correct iden-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, "r_x1": 132.443, "r_y1": 369.535, "r_x2": 132.443, "r_y2": 360.947, "r_x3": 50.112, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "identification model.", "orig": "identification model.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.777, "r_y0": 369.535, "r_x1": 286.365, "r_y1": 369.535, "r_x2": 286.365, "r_y2": 360.947, "r_x3": 136.777, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "The latter improves the latest end-to-", "orig": "The latter improves the latest end-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 381.49, "r_x1": 170.4, "r_y1": 381.49, "r_x2": 170.4, "r_y2": 372.902, "r_x3": 50.112, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "end deep learning model (i.e.", "orig": "end deep learning model (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 417.355, "r_x1": 286.365, "r_y1": 417.355, "r_x2": 286.365, "r_y2": 408.767, "r_x3": 50.112, "r_y3": 408.767, "coord_origin": "TOPLEFT"}, "text": "we can obtain the content of the table-cells from program-", "orig": "we can obtain the content of the table-cells from program-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 429.311, "r_x1": 286.365, "r_y1": 429.311, "r_x2": 286.365, "r_y2": 420.723, "r_x3": 50.112, "r_y3": 420.723, "coord_origin": "TOPLEFT"}, "text": "matic PDF's directly from the PDF source and avoid the", "orig": "matic PDF's directly from the PDF source and avoid the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 441.266, "r_x1": 207.232, "r_y1": 441.266, "r_x2": 207.232, "r_y2": 432.678, "r_x3": 50.112, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. Red-annotation of bounding boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.463, "r_y0": 333.15, "r_x1": 472.474, "r_y1": 333.15, "r_x2": 472.474, "r_y2": 324.837, "r_x3": 326.463, "r_y3": 324.837, "coord_origin": "TOPLEFT"}, "text": "Blue-predictions by TableFormer", "orig": "Blue-predictions by TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 428.838, "r_x1": 491.191, "r_y1": 428.838, "r_x2": 491.191, "r_y2": 420.525, "r_x3": 315.567, "r_y3": 420.525, "coord_origin": "TOPLEFT"}, "text": "c. Structure predicted by TableFormer:", "orig": "c. 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"r_y3": 289.216, "coord_origin": "TOPLEFT"}, "text": "nately, tables come in a large variety of shapes and sizes.", "orig": "nately, tables come in a large variety of shapes and sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 309.759, "r_x1": 103.601, "r_y1": 309.759, "r_x2": 103.601, "r_y2": 301.171, "r_x3": 50.112, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "Furthermore,", "orig": "Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.393, "r_y0": 309.759, "r_x1": 124.692, "r_y1": 309.759, "r_x2": 124.692, "r_y2": 301.171, "r_x3": 108.393, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "they", "orig": "they", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 129.026, "r_y0": 309.759, "r_x1": 286.365, "r_y1": 309.759, "r_x2": 286.365, "r_y2": 301.171, "r_x3": 129.026, "r_y3": 301.171, "coord_origin": "TOPLEFT"}, "text": "can have complex column/row-header", "orig": "can have complex column/row-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 321.714, "r_x1": 286.365, "r_y1": 321.714, "r_x2": 286.365, "r_y2": 313.126, "r_x3": 50.112, "r_y3": 313.126, "coord_origin": "TOPLEFT"}, "text": "configurations, multiline rows, different variety of separa-", "orig": "configurations, multiline rows, different variety of separa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 333.669, "r_x1": 175.123, "r_y1": 333.669, "r_x2": 175.123, "r_y2": 325.081, "r_x3": 50.112, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "tion lines, missing entries, etc.", "orig": "tion lines, missing entries, etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.931, "r_y0": 333.669, "r_x1": 286.365, "r_y1": 333.669, "r_x2": 286.365, "r_y2": 325.081, "r_x3": 180.931, "r_y3": 325.081, "coord_origin": "TOPLEFT"}, "text": "As such, the correct iden-", "orig": "As such, the correct iden-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 345.624, "r_x1": 85.539, "r_y1": 345.624, "r_x2": 85.539, "r_y2": 337.036, "r_x3": 50.112, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "tification", "orig": "tification", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 89.942, 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"rect": {"r_x0": 181.947, "r_y0": 345.624, "r_x1": 286.365, "r_y1": 345.624, "r_x2": 286.365, "r_y2": 337.036, "r_x3": 181.947, "r_y3": 337.036, "coord_origin": "TOPLEFT"}, "text": "from an image is a non-", "orig": "from an image is a non-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 357.58, "r_x1": 95.791, "r_y1": 357.58, "r_x2": 95.791, "r_y2": 348.992, "r_x3": 50.112, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "trivial task.", "orig": "trivial task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, "r_x1": 132.443, "r_y1": 369.535, "r_x2": 132.443, "r_y2": 360.947, "r_x3": 50.112, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "identification model.", "orig": "identification model.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.777, "r_y0": 369.535, "r_x1": 286.365, "r_y1": 369.535, "r_x2": 286.365, "r_y2": 360.947, "r_x3": 136.777, "r_y3": 360.947, "coord_origin": "TOPLEFT"}, "text": "The latter improves the latest end-to-", "orig": "The latter improves the latest end-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 381.49, "r_x1": 170.4, "r_y1": 381.49, "r_x2": 170.4, "r_y2": 372.902, "r_x3": 50.112, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "end deep learning model (i.e.", "orig": "end deep learning model (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 417.355, "r_x1": 286.365, "r_y1": 417.355, "r_x2": 286.365, "r_y2": 408.767, "r_x3": 50.112, "r_y3": 408.767, "coord_origin": "TOPLEFT"}, "text": "we can obtain the content of the table-cells from program-", "orig": "we can obtain the content of the table-cells from program-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 429.311, "r_x1": 286.365, "r_y1": 429.311, "r_x2": 286.365, "r_y2": 420.723, "r_x3": 50.112, "r_y3": 420.723, "coord_origin": "TOPLEFT"}, "text": "matic PDF's directly from the PDF source and avoid the", "orig": "matic PDF's directly from the PDF source and avoid the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 441.266, "r_x1": 207.232, "r_y1": 441.266, "r_x2": 207.232, "r_y2": 432.678, "r_x3": 50.112, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 50.112, "t": 539.943, "r": 126.948, "b": 550.691, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 561.052, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 315.567, "t": 218.34900000000005, "r": 408.441, "b": 226.66200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 315.567, "t": 314.037, "r": 486.402, "b": 333.15, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. 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non-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 357.58, "r_x1": 95.791, "r_y1": 357.58, "r_x2": 95.791, "r_y2": 348.992, "r_x3": 50.112, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "trivial task.", "orig": "trivial task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, "r_x1": 132.443, "r_y1": 369.535, "r_x2": 132.443, 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false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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"coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.112, "t": 539.943, "r": 126.948, "b": 550.691, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 561.052, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.567, "t": 218.34900000000005, "r": 408.441, "b": 226.66200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.567, "t": 314.037, "r": 486.402, "b": 333.15, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. Red-annotation of bounding boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.463, "r_y0": 333.15, "r_x1": 472.474, "r_y1": 333.15, "r_x2": 472.474, "r_y2": 324.837, "r_x3": 326.463, "r_y3": 324.837, "coord_origin": "TOPLEFT"}, "text": "Blue-predictions by TableFormer", "orig": "Blue-predictions by TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.567, "t": 420.525, "r": 491.191, "b": 428.838, "coord_origin": "TOPLEFT"}, "confidence": 0.5501358509063721, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 428.838, "r_x1": 491.191, "r_y1": 428.838, "r_x2": 491.191, "r_y2": 420.525, "r_x3": 315.567, "r_y3": 420.525, "coord_origin": "TOPLEFT"}, "text": "c. Structure predicted by TableFormer:", "orig": "c. Structure predicted by TableFormer:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78173828125, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742761611938477, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.148, "r_y0": 351.356, "r_x1": 412.54, "r_y1": 351.356, "r_x2": 412.54, "r_y2": 343.601, "r_x3": 408.148, "r_y3": 343.601, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 356.11, "r_y0": 350.099, "r_x1": 360.503, "r_y1": 350.099, "r_x2": 360.503, "r_y2": 342.345, "r_x3": 356.11, "r_y3": 342.345, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, 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finding the structure of a given table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.961, "r_x1": 375.552, "r_y1": 664.961, "r_x2": 375.552, "r_y2": 656.409, "r_x3": 308.862, "r_y3": 656.409, "coord_origin": "TOPLEFT"}, "text": "in the document.", "orig": "in the document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.649, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 320.817, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "The first problem is called table-location and has been", "orig": "The first problem is called table-location and has been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "orig": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 474.261, "r_y1": 701.111, "r_x2": 474.261, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "of-the-art object-detection networks (e.g.", "orig": "of-the-art object-detection networks (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.342, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 479.342, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "YOLO and later", "orig": "YOLO and later", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 395.796, "r_y1": 713.066, "r_x2": 395.796, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "on Mask-RCNN [9]).", "orig": "on Mask-RCNN [9]).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.046, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 401.046, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "For all practical purposes, it can be", "orig": "For all practical purposes, it can be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). 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image is a non-", "orig": "from an image is a non-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 357.58, "r_x1": 95.791, "r_y1": 357.58, "r_x2": 95.791, "r_y2": 348.992, "r_x3": 50.112, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "trivial task.", "orig": "trivial task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.746, "r_y0": 357.58, "r_x1": 286.365, "r_y1": 357.58, "r_x2": 286.365, "r_y2": 348.992, "r_x3": 99.746, "r_y3": 348.992, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present a new table-structure", "orig": "In this paper, we present a new table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 369.535, 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.248, "r_y0": 381.49, "r_x1": 286.365, "r_y1": 381.49, "r_x2": 286.365, "r_y2": 372.902, "r_x3": 176.248, "r_y3": 372.902, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder from", "orig": "encoder-dual-decoder from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 393.445, "r_x1": 196.572, "r_y1": 393.445, "r_x2": 196.572, "r_y2": 384.857, "r_x3": 50.112, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "PubTabNet) in two significant ways.", "orig": "PubTabNet) in two significant ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.643, "r_y0": 393.445, "r_x1": 286.365, "r_y1": 393.445, "r_x2": 286.365, "r_y2": 384.857, "r_x3": 201.643, "r_y3": 384.857, "coord_origin": "TOPLEFT"}, "text": "First, we introduce a", "orig": "First, we introduce a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 405.4, "r_x1": 232.328, "r_y1": 405.4, "r_x2": 232.328, "r_y2": 396.812, "r_x3": 50.112, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "new object detection decoder for table-cells.", "orig": "new object detection decoder for table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.584, "r_y0": 405.4, "r_x1": 286.365, "r_y1": 405.4, "r_x2": 286.365, "r_y2": 396.812, "r_x3": 238.584, "r_y3": 396.812, "coord_origin": "TOPLEFT"}, "text": "In this way,", "orig": "In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 417.355, "r_x1": 286.365, "r_y1": 417.355, "r_x2": 286.365, "r_y2": 408.767, "r_x3": 50.112, "r_y3": 408.767, "coord_origin": "TOPLEFT"}, "text": "we can obtain the content of the table-cells from program-", "orig": "we can obtain the content of the table-cells from program-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 429.311, "r_x1": 286.365, "r_y1": 429.311, "r_x2": 286.365, "r_y2": 420.723, "r_x3": 50.112, "r_y3": 420.723, "coord_origin": "TOPLEFT"}, "text": "matic PDF's directly from the PDF source and avoid the", "orig": "matic PDF's directly from the PDF source and avoid the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 441.266, "r_x1": 207.232, "r_y1": 441.266, "r_x2": 207.232, "r_y2": 432.678, "r_x3": 50.112, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "training of the custom OCR decoders.", "orig": "training of the custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.096, "r_y0": 441.266, "r_x1": 286.365, "r_y1": 441.266, "r_x2": 286.365, "r_y2": 432.678, "r_x3": 214.096, "r_y3": 432.678, "coord_origin": "TOPLEFT"}, "text": "This architectural", "orig": "This architectural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 453.221, "r_x1": 286.365, "r_y1": 453.221, "r_x2": 286.365, "r_y2": 444.633, "r_x3": 50.112, "r_y3": 444.633, "coord_origin": "TOPLEFT"}, "text": "change leads to more accurate table-content extraction and", "orig": "change leads to more accurate table-content extraction and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 465.176, "r_x1": 204.233, "r_y1": 465.176, "r_x2": 204.233, "r_y2": 456.588, "r_x3": 50.112, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "allows us to tackle non-english tables.", "orig": "allows us to tackle non-english tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.587, "r_y0": 465.176, "r_x1": 286.365, "r_y1": 465.176, "r_x2": 286.365, "r_y2": 456.588, "r_x3": 208.587, "r_y3": 456.588, "coord_origin": "TOPLEFT"}, "text": "Second, we replace", "orig": "Second, we replace", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 477.131, "r_x1": 286.365, "r_y1": 477.131, "r_x2": 286.365, "r_y2": 468.543, "r_x3": 50.112, "r_y3": 468.543, "coord_origin": "TOPLEFT"}, "text": "the LSTM decoders with transformer based decoders. This", "orig": "the LSTM decoders with transformer based decoders. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 489.087, "r_x1": 286.365, "r_y1": 489.087, "r_x2": 286.365, "r_y2": 480.499, "r_x3": 50.112, "r_y3": 480.499, "coord_origin": "TOPLEFT"}, "text": "upgrade improves significantly the previous state-of-the-art", "orig": "upgrade improves significantly the previous state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 501.042, "r_x1": 286.365, "r_y1": 501.042, "r_x2": 286.365, "r_y2": 492.454, "r_x3": 50.112, "r_y3": 492.454, "coord_origin": "TOPLEFT"}, "text": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "orig": "tree-editing-distance-score (TEDS) from 91% to 98.5% on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 512.9970000000001, "r_x1": 276.652, "r_y1": 512.9970000000001, "r_x2": 276.652, "r_y2": 504.409, "r_x3": 50.112, "r_y3": 504.409, "coord_origin": "TOPLEFT"}, "text": "simple tables and from 88.7% to 95% on complex tables.", "orig": "simple tables and from 88.7% to 95% on complex tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 50.112, "t": 539.943, "r": 126.948, "b": 550.691, "coord_origin": "TOPLEFT"}, "confidence": 0.9317677617073059, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.691, "r_x1": 126.948, "r_y1": 550.691, "r_x2": 126.948, "r_y2": 539.943, "r_x3": 50.112, "r_y3": 539.943, "coord_origin": "TOPLEFT"}, "text": "1. Introduction", "orig": "1. Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Introduction"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 561.052, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9841895699501038, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 569.604, "r_x1": 166.266, "r_y1": 569.604, "r_x2": 166.266, "r_y2": 561.052, "r_x3": 62.067, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "The occurrence of tables", "orig": "The occurrence of tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.759, "r_y0": 569.604, "r_x1": 178.51, "r_y1": 569.604, "r_x2": 178.51, "r_y2": 561.052, "r_x3": 170.759, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.003, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 183.003, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "documents is ubiquitous.", "orig": "documents is ubiquitous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "They often summarise quantitative or factual data, which is", "orig": "They often summarise quantitative or factual data, which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "cumbersome to describe in verbose text but nevertheless ex-", "orig": "cumbersome to describe in verbose text but nevertheless ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 119.95, "r_y1": 605.47, "r_x2": 119.95, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "tremely valuable.", "orig": "tremely valuable.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.778, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 125.778, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, this compact representa-", "orig": "Unfortunately, this compact representa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "tion is often not easy to parse by machines. There are many", "orig": "tion is often not easy to parse by machines. There are many", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "implicit conventions used to obtain a compact table repre-", "orig": "implicit conventions used to obtain a compact table repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "sentation. For example, tables often have complex column-", "orig": "sentation. For example, tables often have complex column-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "and row-headers in order to reduce duplicated cell content.", "orig": "and row-headers in order to reduce duplicated cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "Lines of different shapes and sizes are leveraged to separate", "orig": "Lines of different shapes and sizes are leveraged to separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "content or indicate a tree structure. Additionally, tables can", "orig": "content or indicate a tree structure. Additionally, tables can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "also have empty/missing table-entries or multi-row textual", "orig": "also have empty/missing table-entries or multi-row textual", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 101.848, "r_y1": 701.111, "r_x2": 101.848, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "table-entries.", "orig": "table-entries.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.261, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 106.261, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Fig. 1 shows a table which presents all these", "orig": "Fig. 1 shows a table which presents all these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 76.403, "r_y1": 713.066, "r_x2": 76.403, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "issues.", "orig": "issues.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 315.567, "t": 218.34900000000005, "r": 408.441, "b": 226.66200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.6724023818969727, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 226.66200000000003, "r_x1": 408.441, "r_y1": 226.66200000000003, "r_x2": 408.441, "r_y2": 218.34900000000005, "r_x3": 315.567, "r_y3": 218.34900000000005, "coord_origin": "TOPLEFT"}, "text": "a. Picture of a table:", "orig": "a. Picture of a table:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a. Picture of a table:"}, {"label": "list_item", "id": 17, "page_no": 0, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 315.567, "t": 314.037, "r": 486.402, "b": 333.15, "coord_origin": "TOPLEFT"}, "confidence": 0.5549328923225403, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 322.35, "r_x1": 486.402, "r_y1": 322.35, "r_x2": 486.402, "r_y2": 314.037, "r_x3": 315.567, "r_y3": 314.037, "coord_origin": "TOPLEFT"}, "text": "b. Red-annotation of bounding boxes,", "orig": "b. Red-annotation of bounding boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.463, "r_y0": 333.15, "r_x1": 472.474, "r_y1": 333.15, "r_x2": 472.474, "r_y2": 324.837, "r_x3": 326.463, "r_y3": 324.837, "coord_origin": "TOPLEFT"}, "text": "Blue-predictions by TableFormer", "orig": "Blue-predictions by TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Red-annotation of bounding boxes, Blue-predictions by TableFormer"}, {"label": "list_item", "id": 18, "page_no": 0, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 315.567, "t": 420.525, "r": 491.191, "b": 428.838, "coord_origin": "TOPLEFT"}, "confidence": 0.5501358509063721, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 315.567, "r_y0": 428.838, "r_x1": 491.191, "r_y1": 428.838, "r_x2": 491.191, "r_y2": 420.525, "r_x3": 315.567, "r_y3": 420.525, "coord_origin": "TOPLEFT"}, "text": "c. Structure predicted by TableFormer:", "orig": "c. Structure predicted by TableFormer:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "c. Structure predicted by TableFormer:"}, {"label": "picture", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "picture", "bbox": {"l": 314.78173828125, "t": 338.0652770996094, "r": 539.1802978515625, "b": 410.0494384765625, "coord_origin": "TOPLEFT"}, "confidence": 0.8742761611938477, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.148, "r_y0": 351.356, "r_x1": 412.54, "r_y1": 351.356, "r_x2": 412.54, "r_y2": 343.601, "r_x3": 408.148, "r_y3": 343.601, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 356.11, "r_y0": 350.099, "r_x1": 360.503, "r_y1": 350.099, "r_x2": 360.503, "r_y2": 342.345, "r_x3": 356.11, "r_y3": 342.345, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, 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finding the structure of a given table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.961, "r_x1": 375.552, "r_y1": 664.961, "r_x2": 375.552, "r_y2": 656.409, "r_x3": 308.862, "r_y3": 656.409, "coord_origin": "TOPLEFT"}, "text": "in the document.", "orig": "in the document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document."}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 668.649, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.979152262210846, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 320.817, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "The first problem is called table-location and has been", "orig": "The first problem is called table-location and has been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "orig": "previously addressed [30, 38, 19, 21, 23, 26, 8] with state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 474.261, "r_y1": 701.111, "r_x2": 474.261, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "of-the-art object-detection networks (e.g.", "orig": "of-the-art object-detection networks (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.342, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 479.342, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "YOLO and later", "orig": "YOLO and later", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 395.796, "r_y1": 713.066, "r_x2": 395.796, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "on Mask-RCNN [9]).", "orig": "on Mask-RCNN [9]).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.046, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 401.046, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "For all practical purposes, it can be", "orig": "For all practical purposes, it can be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be"}], "headers": [{"label": "page_footer", "id": 12, "page_no": 0, "cluster": {"id": 12, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8045893311500549, "cells": [{"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 18.34, "t": 160.0, "r": 36.34, "b": 560.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8773143887519836, "cells": [{"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 560.0, "r_x1": 36.34, "r_y1": 160.0, "r_x2": 18.34, "r_y2": 160.0, "r_x3": 18.34, "r_y3": 560.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "orig": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "arXiv:2203.01017v2 [cs.CV] 11 Mar 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "considered as a solved problem, given enough ground-truth", "orig": "considered as a solved problem, given enough ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 112.647, "r_y1": 95.98400000000004, "r_x2": 112.647, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "data to train on.", "orig": "data to train on.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 120.34699999999998, "r_x1": 74.75, "r_y1": 120.34699999999998, "r_x2": 74.75, "r_y2": 111.79499999999996, "r_x3": 50.112, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "sition.", "orig": "sition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.335, "r_y0": 120.34699999999998, "r_x1": 286.365, "r_y1": 120.34699999999998, "r_x2": 286.365, "r_y2": 111.79499999999996, "r_x3": 81.335, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "The latter is a long standing problem in the com-", "orig": "The latter is a long standing problem in the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 132.30200000000002, "r_x1": 235.117, "r_y1": 132.30200000000002, "r_x2": 235.117, "r_y2": 123.75, "r_x3": 50.112, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "munity of document understanding [6, 4, 14].", "orig": "munity of document understanding [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.079, "r_y0": 132.30200000000002, "r_x1": 286.365, "r_y1": 132.30200000000002, "r_x2": 286.365, "r_y2": 123.75, "r_x3": 240.079, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "Contrary to", "orig": "Contrary to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 144.25699999999995, "r_x1": 286.365, "r_y1": 144.25699999999995, "r_x2": 286.365, "r_y2": 135.70500000000004, "r_x3": 50.112, "r_y3": 135.70500000000004, "coord_origin": "TOPLEFT"}, "text": "the table-location problem, there are no commonly used ap-", "orig": "the table-location problem, there are no commonly used ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 156.21299999999997, "r_x1": 286.365, "r_y1": 156.21299999999997, "r_x2": 286.365, "r_y2": 147.66099999999994, "r_x3": 50.112, "r_y3": 147.66099999999994, "coord_origin": "TOPLEFT"}, "text": "proaches that can easily be re-purposed to solve this prob-", "orig": "proaches that can easily be re-purposed to solve this prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 168.168, "r_x1": 286.365, "r_y1": 168.168, "r_x2": 286.365, "r_y2": 159.61599999999999, "r_x3": 50.112, "r_y3": 159.61599999999999, "coord_origin": "TOPLEFT"}, "text": "lem. Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. 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structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. 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Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 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{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.798, "r_y0": 342.39, "r_x1": 545.115, "r_y1": 342.39, "r_x2": 545.115, "r_y2": 333.838, "r_x3": 453.798, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "deep neural networks,", "orig": "deep neural networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 354.345, "r_x1": 545.115, "r_y1": 354.345, "r_x2": 545.115, "r_y2": 345.793, "r_x3": 308.862, "r_y3": 345.793, "coord_origin": "TOPLEFT"}, "text": "the community relied heavily on heuristic and/or statistical", "orig": "the community relied heavily on heuristic and/or statistical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 366.3, "r_x1": 545.115, "r_y1": 366.3, "r_x2": 545.115, "r_y2": 357.748, "r_x3": 308.862, "r_y3": 357.748, "coord_origin": "TOPLEFT"}, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "orig": "methods to do table structure identification [3, 7, 11, 5, 13,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 378.256, "r_x1": 324.633, "r_y1": 378.256, "r_x2": 324.633, "r_y2": 369.704, "r_x3": 308.862, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "28].", "orig": "28].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.634, "r_y0": 378.256, "r_x1": 545.115, "r_y1": 378.256, "r_x2": 545.115, "r_y2": 369.704, "r_x3": 329.634, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "Although such methods work well on constrained ta-", "orig": "Although such methods work well on constrained ta-", "text_direction": 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465.411, "coord_origin": "TOPLEFT"}, "text": "starting", "orig": "starting", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 473.713, "r_y0": 473.963, "r_x1": 545.115, "r_y1": 473.963, "r_x2": 545.115, "r_y2": 465.411, "r_x3": 473.713, "r_y3": 465.411, "coord_origin": "TOPLEFT"}, "text": "from an encoded", "orig": "from an encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.918, "r_x1": 335.701, "r_y1": 485.918, "r_x2": 335.701, "r_y2": 477.366, "r_x3": 308.862, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "image.", "orig": "image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.853, "r_y0": 485.918, "r_x1": 411.009, "r_y1": 485.918, "r_x2": 411.009, "r_y2": 477.366, "r_x3": 345.853, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "Such sequences", "orig": "Such sequences", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.85, "r_y0": 485.918, "r_x1": 424.149, "r_y1": 485.918, "r_x2": 424.149, "r_y2": 477.366, "r_x3": 415.85, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 429.001, "r_y0": 485.918, "r_x1": 454.914, "r_y1": 485.918, "r_x2": 454.914, "r_y2": 477.366, "r_x3": 429.001, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "tokens", "orig": "tokens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 459.756, "r_y0": 485.918, "r_x1": 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497.873, "r_x1": 459.218, "r_y1": 497.873, "r_x2": 459.218, "r_y2": 489.321, "r_x3": 308.862, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "tags [37, 17] or LaTeX symbols[10].", "orig": "tags [37, 17] or LaTeX symbols[10].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.115, "r_y0": 497.873, "r_x1": 545.115, "r_y1": 497.873, "r_x2": 545.115, "r_y2": 489.321, "r_x3": 465.115, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "The choice of sym-", "orig": "The choice of sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.828, "r_x1": 545.115, "r_y1": 509.828, "r_x2": 545.115, "r_y2": 501.276, "r_x3": 308.862, "r_y3": 501.276, "coord_origin": "TOPLEFT"}, "text": "bols is ultimately not very important, since one can be trans-", "orig": "bols is ultimately not very important, since one can be trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.784, "r_x1": 396.692, "r_y1": 521.784, "r_x2": 396.692, "r_y2": 513.232, "r_x3": 308.862, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "formed into the other.", "orig": "formed into the other.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.265, "r_y0": 521.784, "r_x1": 545.115, "r_y1": 521.784, "r_x2": 545.115, "r_y2": 513.232, "r_x3": 401.265, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "There are however subtle variations", "orig": "There are however subtle variations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.739, "r_x1": 433.484, "r_y1": 533.739, "r_x2": 433.484, "r_y2": 525.187, "r_x3": 308.862, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "in the Image-to-Text networks.", "orig": "in the Image-to-Text networks.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 438.187, "r_y0": 533.739, "r_x1": 545.115, "r_y1": 533.739, "r_x2": 545.115, "r_y2": 525.187, "r_x3": 438.187, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "The easiest network archi-", "orig": "The easiest network archi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.694, "r_x1": 420.941, "r_y1": 545.694, "r_x2": 420.941, "r_y2": 537.142, "r_x3": 308.862, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "tectures are 'image-encoder", "orig": "tectures are 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.595, "r_y0": 545.388, "r_x1": 433.558, "r_y1": 545.388, "r_x2": 433.558, "r_y2": 536.8240000000001, "r_x3": 423.595, "r_y3": 536.8240000000001, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.212, "r_y0": 545.694, "r_x1": 545.113, "r_y1": 545.694, "r_x2": 545.113, "r_y2": 537.142, "r_x3": 436.212, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "text-decoder' (IETD), sim-", "orig": "text-decoder' (IETD), sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.649, "r_x1": 545.115, "r_y1": 557.649, "r_x2": 545.115, "r_y2": 549.097, "r_x3": 308.862, "r_y3": 549.097, "coord_origin": "TOPLEFT"}, "text": "ilar to network architectures that try to provide captions to", "orig": "ilar to network architectures that try to provide captions to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.604, "r_x1": 545.115, "r_y1": 569.604, "r_x2": 545.115, "r_y2": 561.052, "r_x3": 308.862, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "images [32]. In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.045, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 364.045, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "to generate the textual content of each table", "orig": "to generate the textual content of each table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 325.739, "r_y1": 701.111, "r_x2": 325.739, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.746, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 331.746, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "The network architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 95.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "considered as a solved problem, given enough ground-truth", "orig": "considered as a solved problem, given enough ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 112.647, "r_y1": 95.98400000000004, "r_x2": 112.647, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "data to train on.", "orig": "data to train on.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 99.84000000000003, "r": 286.365, "b": 227.94399999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 120.34699999999998, "r_x1": 74.75, "r_y1": 120.34699999999998, "r_x2": 74.75, "r_y2": 111.79499999999996, "r_x3": 50.112, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "sition.", "orig": "sition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.335, "r_y0": 120.34699999999998, "r_x1": 286.365, "r_y1": 120.34699999999998, "r_x2": 286.365, "r_y2": 111.79499999999996, "r_x3": 81.335, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "The latter is a long standing problem in the com-", "orig": "The latter is a long standing problem in the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 132.30200000000002, "r_x1": 235.117, "r_y1": 132.30200000000002, "r_x2": 235.117, "r_y2": 123.75, "r_x3": 50.112, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "munity of document understanding [6, 4, 14].", "orig": "munity of document understanding [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.079, "r_y0": 132.30200000000002, "r_x1": 286.365, "r_y1": 132.30200000000002, "r_x2": 286.365, "r_y2": 123.75, "r_x3": 240.079, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "Contrary to", "orig": "Contrary to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 144.25699999999995, "r_x1": 286.365, "r_y1": 144.25699999999995, "r_x2": 286.365, "r_y2": 135.70500000000004, "r_x3": 50.112, "r_y3": 135.70500000000004, "coord_origin": "TOPLEFT"}, "text": "the table-location problem, there are no commonly used ap-", "orig": "the table-location problem, there are no commonly used ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 156.21299999999997, "r_x1": 286.365, "r_y1": 156.21299999999997, "r_x2": 286.365, "r_y2": 147.66099999999994, "r_x3": 50.112, "r_y3": 147.66099999999994, "coord_origin": "TOPLEFT"}, "text": "proaches that can easily be re-purposed to solve this prob-", "orig": "proaches that can easily be re-purposed to solve this prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 168.168, "r_x1": 286.365, "r_y1": 168.168, "r_x2": 286.365, "r_y2": 159.61599999999999, "r_x3": 50.112, "r_y3": 159.61599999999999, "coord_origin": "TOPLEFT"}, "text": "lem. Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.79999999999995, "r": 286.365, "b": 371.859, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.172, "r_x1": 286.365, "r_y1": 288.172, "r_x2": 286.365, "r_y2": 279.62, "r_x3": 50.112, "r_y3": 279.62, "coord_origin": "TOPLEFT"}, "text": "we can obtain the structure of any table, irregardless of the", "orig": "we can obtain the structure of any table, irregardless of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.128, "r_x1": 88.568, "r_y1": 300.128, "r_x2": 88.568, "r_y2": 291.576, "r_x3": 50.112, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "language.", "orig": "language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.502, "r_y0": 300.128, "r_x1": 286.365, "r_y1": 300.128, "r_x2": 286.365, "r_y2": 291.576, "r_x3": 95.502, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "Second, we want our algorithm to leverage as", "orig": "Second, we want our algorithm to leverage as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.083, "r_x1": 286.365, "r_y1": 312.083, "r_x2": 286.365, "r_y2": 303.531, "r_x3": 50.112, "r_y3": 303.531, "coord_origin": "TOPLEFT"}, "text": "much data as possible from the original PDF document. For", "orig": "much data as possible from the original PDF document. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.038, "r_x1": 286.365, "r_y1": 324.038, "r_x2": 286.365, "r_y2": 315.486, "r_x3": 50.112, "r_y3": 315.486, "coord_origin": "TOPLEFT"}, "text": "programmatic PDF documents, the text-cells can often be", "orig": "programmatic PDF documents, the text-cells can often be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.993, "r_x1": 286.365, "r_y1": 335.993, "r_x2": 286.365, "r_y2": 327.441, "r_x3": 50.112, "r_y3": 327.441, "coord_origin": "TOPLEFT"}, "text": "extracted much faster and with higher accuracy compared", "orig": "extracted much faster and with higher accuracy compared", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.948, "r_x1": 286.365, "r_y1": 347.948, "r_x2": 286.365, "r_y2": 339.396, "r_x3": 50.112, "r_y3": 339.396, "coord_origin": "TOPLEFT"}, "text": "to OCR methods. Last but not least, we want to have a di-", "orig": "to OCR methods. 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"coord_origin": "TOPLEFT"}, "text": "PubTabNet", "orig": "PubTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.277, "r_y0": 614.2239999999999, "r_x1": 286.365, "r_y1": 614.2239999999999, "r_x2": 286.365, "r_y2": 605.672, "r_x3": 267.277, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "[37],", "orig": "[37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 626.179, "r_x1": 135.521, "r_y1": 626.179, "r_x2": 135.521, "r_y2": 617.627, "r_x3": 70.037, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36],", "orig": "FinTabNet [36],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.925, "r_y0": 626.179, "r_x1": 286.365, "r_y1": 626.179, "r_x2": 286.365, "r_y2": 617.627, "r_x3": 139.925, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "and TableBank [17] with generated", "orig": "and TableBank [17] with generated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 638.135, "r_x1": 198.056, "r_y1": 638.135, "r_x2": 198.056, "r_y2": 629.583, "r_x3": 70.037, "r_y3": 629.583, "coord_origin": "TOPLEFT"}, "text": "ground-truth for reproducibility.", "orig": "ground-truth for reproducibility.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 650.867, "r": 286.365, "b": 695.284, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 659.419, "r_x1": 204.452, "r_y1": 659.419, "r_x2": 204.452, "r_y2": 650.867, "r_x3": 62.067, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.833, "r_y0": 671.374, "r_x1": 286.365, "r_y1": 671.374, "r_x2": 286.365, "r_y2": 662.822, "r_x3": 246.833, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "In Sec. 3,", "orig": "In Sec. 3,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 683.329, "r_x1": 227.396, "r_y1": 683.329, "r_x2": 227.396, "r_y2": 674.777, "r_x3": 50.112, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "we describe the datasets on which we train.", "orig": "we describe the datasets on which we train.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 232.467, "r_y0": 683.329, "r_x1": 286.365, "r_y1": 683.329, "r_x2": 286.365, "r_y2": 674.777, "r_x3": 232.467, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "In Sec. 4, we", "orig": "In Sec. 4, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 695.284, "r_x1": 286.365, "r_y1": 695.284, "r_x2": 286.365, "r_y2": 686.732, "r_x3": 50.112, "r_y3": 686.732, "coord_origin": "TOPLEFT"}, "text": "introduce the TableFormer model-architecture and describe", "orig": "introduce the TableFormer model-architecture and describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "footnote", "bbox": {"l": 60.971, "t": 704.296, "r": 183.731, "b": 712.653, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 60.971, "r_y0": 709.427, "r_x1": 63.96, "r_y1": 709.427, "r_x2": 63.96, "r_y2": 704.296, "r_x3": 60.971, "r_y3": 704.296, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.458, "r_y0": 712.653, "r_x1": 183.731, "r_y1": 712.653, "r_x2": 183.731, "r_y2": 705.811, "r_x3": 64.458, "r_y3": 705.811, "coord_origin": "TOPLEFT"}, "text": "https://github.com/IBM/SynthTabNet", "orig": "https://github.com/IBM/SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.49, "r": 545.115, "b": 330.369, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", 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492.682, "r_y0": 485.918, "r_x1": 545.115, "r_y1": 485.918, "r_x2": 545.115, "r_y2": 477.366, "r_x3": 492.682, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "HTML table", "orig": "HTML table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.873, "r_x1": 459.218, "r_y1": 497.873, "r_x2": 459.218, "r_y2": 489.321, "r_x3": 308.862, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "tags [37, 17] or LaTeX symbols[10].", "orig": "tags [37, 17] or LaTeX symbols[10].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.115, "r_y0": 497.873, "r_x1": 545.115, "r_y1": 497.873, "r_x2": 545.115, "r_y2": 489.321, "r_x3": 465.115, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "The choice of sym-", "orig": "The choice of sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.828, "r_x1": 545.115, "r_y1": 509.828, "r_x2": 545.115, "r_y2": 501.276, "r_x3": 308.862, "r_y3": 501.276, "coord_origin": "TOPLEFT"}, "text": "bols is ultimately not very important, since one can be trans-", "orig": "bols is ultimately not very important, since one can be trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.784, "r_x1": 396.692, "r_y1": 521.784, "r_x2": 396.692, "r_y2": 513.232, "r_x3": 308.862, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "formed into the other.", "orig": "formed into the other.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.265, "r_y0": 521.784, "r_x1": 545.115, "r_y1": 521.784, "r_x2": 545.115, "r_y2": 513.232, "r_x3": 401.265, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "There are however subtle variations", "orig": "There are however subtle variations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.739, "r_x1": 433.484, "r_y1": 533.739, "r_x2": 433.484, "r_y2": 525.187, "r_x3": 308.862, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "in the Image-to-Text networks.", "orig": "in the Image-to-Text networks.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 438.187, "r_y0": 533.739, "r_x1": 545.115, "r_y1": 533.739, "r_x2": 545.115, "r_y2": 525.187, "r_x3": 438.187, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "The easiest network archi-", "orig": "The easiest network archi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.694, "r_x1": 420.941, "r_y1": 545.694, "r_x2": 420.941, "r_y2": 537.142, "r_x3": 308.862, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "tectures are 'image-encoder", "orig": "tectures are 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.595, "r_y0": 545.388, "r_x1": 433.558, "r_y1": 545.388, "r_x2": 433.558, "r_y2": 536.8240000000001, "r_x3": 423.595, "r_y3": 536.8240000000001, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.212, "r_y0": 545.694, "r_x1": 545.113, "r_y1": 545.694, "r_x2": 545.113, "r_y2": 537.142, "r_x3": 436.212, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "text-decoder' (IETD), sim-", "orig": "text-decoder' (IETD), sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.649, "r_x1": 545.115, "r_y1": 557.649, "r_x2": 545.115, "r_y2": 549.097, "r_x3": 308.862, "r_y3": 549.097, "coord_origin": "TOPLEFT"}, "text": "ilar to network architectures that try to provide captions to", "orig": "ilar to network architectures that try to provide captions to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.604, "r_x1": 545.115, "r_y1": 569.604, "r_x2": 545.115, "r_y2": 561.052, "r_x3": 308.862, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "images [32]. In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, 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architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 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99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 120.34699999999998, "r_x1": 74.75, "r_y1": 120.34699999999998, "r_x2": 74.75, "r_y2": 111.79499999999996, "r_x3": 50.112, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "sition.", "orig": "sition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.335, "r_y0": 120.34699999999998, "r_x1": 286.365, "r_y1": 120.34699999999998, "r_x2": 286.365, "r_y2": 111.79499999999996, "r_x3": 81.335, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "The latter is a long standing problem in the com-", "orig": "The latter is a long 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144.25699999999995, "r_x1": 286.365, "r_y1": 144.25699999999995, "r_x2": 286.365, "r_y2": 135.70500000000004, "r_x3": 50.112, "r_y3": 135.70500000000004, "coord_origin": "TOPLEFT"}, "text": "the table-location problem, there are no commonly used ap-", "orig": "the table-location problem, there are no commonly used ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 156.21299999999997, "r_x1": 286.365, "r_y1": 156.21299999999997, "r_x2": 286.365, "r_y2": 147.66099999999994, "r_x3": 50.112, "r_y3": 147.66099999999994, "coord_origin": "TOPLEFT"}, "text": "proaches that can easily be re-purposed to solve this prob-", "orig": "proaches that can easily be re-purposed to solve this prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 168.168, "r_x1": 286.365, 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Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.79999999999995, "r": 286.365, "b": 371.859, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.172, "r_x1": 286.365, "r_y1": 288.172, "r_x2": 286.365, "r_y2": 279.62, "r_x3": 50.112, "r_y3": 279.62, "coord_origin": "TOPLEFT"}, "text": "we can obtain the structure of any table, irregardless of the", "orig": "we can obtain the structure of any table, irregardless of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.128, "r_x1": 88.568, "r_y1": 300.128, "r_x2": 88.568, "r_y2": 291.576, "r_x3": 50.112, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "language.", "orig": "language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.502, "r_y0": 300.128, "r_x1": 286.365, "r_y1": 300.128, "r_x2": 286.365, "r_y2": 291.576, "r_x3": 95.502, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "Second, we want our algorithm to leverage as", "orig": "Second, we want our algorithm to leverage as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.083, "r_x1": 286.365, "r_y1": 312.083, "r_x2": 286.365, "r_y2": 303.531, "r_x3": 50.112, "r_y3": 303.531, "coord_origin": "TOPLEFT"}, "text": "much data as possible from the original PDF document. For", "orig": "much data as possible from the original PDF document. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.038, "r_x1": 286.365, "r_y1": 324.038, "r_x2": 286.365, "r_y2": 315.486, "r_x3": 50.112, "r_y3": 315.486, "coord_origin": "TOPLEFT"}, "text": "programmatic PDF documents, the text-cells can often be", "orig": "programmatic PDF documents, the text-cells can often be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.993, "r_x1": 286.365, "r_y1": 335.993, "r_x2": 286.365, "r_y2": 327.441, "r_x3": 50.112, "r_y3": 327.441, "coord_origin": "TOPLEFT"}, "text": "extracted much faster and with higher accuracy compared", "orig": "extracted much faster and with higher accuracy compared", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.948, "r_x1": 286.365, "r_y1": 347.948, "r_x2": 286.365, "r_y2": 339.396, "r_x3": 50.112, "r_y3": 339.396, "coord_origin": "TOPLEFT"}, "text": "to OCR methods. Last but not least, we want to have a di-", "orig": "to OCR methods. Last but not least, we want to have a di-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.904, "r_x1": 286.365, "r_y1": 359.904, "r_x2": 286.365, "r_y2": 351.352, "r_x3": 50.112, "r_y3": 351.352, "coord_origin": "TOPLEFT"}, "text": "rect link between the table-cell and its bounding box in the", "orig": "rect link between the table-cell and its bounding box in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.859, "r_x1": 76.951, "r_y1": 371.859, "r_x2": 76.951, "r_y2": 363.307, "r_x3": 50.112, "r_y3": 363.307, "coord_origin": "TOPLEFT"}, "text": "image.", "orig": "image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.715, "r": 286.367, "b": 432.087, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 396.222, "r_x1": 120.986, "r_y1": 396.222, "r_x2": 120.986, "r_y2": 387.67, "r_x3": 50.112, "r_y3": 387.67, "coord_origin": "TOPLEFT"}, "text": "new model called", "orig": "new model called", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.901, "r_y0": 396.238, "r_x1": 179.731, "r_y1": 396.238, "r_x2": 179.731, "r_y2": 387.282, "r_x3": 123.901, "r_y3": 387.282, "coord_origin": "TOPLEFT"}, "text": "TableFormer", "orig": "TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.646, "r_y0": 396.222, "r_x1": 286.367, "r_y1": 396.222, "r_x2": 286.367, "r_y2": 387.67, "r_x3": 182.646, "r_y3": 387.67, "coord_origin": "TOPLEFT"}, "text": "and a synthetically gener-", "orig": "and a synthetically gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 408.177, "r_x1": 181.758, "r_y1": 408.177, "r_x2": 181.758, "r_y2": 399.625, "r_x3": 50.112, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "ated table structure dataset called", "orig": "ated table structure dataset called", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.104, "r_y0": 408.193, "r_x1": 240.203, "r_y1": 408.193, "r_x2": 240.203, "r_y2": 399.237, "r_x3": 184.104, "r_y3": 399.237, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.204, "r_y0": 403.942, "r_x1": 243.691, "r_y1": 403.942, "r_x2": 243.691, "r_y2": 397.956, "r_x3": 240.204, "r_y3": 397.956, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 244.189, "r_y0": 408.177, "r_x1": 246.68, "r_y1": 408.177, "r_x2": 246.68, "r_y2": 399.625, "r_x3": 244.189, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 249.718, "r_y0": 408.177, "r_x1": 286.361, "r_y1": 408.177, "r_x2": 286.361, "r_y2": 399.625, "r_x3": 249.718, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "In partic-", "orig": "In partic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.132, "r_x1": 67.696, "r_y1": 420.132, "r_x2": 67.696, "r_y2": 411.58, "r_x3": 50.112, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "ular,", "orig": "ular,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.322, "r_y0": 420.132, "r_x1": 286.365, "r_y1": 420.132, "r_x2": 286.365, "r_y2": 411.58, "r_x3": 71.322, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "our contributions in this work can be summarised as", "orig": "our contributions in this work can be summarised as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.087, "r_x1": 82.52, "r_y1": 432.087, "r_x2": 82.52, "r_y2": 423.535, "r_x3": 50.112, "r_y3": 423.535, "coord_origin": "TOPLEFT"}, "text": "follows:", "orig": "follows:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569, "t": 444.432, "r": 286.365, "b": 489.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 453.372, "r_x1": 65.056, "r_y1": 453.372, "r_x2": 65.056, "r_y2": 444.82, "r_x3": 61.569, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 453.372, "r_x1": 117.101, "r_y1": 453.372, "r_x2": 117.101, "r_y2": 444.82, "r_x3": 70.037, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "We propose", "orig": "We propose", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, 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"r_y2": 526.3330000000001, "r_x3": 70.037, "r_y3": 526.3330000000001, "coord_origin": "TOPLEFT"}, "text": "while being much more efficient in training and infer-", "orig": "while being much more efficient in training and infer-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 546.84, "r_x1": 161.653, "r_y1": 546.84, "r_x2": 161.653, "r_y2": 538.288, "r_x3": 70.037, "r_y3": 538.288, "coord_origin": "TOPLEFT"}, "text": "ence to existing works.", "orig": "ence to existing works.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 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present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569, "t": 605.672, "r": 286.365, "b": 638.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 614.2239999999999, "r_x1": 65.056, "r_y1": 614.2239999999999, "r_x2": 65.056, "r_y2": 605.672, "r_x3": 61.569, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 614.2239999999999, "r_x1": 132.005, "r_y1": 614.2239999999999, "r_x2": 132.005, "r_y2": 605.672, "r_x3": 70.037, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "An augmented", "orig": "An augmented", 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"r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.617, "r_y0": 614.2239999999999, "r_x1": 261.2, "r_y1": 614.2239999999999, "r_x2": 261.2, "r_y2": 605.672, "r_x3": 216.617, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "PubTabNet", "orig": "PubTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.277, "r_y0": 614.2239999999999, "r_x1": 286.365, "r_y1": 614.2239999999999, "r_x2": 286.365, "r_y2": 605.672, "r_x3": 267.277, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "[37],", "orig": "[37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 626.179, "r_x1": 135.521, "r_y1": 626.179, "r_x2": 135.521, "r_y2": 617.627, "r_x3": 70.037, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36],", "orig": "FinTabNet [36],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.925, "r_y0": 626.179, "r_x1": 286.365, "r_y1": 626.179, "r_x2": 286.365, "r_y2": 617.627, "r_x3": 139.925, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "and TableBank [17] with generated", "orig": "and TableBank [17] with generated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 638.135, "r_x1": 198.056, "r_y1": 638.135, "r_x2": 198.056, "r_y2": 629.583, "r_x3": 70.037, "r_y3": 629.583, "coord_origin": "TOPLEFT"}, "text": "ground-truth for reproducibility.", "orig": "ground-truth for reproducibility.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 650.867, "r": 286.365, "b": 695.284, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 659.419, "r_x1": 204.452, "r_y1": 659.419, "r_x2": 204.452, "r_y2": 650.867, "r_x3": 62.067, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.833, "r_y0": 671.374, "r_x1": 286.365, "r_y1": 671.374, "r_x2": 286.365, "r_y2": 662.822, "r_x3": 246.833, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "In Sec. 3,", "orig": "In Sec. 3,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 683.329, "r_x1": 227.396, "r_y1": 683.329, "r_x2": 227.396, "r_y2": 674.777, "r_x3": 50.112, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "we describe the datasets on which we train.", "orig": "we describe the datasets on which we train.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 232.467, "r_y0": 683.329, "r_x1": 286.365, "r_y1": 683.329, "r_x2": 286.365, "r_y2": 674.777, "r_x3": 232.467, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "In Sec. 4, we", "orig": "In Sec. 4, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 695.284, "r_x1": 286.365, "r_y1": 695.284, "r_x2": 286.365, "r_y2": 686.732, "r_x3": 50.112, "r_y3": 686.732, "coord_origin": "TOPLEFT"}, "text": "introduce the TableFormer model-architecture and describe", "orig": "introduce the TableFormer model-architecture and describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.971, "t": 704.296, "r": 183.731, "b": 712.653, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 60.971, "r_y0": 709.427, "r_x1": 63.96, "r_y1": 709.427, "r_x2": 63.96, "r_y2": 704.296, "r_x3": 60.971, "r_y3": 704.296, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.458, "r_y0": 712.653, "r_x1": 183.731, "r_y1": 712.653, "r_x2": 183.731, "r_y2": 705.811, "r_x3": 64.458, "r_y3": 705.811, "coord_origin": "TOPLEFT"}, "text": "https://github.com/IBM/SynthTabNet", "orig": "https://github.com/IBM/SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.49, "r": 545.115, "b": 330.369, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.838, "r": 545.115, "b": 449.987, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.845, "r_y0": 342.39, "r_x1": 436.802, "r_y1": 342.39, "r_x2": 436.802, "r_y2": 333.838, "r_x3": 395.845, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "popularity", "orig": "popularity", 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"TOPLEFT"}, "text": "the community relied heavily on heuristic and/or statistical", "orig": "the community relied heavily on heuristic and/or statistical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 366.3, "r_x1": 545.115, "r_y1": 366.3, "r_x2": 545.115, "r_y2": 357.748, "r_x3": 308.862, "r_y3": 357.748, "coord_origin": "TOPLEFT"}, "text": "methods to do table structure identification [3, 7, 11, 5, 13,", "orig": "methods to do table structure identification [3, 7, 11, 5, 13,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 378.256, "r_x1": 324.633, "r_y1": 378.256, "r_x2": 324.633, "r_y2": 369.704, "r_x3": 308.862, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "28].", "orig": "28].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.634, "r_y0": 378.256, "r_x1": 545.115, "r_y1": 378.256, "r_x2": 545.115, "r_y2": 369.704, "r_x3": 329.634, "r_y3": 369.704, "coord_origin": "TOPLEFT"}, "text": "Although such methods work well on constrained ta-", "orig": "Although such methods work well on constrained ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 390.211, "r_x1": 545.115, "r_y1": 390.211, "r_x2": 545.115, "r_y2": 381.659, "r_x3": 308.862, "r_y3": 381.659, "coord_origin": "TOPLEFT"}, "text": "bles [12], a more data-driven approach can be applied due", "orig": "bles [12], a more data-driven approach can be applied due", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 402.166, 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best-of-our knowl-", "orig": "To the best-of-our knowl-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 426.076, "r_x1": 545.115, "r_y1": 426.076, "r_x2": 545.115, "r_y2": 417.524, "r_x3": 308.862, "r_y3": 417.524, "coord_origin": "TOPLEFT"}, "text": "edge, there are currently two different types of network ar-", "orig": "edge, there are currently two different types of network ar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 438.032, "r_x1": 545.115, "r_y1": 438.032, "r_x2": 545.115, "r_y2": 429.48, "r_x3": 308.862, "r_y3": 429.48, "coord_origin": "TOPLEFT"}, "text": "chitecture that are being pursued for state-of-the-art table-", "orig": "chitecture that are being pursued for state-of-the-art table-", "text_direction": "left_to_right", 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Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 453.068, "r": 545.117, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 462.024, "r_x1": 423.262, "r_y1": 462.024, "r_x2": 423.262, "r_y2": 453.068, "r_x3": 320.817, "r_y3": 453.068, "coord_origin": "TOPLEFT"}, "text": "Image-to-Text networks", "orig": "Image-to-Text networks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.267, "r_y0": 462.008, "r_x1": 426.037, "r_y1": 462.008, "r_x2": 426.037, "r_y2": 453.456, "r_x3": 423.267, "r_y3": 453.456, "coord_origin": "TOPLEFT"}, 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255}, "rect": {"r_x0": 492.682, "r_y0": 485.918, "r_x1": 545.115, "r_y1": 485.918, "r_x2": 545.115, "r_y2": 477.366, "r_x3": 492.682, "r_y3": 477.366, "coord_origin": "TOPLEFT"}, "text": "HTML table", "orig": "HTML table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.873, "r_x1": 459.218, "r_y1": 497.873, "r_x2": 459.218, "r_y2": 489.321, "r_x3": 308.862, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "tags [37, 17] or LaTeX symbols[10].", "orig": "tags [37, 17] or LaTeX symbols[10].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.115, "r_y0": 497.873, "r_x1": 545.115, "r_y1": 497.873, "r_x2": 545.115, "r_y2": 489.321, "r_x3": 465.115, "r_y3": 489.321, "coord_origin": "TOPLEFT"}, "text": "The choice of sym-", "orig": "The choice of sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.828, "r_x1": 545.115, "r_y1": 509.828, "r_x2": 545.115, "r_y2": 501.276, "r_x3": 308.862, "r_y3": 501.276, "coord_origin": "TOPLEFT"}, "text": "bols is ultimately not very important, since one can be trans-", "orig": "bols is ultimately not very important, since one can be trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.784, "r_x1": 396.692, "r_y1": 521.784, "r_x2": 396.692, "r_y2": 513.232, "r_x3": 308.862, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "formed into the other.", "orig": "formed into the other.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 401.265, "r_y0": 521.784, "r_x1": 545.115, "r_y1": 521.784, "r_x2": 545.115, "r_y2": 513.232, "r_x3": 401.265, "r_y3": 513.232, "coord_origin": "TOPLEFT"}, "text": "There are however subtle variations", "orig": "There are however subtle variations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.739, "r_x1": 433.484, "r_y1": 533.739, "r_x2": 433.484, "r_y2": 525.187, "r_x3": 308.862, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "in the Image-to-Text networks.", "orig": "in the Image-to-Text networks.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 438.187, "r_y0": 533.739, "r_x1": 545.115, "r_y1": 533.739, "r_x2": 545.115, "r_y2": 525.187, "r_x3": 438.187, "r_y3": 525.187, "coord_origin": "TOPLEFT"}, "text": "The easiest network archi-", "orig": "The easiest network archi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.694, "r_x1": 420.941, "r_y1": 545.694, "r_x2": 420.941, "r_y2": 537.142, "r_x3": 308.862, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "tectures are 'image-encoder", "orig": "tectures are 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.595, "r_y0": 545.388, "r_x1": 433.558, "r_y1": 545.388, "r_x2": 433.558, "r_y2": 536.8240000000001, "r_x3": 423.595, "r_y3": 536.8240000000001, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.212, "r_y0": 545.694, "r_x1": 545.113, "r_y1": 545.694, "r_x2": 545.113, "r_y2": 537.142, "r_x3": 436.212, "r_y3": 537.142, "coord_origin": "TOPLEFT"}, "text": "text-decoder' (IETD), sim-", "orig": "text-decoder' (IETD), sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.649, "r_x1": 545.115, "r_y1": 557.649, "r_x2": 545.115, "r_y2": 549.097, "r_x3": 308.862, "r_y3": 549.097, "coord_origin": "TOPLEFT"}, "text": "ilar to network architectures that try to provide captions to", "orig": "ilar to network architectures that try to provide captions to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.604, "r_x1": 545.115, "r_y1": 569.604, "r_x2": 545.115, "r_y2": 561.052, "r_x3": 308.862, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "images [32]. In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.045, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 364.045, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "to generate the textual content of each table", "orig": "to generate the textual content of each table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 325.739, "r_y1": 701.111, "r_x2": 325.739, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.746, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 331.746, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "The network architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder \u2192 text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder \u2192 dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}, {"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}], "body": [{"label": "text", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 95.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9656890034675598, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, 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"text", "bbox": {"l": 50.112, "t": 99.84000000000003, "r": 286.365, "b": 227.94399999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9838606119155884, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 108.39200000000005, "r_x1": 286.365, "r_y1": 108.39200000000005, "r_x2": 286.365, "r_y2": 99.84000000000003, "r_x3": 62.067, "r_y3": 99.84000000000003, "coord_origin": "TOPLEFT"}, "text": "The second problem is called table-structure decompo-", "orig": "The second problem is called table-structure decompo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 120.34699999999998, "r_x1": 74.75, "r_y1": 120.34699999999998, "r_x2": 74.75, "r_y2": 111.79499999999996, "r_x3": 50.112, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "sition.", "orig": "sition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.335, "r_y0": 120.34699999999998, "r_x1": 286.365, "r_y1": 120.34699999999998, "r_x2": 286.365, "r_y2": 111.79499999999996, "r_x3": 81.335, "r_y3": 111.79499999999996, "coord_origin": "TOPLEFT"}, "text": "The latter is a long standing problem in the com-", "orig": "The latter is a long standing problem in the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 132.30200000000002, "r_x1": 235.117, "r_y1": 132.30200000000002, "r_x2": 235.117, "r_y2": 123.75, "r_x3": 50.112, "r_y3": 123.75, "coord_origin": "TOPLEFT"}, "text": "munity of document understanding [6, 4, 14].", "orig": "munity of document understanding [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"r_y3": 147.66099999999994, "coord_origin": "TOPLEFT"}, "text": "proaches that can easily be re-purposed to solve this prob-", "orig": "proaches that can easily be re-purposed to solve this prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 168.168, "r_x1": 286.365, "r_y1": 168.168, "r_x2": 286.365, "r_y2": 159.61599999999999, "r_x3": 50.112, "r_y3": 159.61599999999999, "coord_origin": "TOPLEFT"}, "text": "lem. Lately, a set of new model-architectures has been pro-", "orig": "lem. Lately, a set of new model-architectures has been pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 180.12300000000005, "r_x1": 286.365, "r_y1": 180.12300000000005, "r_x2": 286.365, "r_y2": 171.57100000000003, "r_x3": 50.112, "r_y3": 171.57100000000003, "coord_origin": "TOPLEFT"}, "text": "posed by the community to address table-structure decom-", "orig": "posed by the community to address table-structure decom-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.07799999999997, "r_x1": 286.365, "r_y1": 192.07799999999997, "r_x2": 286.365, "r_y2": 183.52599999999995, "r_x3": 50.112, "r_y3": 183.52599999999995, "coord_origin": "TOPLEFT"}, "text": "position [37, 36, 18, 20]. All these models have some weak-", "orig": "position [37, 36, 18, 20]. All these models have some weak-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.03300000000002, "r_x1": 114.51, "r_y1": 204.03300000000002, "r_x2": 114.51, "r_y2": 195.481, "r_x3": 50.112, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "nesses (see Sec.", "orig": "nesses (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.163, "r_y0": 204.03300000000002, "r_x1": 129.952, "r_y1": 204.03300000000002, "r_x2": 129.952, "r_y2": 195.481, "r_x3": 119.163, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "2).", "orig": "2).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.615, "r_y0": 204.03300000000002, "r_x1": 286.365, "r_y1": 204.03300000000002, "r_x2": 286.365, "r_y2": 195.481, "r_x3": 134.615, "r_y3": 195.481, "coord_origin": "TOPLEFT"}, "text": "The common denominator here is the", "orig": "The common denominator here is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.98800000000006, "r_x1": 286.365, "r_y1": 215.98800000000006, "r_x2": 286.365, "r_y2": 207.43600000000004, "r_x3": 50.112, "r_y3": 207.43600000000004, "coord_origin": "TOPLEFT"}, "text": "reliance on textual features and/or the inability to provide", "orig": "reliance on textual features and/or the inability to provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.94399999999996, "r_x1": 278.664, "r_y1": 227.94399999999996, "r_x2": 278.664, "r_y2": 219.39200000000005, "r_x3": 50.112, "r_y3": 219.39200000000005, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table-cell in the original image.", "orig": "the bounding box of each table-cell in the original image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image."}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 231.79999999999995, "r": 286.365, "b": 371.859, "coord_origin": "TOPLEFT"}, "confidence": 0.9855920076370239, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 240.35199999999998, "r_x1": 286.365, "r_y1": 240.35199999999998, "r_x2": 286.365, "r_y2": 231.79999999999995, "r_x3": 62.067, "r_y3": 231.79999999999995, "coord_origin": "TOPLEFT"}, "text": "In this paper, we want to address these weaknesses and", "orig": "In this paper, we want to address these weaknesses and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 252.30700000000002, "r_x1": 78.884, "r_y1": 252.30700000000002, "r_x2": 78.884, "r_y2": 243.755, "r_x3": 50.112, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "present", "orig": "present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.068, "r_y0": 252.30700000000002, "r_x1": 116.373, "r_y1": 252.30700000000002, "r_x2": 116.373, "r_y2": 243.755, "r_x3": 83.068, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "a robust", "orig": "a robust", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.558, "r_y0": 252.30700000000002, "r_x1": 178.102, "r_y1": 252.30700000000002, "r_x2": 178.102, "r_y2": 243.755, "r_x3": 120.558, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "table-structure", "orig": "table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.276, "r_y0": 252.30700000000002, "r_x1": 286.365, "r_y1": 252.30700000000002, "r_x2": 286.365, "r_y2": 243.755, "r_x3": 182.276, "r_y3": 243.755, "coord_origin": "TOPLEFT"}, "text": "decomposition algorithm.", "orig": "decomposition algorithm.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.26199999999994, "r_x1": 260.432, "r_y1": 264.26199999999994, "r_x2": 260.432, "r_y2": 255.71000000000004, "r_x3": 50.112, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "The design criteria for our model are the following.", "orig": "The design criteria for our model are the following.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.603, "r_y0": 264.26199999999994, "r_x1": 286.365, "r_y1": 264.26199999999994, "r_x2": 286.365, "r_y2": 255.71000000000004, "r_x3": 265.603, "r_y3": 255.71000000000004, "coord_origin": "TOPLEFT"}, "text": "First,", "orig": "First,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.217, "r_x1": 286.365, "r_y1": 276.217, "r_x2": 286.365, "r_y2": 267.66499999999996, "r_x3": 50.112, "r_y3": 267.66499999999996, "coord_origin": "TOPLEFT"}, "text": "we want our algorithm to be language agnostic. In this way,", "orig": "we want our algorithm to be language agnostic. In this way,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.172, "r_x1": 286.365, "r_y1": 288.172, "r_x2": 286.365, "r_y2": 279.62, "r_x3": 50.112, "r_y3": 279.62, "coord_origin": "TOPLEFT"}, "text": "we can obtain the structure of any table, irregardless of the", "orig": "we can obtain the structure of any table, irregardless of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.128, "r_x1": 88.568, "r_y1": 300.128, "r_x2": 88.568, "r_y2": 291.576, "r_x3": 50.112, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "language.", "orig": "language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.502, "r_y0": 300.128, "r_x1": 286.365, "r_y1": 300.128, "r_x2": 286.365, "r_y2": 291.576, "r_x3": 95.502, "r_y3": 291.576, "coord_origin": "TOPLEFT"}, "text": "Second, we want our algorithm to leverage as", "orig": "Second, we want our algorithm to leverage as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.083, "r_x1": 286.365, "r_y1": 312.083, "r_x2": 286.365, "r_y2": 303.531, "r_x3": 50.112, "r_y3": 303.531, "coord_origin": "TOPLEFT"}, "text": "much data as possible from the original PDF document. For", "orig": "much data as possible from the original PDF document. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.038, "r_x1": 286.365, "r_y1": 324.038, "r_x2": 286.365, "r_y2": 315.486, "r_x3": 50.112, "r_y3": 315.486, "coord_origin": "TOPLEFT"}, "text": "programmatic PDF documents, the text-cells can often be", "orig": "programmatic PDF documents, the text-cells can often be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.993, "r_x1": 286.365, "r_y1": 335.993, "r_x2": 286.365, "r_y2": 327.441, "r_x3": 50.112, "r_y3": 327.441, "coord_origin": "TOPLEFT"}, "text": "extracted much faster and with higher accuracy compared", "orig": "extracted much faster and with higher accuracy compared", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.948, "r_x1": 286.365, "r_y1": 347.948, "r_x2": 286.365, "r_y2": 339.396, "r_x3": 50.112, "r_y3": 339.396, "coord_origin": "TOPLEFT"}, "text": "to OCR methods. Last but not least, we want to have a di-", "orig": "to OCR methods. Last but not least, we want to have a di-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.904, "r_x1": 286.365, "r_y1": 359.904, "r_x2": 286.365, "r_y2": 351.352, "r_x3": 50.112, "r_y3": 351.352, "coord_origin": "TOPLEFT"}, "text": "rect link between the table-cell and its bounding box in the", "orig": "rect link between the table-cell and its bounding box in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.859, "r_x1": 76.951, "r_y1": 371.859, "r_x2": 76.951, "r_y2": 363.307, "r_x3": 50.112, "r_y3": 363.307, "coord_origin": "TOPLEFT"}, "text": "image.", "orig": "image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 375.715, "r": 286.367, "b": 432.087, "coord_origin": "TOPLEFT"}, "confidence": 0.9820111393928528, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 384.267, "r_x1": 286.365, "r_y1": 384.267, "r_x2": 286.365, "r_y2": 375.715, "r_x3": 62.067, "r_y3": 375.715, "coord_origin": "TOPLEFT"}, "text": "To meet the design criteria listed above, we developed a", "orig": "To meet the design criteria listed above, we developed a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 396.222, "r_x1": 120.986, "r_y1": 396.222, "r_x2": 120.986, "r_y2": 387.67, "r_x3": 50.112, "r_y3": 387.67, "coord_origin": "TOPLEFT"}, "text": "new model called", "orig": "new model called", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.901, "r_y0": 396.238, "r_x1": 179.731, "r_y1": 396.238, "r_x2": 179.731, "r_y2": 387.282, "r_x3": 123.901, "r_y3": 387.282, "coord_origin": "TOPLEFT"}, "text": "TableFormer", "orig": "TableFormer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.646, "r_y0": 396.222, "r_x1": 286.367, "r_y1": 396.222, "r_x2": 286.367, "r_y2": 387.67, "r_x3": 182.646, "r_y3": 387.67, "coord_origin": "TOPLEFT"}, "text": "and a synthetically gener-", "orig": "and a synthetically gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 408.177, "r_x1": 181.758, "r_y1": 408.177, "r_x2": 181.758, "r_y2": 399.625, "r_x3": 50.112, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "ated table structure dataset called", "orig": "ated table structure dataset called", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.104, "r_y0": 408.193, "r_x1": 240.203, "r_y1": 408.193, "r_x2": 240.203, "r_y2": 399.237, "r_x3": 184.104, "r_y3": 399.237, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.204, "r_y0": 403.942, "r_x1": 243.691, "r_y1": 403.942, "r_x2": 243.691, "r_y2": 397.956, "r_x3": 240.204, "r_y3": 397.956, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 244.189, "r_y0": 408.177, "r_x1": 246.68, "r_y1": 408.177, "r_x2": 246.68, "r_y2": 399.625, "r_x3": 244.189, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 249.718, "r_y0": 408.177, "r_x1": 286.361, "r_y1": 408.177, "r_x2": 286.361, "r_y2": 399.625, "r_x3": 249.718, "r_y3": 399.625, "coord_origin": "TOPLEFT"}, "text": "In partic-", "orig": "In partic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.132, "r_x1": 67.696, "r_y1": 420.132, "r_x2": 67.696, "r_y2": 411.58, "r_x3": 50.112, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "ular,", "orig": "ular,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.322, "r_y0": 420.132, "r_x1": 286.365, "r_y1": 420.132, "r_x2": 286.365, "r_y2": 411.58, "r_x3": 71.322, "r_y3": 411.58, "coord_origin": "TOPLEFT"}, "text": "our contributions in this work can be summarised as", "orig": "our contributions in this work can be summarised as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.087, "r_x1": 82.52, "r_y1": 432.087, "r_x2": 82.52, "r_y2": 423.535, "r_x3": 50.112, "r_y3": 423.535, "coord_origin": "TOPLEFT"}, "text": "follows:", "orig": "follows:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows:"}, {"label": "list_item", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 61.569, "t": 444.432, "r": 286.365, "b": 489.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9822162389755249, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 453.372, "r_x1": 65.056, "r_y1": 453.372, "r_x2": 65.056, "r_y2": 444.82, "r_x3": 61.569, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 453.372, "r_x1": 117.101, "r_y1": 453.372, "r_x2": 117.101, "r_y2": 444.82, "r_x3": 70.037, "r_y3": 444.82, "coord_origin": "TOPLEFT"}, "text": "We propose", "orig": "We propose", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, 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boxes for", "orig": "that predicts tables structure and bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 477.282, "r_x1": 286.365, "r_y1": 477.282, "r_x2": 286.365, "r_y2": 468.73, "r_x3": 70.037, "r_y3": 468.73, "coord_origin": "TOPLEFT"}, "text": "the table content simultaneously in an end-to-end ap-", "orig": "the table content simultaneously in an end-to-end ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 489.237, "r_x1": 99.636, "r_y1": 489.237, "r_x2": 99.636, "r_y2": 480.685, "r_x3": 70.037, "r_y3": 480.685, "coord_origin": "TOPLEFT"}, "text": "proach.", "orig": "proach.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach."}, {"label": "list_item", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 61.569, "t": 502.034, "r": 286.365, "b": 546.84, "coord_origin": "TOPLEFT"}, "confidence": 0.982271671295166, "cells": [{"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 510.974, "r_x1": 65.056, "r_y1": 510.974, "r_x2": 65.056, "r_y2": 502.422, "r_x3": 61.569, "r_y3": 502.422, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 510.974, "r_x1": 111.88, "r_y1": 510.974, "r_x2": 111.88, "r_y2": 502.422, "r_x3": 70.037, "r_y3": 502.422, "coord_origin": "TOPLEFT"}, "text": "Across all", "orig": "Across all", "text_direction": "left_to_right", 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"orig": "signif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 522.929, "r_x1": 97.155, "r_y1": 522.929, "r_x2": 97.155, "r_y2": 514.377, "r_x3": 70.037, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "icantly", "orig": "icantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.678, "r_y0": 522.929, "r_x1": 150.375, "r_y1": 522.929, "r_x2": 150.375, "r_y2": 514.377, "r_x3": 101.678, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "outperforms", "orig": "outperforms", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.898, "r_y0": 522.929, "r_x1": 186.301, "r_y1": 522.929, "r_x2": 186.301, "r_y2": 514.377, "r_x3": 154.898, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "existing", "orig": "existing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.824, "r_y0": 522.929, "r_x1": 250.021, "r_y1": 522.929, "r_x2": 250.021, "r_y2": 514.377, "r_x3": 190.824, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 254.544, "r_y0": 522.929, "r_x1": 286.365, "r_y1": 522.929, "r_x2": 286.365, "r_y2": 514.377, "r_x3": 254.544, "r_y3": 514.377, "coord_origin": "TOPLEFT"}, "text": "metrics,", "orig": "metrics,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 534.885, "r_x1": 286.365, "r_y1": 534.885, "r_x2": 286.365, "r_y2": 526.3330000000001, "r_x3": 70.037, "r_y3": 526.3330000000001, "coord_origin": "TOPLEFT"}, "text": "while being much more efficient in training and infer-", "orig": "while being much more efficient in training and infer-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 546.84, "r_x1": 161.653, "r_y1": 546.84, "r_x2": 161.653, "r_y2": 538.288, "r_x3": 70.037, "r_y3": 538.288, "coord_origin": "TOPLEFT"}, "text": "ence to existing works.", "orig": "ence to existing works.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works."}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 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559.637, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.553, "r_y0": 568.577, "r_x1": 186.976, "r_y1": 568.577, "r_x2": 186.976, "r_y2": 560.025, "r_x3": 182.553, "r_y3": 560.025, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.848, "r_y0": 568.577, "r_x1": 242.767, "r_y1": 568.577, "r_x2": 242.767, "r_y2": 560.025, "r_x3": 191.848, "r_y3": 560.025, "coord_origin": "TOPLEFT"}, "text": "synthetically", "orig": "synthetically", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.639, "r_y0": 568.577, "r_x1": 286.363, "r_y1": 568.577, "r_x2": 286.363, "r_y2": 560.025, "r_x3": 247.639, "r_y3": 560.025, "coord_origin": "TOPLEFT"}, "text": "generated", "orig": "generated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 580.532, "r_x1": 286.365, "r_y1": 580.532, "r_x2": 286.365, "r_y2": 571.98, "r_x3": 70.037, "r_y3": 571.98, "coord_origin": "TOPLEFT"}, "text": "dataset, with various appearance styles and complex-", "orig": "dataset, with various appearance styles and complex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 592.487, "r_x1": 82.401, "r_y1": 592.487, "r_x2": 82.401, "r_y2": 583.935, "r_x3": 70.037, "r_y3": 583.935, "coord_origin": "TOPLEFT"}, "text": "ity.", "orig": "ity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity."}, {"label": "list_item", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 61.569, "t": 605.672, "r": 286.365, "b": 638.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9806395173072815, "cells": [{"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 614.2239999999999, "r_x1": 65.056, "r_y1": 614.2239999999999, "r_x2": 65.056, "r_y2": 605.672, "r_x3": 61.569, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 614.2239999999999, "r_x1": 132.005, "r_y1": 614.2239999999999, "r_x2": 132.005, "r_y2": 605.672, "r_x3": 70.037, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "An augmented", "orig": "An augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.082, "r_y0": 614.2239999999999, "r_x1": 165.748, "r_y1": 614.2239999999999, "r_x2": 165.748, "r_y2": 605.672, "r_x3": 138.082, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "dataset", "orig": "dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.815, "r_y0": 614.2239999999999, "r_x1": 194.5, "r_y1": 614.2239999999999, "r_x2": 194.5, "r_y2": 605.672, "r_x3": 171.815, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "based", "orig": "based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.577, "r_y0": 614.2239999999999, "r_x1": 210.54, "r_y1": 614.2239999999999, "r_x2": 210.54, "r_y2": 605.672, "r_x3": 200.577, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.617, "r_y0": 614.2239999999999, "r_x1": 261.2, "r_y1": 614.2239999999999, "r_x2": 261.2, "r_y2": 605.672, "r_x3": 216.617, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "PubTabNet", "orig": "PubTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.277, "r_y0": 614.2239999999999, "r_x1": 286.365, "r_y1": 614.2239999999999, "r_x2": 286.365, "r_y2": 605.672, "r_x3": 267.277, "r_y3": 605.672, "coord_origin": "TOPLEFT"}, "text": "[37],", "orig": "[37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 626.179, "r_x1": 135.521, "r_y1": 626.179, "r_x2": 135.521, "r_y2": 617.627, "r_x3": 70.037, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36],", "orig": "FinTabNet [36],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.925, "r_y0": 626.179, "r_x1": 286.365, "r_y1": 626.179, "r_x2": 286.365, "r_y2": 617.627, "r_x3": 139.925, "r_y3": 617.627, "coord_origin": "TOPLEFT"}, "text": "and TableBank [17] with generated", "orig": "and TableBank [17] with generated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 638.135, "r_x1": 198.056, "r_y1": 638.135, "r_x2": 198.056, "r_y2": 629.583, "r_x3": 70.037, "r_y3": 629.583, "coord_origin": "TOPLEFT"}, "text": "ground-truth for reproducibility.", "orig": "ground-truth for reproducibility.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility."}, {"label": "text", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 650.867, "r": 286.365, "b": 695.284, "coord_origin": "TOPLEFT"}, "confidence": 0.9742533564567566, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 659.419, "r_x1": 204.452, "r_y1": 659.419, "r_x2": 204.452, "r_y2": 650.867, "r_x3": 62.067, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows.", "orig": "The paper is structured as follows.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.789, "r_y0": 659.419, "r_x1": 286.365, "r_y1": 659.419, "r_x2": 286.365, "r_y2": 650.867, "r_x3": 210.789, "r_y3": 650.867, "coord_origin": "TOPLEFT"}, "text": "In Sec. 2, we give", "orig": "In Sec. 2, we give", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 671.374, "r_x1": 240.896, "r_y1": 671.374, "r_x2": 240.896, "r_y2": 662.822, "r_x3": 50.112, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "a brief overview of the current state-of-the-art.", "orig": "a brief overview of the current state-of-the-art.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.833, "r_y0": 671.374, "r_x1": 286.365, "r_y1": 671.374, "r_x2": 286.365, "r_y2": 662.822, "r_x3": 246.833, "r_y3": 662.822, "coord_origin": "TOPLEFT"}, "text": "In Sec. 3,", "orig": "In Sec. 3,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 683.329, "r_x1": 227.396, "r_y1": 683.329, "r_x2": 227.396, "r_y2": 674.777, "r_x3": 50.112, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "we describe the datasets on which we train.", "orig": "we describe the datasets on which we train.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 232.467, "r_y0": 683.329, "r_x1": 286.365, "r_y1": 683.329, "r_x2": 286.365, "r_y2": 674.777, "r_x3": 232.467, "r_y3": 674.777, "coord_origin": "TOPLEFT"}, "text": "In Sec. 4, we", "orig": "In Sec. 4, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 695.284, "r_x1": 286.365, "r_y1": 695.284, "r_x2": 286.365, "r_y2": 686.732, "r_x3": 50.112, "r_y3": 686.732, "coord_origin": "TOPLEFT"}, "text": "introduce the TableFormer model-architecture and describe", "orig": "introduce the TableFormer model-architecture and describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe"}, {"label": "footnote", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "footnote", "bbox": {"l": 60.971, "t": 704.296, "r": 183.731, "b": 712.653, "coord_origin": "TOPLEFT"}, "confidence": 0.895367443561554, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 60.971, "r_y0": 709.427, "r_x1": 63.96, "r_y1": 709.427, "r_x2": 63.96, "r_y2": 704.296, "r_x3": 60.971, "r_y3": 704.296, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.458, "r_y0": 712.653, "r_x1": 183.731, "r_y1": 712.653, "r_x2": 183.731, "r_y2": 705.811, "r_x3": 64.458, "r_y3": 705.811, "coord_origin": "TOPLEFT"}, "text": "https://github.com/IBM/SynthTabNet", "orig": "https://github.com/IBM/SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 https://github.com/IBM/SynthTabNet"}, {"label": "text", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9774322509765625, "cells": [{"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "its results & performance in Sec. 5. As a conclusion, we de-", "orig": "its results & performance in Sec. 5. As a conclusion, we de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "scribe how this new model-architecture can be re-purposed", "orig": "scribe how this new model-architecture can be re-purposed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 508.084, "r_y1": 107.93899999999996, "r_x2": 508.084, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for other tasks in the computer-vision community.", "orig": "for other tasks in the computer-vision community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community."}, {"label": "section_header", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 308.862, "t": 121.73199999999997, "r": 498.28, "b": 132.48000000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9428724646568298, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 132.48000000000002, "r_x1": 498.28, "r_y1": 132.48000000000002, "r_x2": 498.28, "r_y2": 121.73199999999997, "r_x3": 308.862, "r_y3": 121.73199999999997, "coord_origin": "TOPLEFT"}, "text": "2. Previous work and State of the Art", "orig": "2. Previous work and State of the Art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Previous work and State of the Art"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 142.49, "r": 545.115, "b": 330.369, "coord_origin": "TOPLEFT"}, "confidence": 0.987194836139679, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 151.04200000000003, "r_x1": 545.115, "r_y1": 151.04200000000003, "r_x2": 545.115, "r_y2": 142.49, "r_x3": 320.817, "r_y3": 142.49, "coord_origin": "TOPLEFT"}, "text": "Identifying the structure of a table has been an outstand-", "orig": "Identifying the structure of a table has been an outstand-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 162.99699999999996, "r_x1": 545.115, "r_y1": 162.99699999999996, "r_x2": 545.115, "r_y2": 154.44500000000005, "r_x3": 308.862, "r_y3": 154.44500000000005, "coord_origin": "TOPLEFT"}, "text": "ing problem in the document-parsing community, that mo-", "orig": "ing problem in the document-parsing community, that mo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 174.952, "r_x1": 334.376, "r_y1": 174.952, "r_x2": 334.376, "r_y2": 166.39999999999998, "r_x3": 308.862, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "tivates", "orig": "tivates", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.192, "r_y0": 174.952, "r_x1": 522.56, "r_y1": 174.952, "r_x2": 522.56, "r_y2": 166.39999999999998, "r_x3": 338.192, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "many organised public challenges [6, 4, 14].", "orig": "many organised public challenges [6, 4, 14].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 174.952, "r_x1": 545.115, "r_y1": 174.952, "r_x2": 545.115, "r_y2": 166.39999999999998, "r_x3": 529.623, "r_y3": 166.39999999999998, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 186.90700000000004, "r_x1": 545.115, "r_y1": 186.90700000000004, "r_x2": 545.115, "r_y2": 178.35500000000002, "r_x3": 308.862, "r_y3": 178.35500000000002, "coord_origin": "TOPLEFT"}, "text": "difficulty of the problem can be attributed to a number of", "orig": "difficulty of the problem can be attributed to a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 198.86199999999997, "r_x1": 545.115, "r_y1": 198.86199999999997, "r_x2": 545.115, "r_y2": 190.30999999999995, "r_x3": 308.862, "r_y3": 190.30999999999995, "coord_origin": "TOPLEFT"}, "text": "factors. First, there is a large variety in the shapes and sizes", "orig": "factors. First, there is a large variety in the shapes and sizes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 210.817, "r_x1": 346.979, "r_y1": 210.817, "r_x2": 346.979, "r_y2": 202.265, "r_x3": 308.862, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "of tables.", "orig": "of tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.869, "r_y0": 210.817, "r_x1": 508.642, "r_y1": 210.817, "r_x2": 508.642, "r_y2": 202.265, "r_x3": 354.869, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "Such large variety requires a flexible", "orig": "Such large variety requires a flexible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.737, "r_y0": 210.817, "r_x1": 545.115, "r_y1": 210.817, "r_x2": 545.115, "r_y2": 202.265, "r_x3": 512.737, "r_y3": 202.265, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 222.77300000000002, "r_x1": 545.115, "r_y1": 222.77300000000002, "r_x2": 545.115, "r_y2": 214.221, "r_x3": 308.862, "r_y3": 214.221, "coord_origin": "TOPLEFT"}, "text": "This is especially true for complex column- and row head-", "orig": "This is especially true for complex column- and row head-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 234.72799999999995, "r_x1": 322.969, "r_y1": 234.72799999999995, "r_x2": 322.969, "r_y2": 226.17600000000004, "r_x3": 308.862, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "ers,", "orig": "ers,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.084, "r_y0": 234.72799999999995, "r_x1": 530.918, "r_y1": 234.72799999999995, "r_x2": 530.918, "r_y2": 226.17600000000004, "r_x3": 327.084, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "which can be extremely intricate and demanding.", "orig": "which can be extremely intricate and demanding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.922, "r_y0": 234.72799999999995, "r_x1": 545.115, "r_y1": 234.72799999999995, "r_x2": 545.115, "r_y2": 226.17600000000004, "r_x3": 537.922, "r_y3": 226.17600000000004, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 246.683, "r_x1": 545.115, "r_y1": 246.683, "r_x2": 545.115, "r_y2": 238.13099999999997, "r_x3": 308.862, "r_y3": 238.13099999999997, "coord_origin": "TOPLEFT"}, "text": "second factor of complexity is the lack of data with regard", "orig": "second factor of complexity is the lack of data with regard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.63800000000003, "r_x1": 545.115, "r_y1": 258.63800000000003, "r_x2": 545.115, "r_y2": 250.086, "r_x3": 308.862, "r_y3": 250.086, "coord_origin": "TOPLEFT"}, "text": "to table-structure. Until the publication of PubTabNet [37],", "orig": "to table-structure. Until the publication of PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.59299999999996, "r_x1": 439.84, "r_y1": 270.59299999999996, "r_x2": 439.84, "r_y2": 262.04100000000005, "r_x3": 308.862, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "there were no large datasets (i.e.", "orig": "there were no large datasets (i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.44, "r_y0": 270.429, "r_x1": 452.189, "r_y1": 270.429, "r_x2": 452.189, "r_y2": 261.72299999999996, "r_x3": 444.44, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.89, "r_y0": 270.429, "r_x1": 470.834, "r_y1": 270.429, "r_x2": 470.834, "r_y2": 261.72299999999996, "r_x3": 455.89, "r_y3": 261.72299999999996, "coord_origin": "TOPLEFT"}, "text": "100", "orig": "100", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.834, "r_y0": 270.59299999999996, "r_x1": 545.115, "r_y1": 270.59299999999996, "r_x2": 545.115, "r_y2": 262.04100000000005, "r_x3": 470.834, "r_y3": 262.04100000000005, "coord_origin": "TOPLEFT"}, "text": "K tables) that pro-", "orig": "K tables) that pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.548, "r_x1": 545.115, "r_y1": 282.548, "r_x2": 545.115, "r_y2": 273.996, "r_x3": 308.862, "r_y3": 273.996, "coord_origin": "TOPLEFT"}, "text": "vided structure information. This happens primarily due to", "orig": "vided structure information. This happens primarily due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.504, "r_x1": 545.115, "r_y1": 294.504, "r_x2": 545.115, "r_y2": 285.952, "r_x3": 308.862, "r_y3": 285.952, "coord_origin": "TOPLEFT"}, "text": "the fact that tables are notoriously time-consuming to an-", "orig": "the fact that tables are notoriously time-consuming to an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 306.459, "r_x1": 545.115, "r_y1": 306.459, "r_x2": 545.115, "r_y2": 297.907, "r_x3": 308.862, "r_y3": 297.907, "coord_origin": "TOPLEFT"}, "text": "notate by hand. However, this has definitely changed in re-", "orig": "notate by hand. However, this has definitely changed in re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 318.414, "r_x1": 545.115, "r_y1": 318.414, "r_x2": 545.115, "r_y2": 309.862, "r_x3": 308.862, "r_y3": 309.862, "coord_origin": "TOPLEFT"}, "text": "cent years with the deliverance of PubTabNet [37], FinTab-", "orig": "cent years with the deliverance of PubTabNet [37], FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 330.369, "r_x1": 425.923, "r_y1": 330.369, "r_x2": 425.923, "r_y2": 321.817, "r_x3": 308.862, "r_y3": 321.817, "coord_origin": "TOPLEFT"}, "text": "Net [36], TableBank [17] etc.", "orig": "Net [36], TableBank [17] etc.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc."}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 333.838, "r": 545.115, "b": 449.987, "coord_origin": "TOPLEFT"}, "confidence": 0.9869040250778198, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 342.39, "r_x1": 391.492, "r_y1": 342.39, "r_x2": 391.492, "r_y2": 333.838, "r_x3": 320.817, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "Before the rising", "orig": "Before the rising", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.845, "r_y0": 342.39, "r_x1": 436.802, "r_y1": 342.39, "r_x2": 436.802, "r_y2": 333.838, "r_x3": 395.845, "r_y3": 333.838, "coord_origin": "TOPLEFT"}, "text": "popularity", "orig": "popularity", 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best-of-our knowl-", "orig": "To the best-of-our knowl-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 426.076, "r_x1": 545.115, "r_y1": 426.076, "r_x2": 545.115, "r_y2": 417.524, "r_x3": 308.862, "r_y3": 417.524, "coord_origin": "TOPLEFT"}, "text": "edge, there are currently two different types of network ar-", "orig": "edge, there are currently two different types of network ar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 438.032, "r_x1": 545.115, "r_y1": 438.032, "r_x2": 545.115, "r_y2": 429.48, "r_x3": 308.862, "r_y3": 429.48, "coord_origin": "TOPLEFT"}, "text": "chitecture that are being pursued for state-of-the-art table-", "orig": "chitecture that are being pursued for state-of-the-art table-", "text_direction": "left_to_right", 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Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 453.068, "r": 545.117, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9843218326568604, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 462.024, "r_x1": 423.262, "r_y1": 462.024, "r_x2": 423.262, "r_y2": 453.068, "r_x3": 320.817, "r_y3": 453.068, "coord_origin": "TOPLEFT"}, "text": "Image-to-Text networks", "orig": "Image-to-Text networks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.267, "r_y0": 462.008, "r_x1": 426.037, "r_y1": 462.008, "r_x2": 426.037, "r_y2": 453.456, "r_x3": 423.267, "r_y3": 453.456, "coord_origin": "TOPLEFT"}, 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In these IETD networks, one expects as output", "orig": "images [32]. In these IETD networks, one expects as output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.559, "r_x1": 503.671, "r_y1": 581.559, "r_x2": 503.671, "r_y2": 573.0070000000001, "r_x3": 308.862, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the LaTeX/HTML string of the entire table, i.e.", "orig": "the LaTeX/HTML string of the entire table, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.579, "r_y0": 581.559, "r_x1": 545.115, "r_y1": 581.559, "r_x2": 545.115, "r_y2": 573.0070000000001, "r_x3": 509.579, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "the sym-", "orig": "the sym-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.515, "r_x1": 545.115, "r_y1": 593.515, "r_x2": 545.115, "r_y2": 584.963, "r_x3": 308.862, "r_y3": 584.963, "coord_origin": "TOPLEFT"}, "text": "bols necessary for creating the table with the content of the", "orig": "bols necessary for creating the table with the content of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.47, "r_x1": 330.72, "r_y1": 605.47, "r_x2": 330.72, "r_y2": 596.918, "r_x3": 308.862, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "table.", "orig": "table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.536, "r_y0": 605.47, "r_x1": 497.075, "r_y1": 605.47, "r_x2": 497.075, "r_y2": 596.918, "r_x3": 334.536, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "Another approach is the 'image-encoder", "orig": "Another approach is the 'image-encoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 499.805, "r_y0": 605.164, "r_x1": 509.768, "r_y1": 605.164, "r_x2": 509.768, "r_y2": 596.6, "r_x3": 499.805, "r_y3": 596.6, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.501, "r_y0": 605.47, "r_x1": 545.109, "r_y1": 605.47, "r_x2": 545.109, "r_y2": 596.918, "r_x3": 512.501, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "dual de-", "orig": "dual de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.425, "r_x1": 545.115, "r_y1": 617.425, "r_x2": 545.115, "r_y2": 608.873, "r_x3": 308.862, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "coder' (IEDD) networks. In these type of networks, one has", "orig": "coder' (IEDD) networks. In these type of networks, one has", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.38, "r_x1": 545.115, "r_y1": 629.38, "r_x2": 545.115, "r_y2": 620.828, "r_x3": 308.862, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "two consecutive decoders with different purposes. The first", "orig": "two consecutive decoders with different purposes. The first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.335, "r_x1": 364.782, "r_y1": 641.335, "r_x2": 364.782, "r_y2": 632.783, "r_x3": 308.862, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "decoder is the", "orig": "decoder is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.574, "r_y0": 641.192, "r_x1": 415.614, "r_y1": 641.192, "r_x2": 415.614, "r_y2": 632.604, "r_x3": 367.574, "r_y3": 632.604, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 415.613, "r_y0": 641.335, "r_x1": 418.104, "r_y1": 641.335, "r_x2": 418.104, "r_y2": 632.783, "r_x3": 415.613, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 420.963, "r_y0": 641.335, "r_x1": 433.137, "r_y1": 641.335, "r_x2": 433.137, "r_y2": 632.783, "r_x3": 420.963, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "i.e.", "orig": "i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.122, "r_y0": 641.335, "r_x1": 442.661, "r_y1": 641.335, "r_x2": 442.661, "r_y2": 632.783, "r_x3": 437.122, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.441, "r_y0": 641.335, "r_x1": 545.117, "r_y1": 641.335, "r_x2": 545.117, "r_y2": 632.783, "r_x3": 445.441, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "only produces the HTM-", "orig": "only produces the HTM-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "L/LaTeX tags which construct an empty table. The second", "orig": "L/LaTeX tags which construct an empty table. The second", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.103, "r_x1": 373.599, "r_y1": 665.103, "r_x2": 373.599, "r_y2": 656.515, "r_x3": 308.862, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "content-decoder", "orig": "content-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.907, "r_y0": 665.246, "r_x1": 545.116, "r_y1": 665.246, "r_x2": 545.116, "r_y2": 656.694, "r_x3": 376.907, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "uses the encoding of the image in combi-", "orig": "uses the encoding of the image in combi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "nation with the output encoding of each cell-tag (from the", "orig": "nation with the output encoding of each cell-tag (from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.013, "r_x1": 356.902, "r_y1": 689.013, "r_x2": 356.902, "r_y2": 680.425, "r_x3": 308.862, "r_y3": 680.425, "coord_origin": "TOPLEFT"}, "text": "tag-decoder", "orig": "tag-decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.131, "r_y0": 689.156, "r_x1": 360.449, "r_y1": 689.156, "r_x2": 360.449, "r_y2": 680.604, "r_x3": 357.131, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.045, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 364.045, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "to generate the textual content of each table", "orig": "to generate the textual content of each table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 325.739, "r_y1": 701.111, "r_x2": 325.739, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.746, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 331.746, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "The network architecture of IEDD is certainly more", "orig": "The network architecture of IEDD is certainly more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "elaborate, but it has the advantage that one can pre-train the", "orig": "elaborate, but it has the advantage that one can pre-train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are 'image-encoder \u2192 text-decoder' (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the 'image-encoder \u2192 dual decoder' (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the"}], "headers": [{"label": "page_footer", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8778094053268433, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 250.151, "r_y1": 84.029, "r_x2": 250.151, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tag-decoder which is constrained to the table-tags.", "orig": "tag-decoder which is constrained to the table-tags.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 96.03999999999996, "r_x1": 70.366, "r_y1": 96.03999999999996, "r_x2": 70.366, "r_y2": 87.48800000000006, "r_x3": 62.067, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.931, "r_y0": 96.03999999999996, "r_x1": 110.953, "r_y1": 96.03999999999996, "r_x2": 110.953, "r_y2": 87.48800000000006, "r_x3": 76.931, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "practice,", "orig": "practice,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 118.545, "r_y0": 96.03999999999996, "r_x1": 136.258, "r_y1": 96.03999999999996, "r_x2": 136.258, "r_y2": 87.48800000000006, "r_x3": 118.545, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "both", "orig": "both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.824, "r_y0": 96.03999999999996, "r_x1": 175.372, "r_y1": 96.03999999999996, "r_x2": 175.372, "r_y2": 87.48800000000006, "r_x3": 142.824, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "network", "orig": "network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.947, "r_y0": 96.03999999999996, "r_x1": 232.836, "r_y1": 96.03999999999996, "r_x2": 232.836, "r_y2": 87.48800000000006, "r_x3": 181.947, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "architectures", "orig": "architectures", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 239.411, "r_y0": 96.03999999999996, "r_x1": 265.414, "r_y1": 96.03999999999996, "r_x2": 265.414, "r_y2": 87.48800000000006, "r_x3": 239.411, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "(IETD", "orig": "(IETD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 271.979, "r_y0": 96.03999999999996, "r_x1": 286.365, "r_y1": 96.03999999999996, "r_x2": 286.365, "r_y2": 87.48800000000006, "r_x3": 271.979, "r_y3": 87.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 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For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. Additionally, OCR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 239.50199999999995, "r_x1": 286.365, "r_y1": 239.50199999999995, "r_x2": 286.365, "r_y2": 230.95000000000005, "r_x3": 50.112, "r_y3": 230.95000000000005, "coord_origin": "TOPLEFT"}, "text": "can be completely omitted if the tables originate from pro-", "orig": "can be completely omitted if the tables originate from pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 251.457, "r_x1": 286.365, "r_y1": 251.457, "r_x2": 286.365, "r_y2": 242.90499999999997, "r_x3": 50.112, "r_y3": 242.90499999999997, "coord_origin": "TOPLEFT"}, "text": "grammatic PDF documents with known positions of each", "orig": "grammatic PDF documents with known positions of each", "text_direction": "left_to_right", 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.849, "r_y0": 311.29, "r_x1": 286.365, "r_y1": 311.29, "r_x2": 286.365, "r_y2": 302.738, "r_x3": 138.849, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "Note that one table cell can consti-", "orig": "Note that one table cell can consti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.245, "r_x1": 286.365, "r_y1": 323.245, "r_x2": 286.365, "r_y2": 314.693, "r_x3": 50.112, "r_y3": 314.693, "coord_origin": "TOPLEFT"}, "text": "tute out of multiple text-cells. To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 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255}, "rect": {"r_x0": 97.305, "r_y0": 394.976, "r_x1": 286.365, "r_y1": 394.976, "r_x2": 286.365, "r_y2": 386.424, "r_x3": 97.305, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "Graph Convolutional Networks (GCN's) based", "orig": "Graph Convolutional Networks (GCN's) based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 406.931, "r_x1": 286.365, "r_y1": 406.931, "r_x2": 286.365, "r_y2": 398.379, "r_x3": 50.112, "r_y3": 398.379, "coord_origin": "TOPLEFT"}, "text": "methods take the image as an input, but also the position of", "orig": "methods take the image as an input, but also the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 418.886, "r_x1": 286.365, "r_y1": 418.886, "r_x2": 286.365, "r_y2": 410.334, "r_x3": 50.112, "r_y3": 410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.882, "r_y0": 502.629, "r_x1": 255.652, "r_y1": 502.629, "r_x2": 255.652, "r_y2": 494.077, "r_x3": 252.882, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.63, "r_y0": 502.629, "r_x1": 286.366, "r_y1": 502.629, "r_x2": 286.366, "r_y2": 494.077, "r_x3": 258.63, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": "Apop-", "orig": "Apop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 514.5840000000001, "r_x1": 286.365, "r_y1": 514.5840000000001, "r_x2": 286.365, "r_y2": 506.032, "r_x3": 50.112, "r_y3": 506.032, "coord_origin": "TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", 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"rule-sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.975, "r_y0": 574.36, "r_x1": 256.726, "r_y1": 574.36, "r_x2": 256.726, "r_y2": 565.808, "r_x3": 248.975, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.459, "r_y0": 574.36, "r_x1": 286.365, "r_y1": 574.36, "r_x2": 286.365, "r_y2": 565.808, "r_x3": 261.459, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "obtain", "orig": "obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.315, "r_x1": 122.64, "r_y1": 586.315, "r_x2": 122.64, "r_y2": 577.763, "r_x3": 50.112, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "its table-structure.", "orig": "its table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.482, "r_y0": 586.315, "r_x1": 286.365, "r_y1": 586.315, "r_x2": 286.365, "r_y2": 577.763, "r_x3": 127.482, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "Currently, this approach achieves state-", "orig": "Currently, this approach achieves state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 598.27, "r_x1": 87.731, "r_y1": 598.27, "r_x2": 87.731, "r_y2": 589.718, "r_x3": 50.112, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "of-the-art", "orig": "of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.074, "r_y0": 598.27, "r_x1": 120.577, "r_y1": 598.27, "r_x2": 120.577, "r_y2": 589.718, "r_x3": 92.074, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "results,", "orig": "results,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.389, "r_y0": 598.27, "r_x1": 137.922, "r_y1": 598.27, "r_x2": 137.922, "r_y2": 589.718, "r_x3": 125.389, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "but", "orig": "but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.266, "r_y0": 598.27, "r_x1": 148.911, "r_y1": 598.27, "r_x2": 148.911, "r_y2": 589.718, "r_x3": 142.266, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.255, "r_y0": 598.27, "r_x1": 165.987, "r_y1": 598.27, "r_x2": 165.987, "r_y2": 589.718, "r_x3": 153.255, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "not", "orig": "not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.331, "r_y0": 598.27, "r_x1": 286.365, "r_y1": 598.27, "r_x2": 286.365, "r_y2": 589.718, "r_x3": 170.331, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "an end-to-end deep-learning", "orig": "an end-to-end deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 610.225, "r_x1": 82.49, "r_y1": 610.225, "r_x2": 82.49, "r_y2": 601.673, "r_x3": 50.112, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.173, "r_y0": 610.225, "r_x1": 286.365, "r_y1": 610.225, "r_x2": 286.365, "r_y2": 601.673, "r_x3": 87.173, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "As such, new rules need to be written if different", "orig": "As such, new rules need to be written if different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 622.181, "r_x1": 175.989, "r_y1": 622.181, "r_x2": 175.989, "r_y2": 613.629, "r_x3": 50.112, "r_y3": 613.629, "coord_origin": "TOPLEFT"}, "text": "types of tables are encountered.", "orig": "types of tables are encountered.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 646.693, "r_x1": 105.225, "r_y1": 646.693, "r_x2": 105.225, "r_y2": 635.9449999999999, "r_x3": 50.112, "r_y3": 635.9449999999999, "coord_origin": "TOPLEFT"}, "text": "3. Datasets", "orig": "3. 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"text": "dimensions in PubTabNet + FinTabNet datasets", "orig": "dimensions in PubTabNet + FinTabNet datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 326.294, "r_x1": 437.27, "r_y1": 326.294, "r_x2": 437.27, "r_y2": 317.742, "r_x3": 308.862, "r_y3": 317.742, "coord_origin": "TOPLEFT"}, "text": "balance in the previous datasets.", "orig": "balance in the previous datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 340.352, "r_x1": 545.115, "r_y1": 340.352, "r_x2": 545.115, "r_y2": 331.8, "r_x3": 320.817, "r_y3": 331.8, "coord_origin": "TOPLEFT"}, "text": "The PubTabNet dataset contains 509k tables delivered as", "orig": "The PubTabNet dataset contains 509k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 352.307, "r_x1": 545.115, "r_y1": 352.307, "r_x2": 545.115, "r_y2": 343.755, "r_x3": 308.862, "r_y3": 343.755, "coord_origin": "TOPLEFT"}, "text": "annotated PNG images. The annotations consist of the table", "orig": "annotated PNG images. The annotations consist of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 364.263, "r_x1": 545.115, "r_y1": 364.263, "r_x2": 545.115, "r_y2": 355.711, "r_x3": 308.862, "r_y3": 355.711, "coord_origin": "TOPLEFT"}, "text": "structure represented in HTML format, the tokenized text", "orig": "structure represented in HTML format, the tokenized text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.218, "r_x1": 545.115, "r_y1": 376.218, "r_x2": 545.115, "r_y2": 367.666, "r_x3": 308.862, "r_y3": 367.666, "coord_origin": "TOPLEFT"}, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "orig": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.173, "r_x1": 545.115, "r_y1": 388.173, "r_x2": 545.115, "r_y2": 379.621, "r_x3": 308.862, "r_y3": 379.621, "coord_origin": "TOPLEFT"}, "text": "pearance style of PubTabNet. Depending on its complexity,", "orig": "pearance style of PubTabNet. Depending on its complexity,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.128, "r_x1": 545.115, "r_y1": 400.128, "r_x2": 545.115, "r_y2": 391.576, "r_x3": 308.862, "r_y3": 391.576, "coord_origin": "TOPLEFT"}, "text": "a table is characterized as 'simple' when it does not contain", "orig": "a table is characterized as 'simple' when it does not contain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.083, "r_x1": 545.115, "r_y1": 412.083, "r_x2": 545.115, "r_y2": 403.531, "r_x3": 308.862, "r_y3": 403.531, "coord_origin": "TOPLEFT"}, "text": "row spans or column spans, otherwise it is 'complex'. The", "orig": "row spans or column spans, otherwise it is 'complex'. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.038, "r_x1": 545.115, "r_y1": 424.038, "r_x2": 545.115, "r_y2": 415.486, "r_x3": 308.862, "r_y3": 415.486, "coord_origin": "TOPLEFT"}, "text": "dataset is divided into Train and Val splits (roughly 98% and", "orig": "dataset is divided into Train and Val splits (roughly 98% and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.994, "r_x1": 545.115, "r_y1": 435.994, "r_x2": 545.115, "r_y2": 427.442, "r_x3": 308.862, "r_y3": 427.442, "coord_origin": "TOPLEFT"}, "text": "2%). The Train split consists of 54% simple and 46% com-", "orig": "2%). The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized text and the bounding boxes on a table cell basis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 519.6800000000001, "r_x1": 545.115, "r_y1": 519.6800000000001, "r_x2": 545.115, "r_y2": 511.128, "r_x3": 308.862, "r_y3": 511.128, "coord_origin": "TOPLEFT"}, "text": "The dataset is divided into Train, Test and Val splits (81%,", "orig": "The dataset is divided into Train, Test and Val splits (81%,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.635, "r_x1": 545.115, "r_y1": 531.635, "r_x2": 545.115, "r_y2": 523.0830000000001, "r_x3": 308.862, "r_y3": 523.0830000000001, "coord_origin": "TOPLEFT"}, "text": "9.5%, 9.5%), and each one is almost equally divided into", "orig": "9.5%, 9.5%), and each one is almost equally divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.59, "r_x1": 545.115, "r_y1": 543.59, "r_x2": 545.115, "r_y2": 535.038, "r_x3": 308.862, "r_y3": 535.038, "coord_origin": "TOPLEFT"}, "text": "simple and complex tables (Train: 48% simple, 52% com-", "orig": "simple and complex tables (Train: 48% simple, 52% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.545, "r_x1": 350.964, "r_y1": 555.545, "r_x2": 350.964, "r_y2": 546.9929999999999, "r_x3": 308.862, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "plex, Test:", "orig": "plex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.467, "r_y0": 555.545, "r_x1": 490.082, "r_y1": 555.545, "r_x2": 490.082, "r_y2": 546.9929999999999, "r_x3": 355.467, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "48% simple, 52% complex, Test:", "orig": "48% simple, 52% complex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.595, "r_y0": 555.545, "r_x1": 545.115, "r_y1": 555.545, "r_x2": 545.115, "r_y2": 546.9929999999999, "r_x3": 494.595, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "53% simple,", "orig": "53% simple,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.501, "r_x1": 370.6, "r_y1": 567.501, "r_x2": 370.6, "r_y2": 558.9490000000001, "r_x3": 308.862, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "47% complex).", "orig": "47% complex).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.731, "r_y0": 603.366, "r_x1": 545.115, "r_y1": 603.366, "r_x2": 545.115, "r_y2": 594.814, "r_x3": 407.731, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "The entire dataset consists of sim-", "orig": "The entire dataset consists of sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 615.321, "r_x1": 545.115, "r_y1": 615.321, "r_x2": 545.115, "r_y2": 606.769, "r_x3": 308.862, "r_y3": 606.769, "coord_origin": "TOPLEFT"}, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "orig": "ple tables and it is divided into 90% Train, 3% Test and 7%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 627.2760000000001, "r_x1": 348.164, "r_y1": 627.2760000000001, "r_x2": 348.164, "r_y2": 618.7239999999999, "r_x3": 308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 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"r_y2": 99.44299999999998, "r_x3": 50.112, "r_y3": 99.44299999999998, "coord_origin": "TOPLEFT"}, "text": "IEDD) require an implicit, custom trained object-character-", "orig": "IEDD) require an implicit, custom trained object-character-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.95100000000002, "r_x1": 286.365, "r_y1": 119.95100000000002, "r_x2": 286.365, "r_y2": 111.399, "r_x3": 50.112, "r_y3": 111.399, "coord_origin": "TOPLEFT"}, "text": "recognition (OCR) to obtain the content of the table-cells.", "orig": "recognition (OCR) to obtain the content of the table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.90599999999995, "r_x1": 286.365, "r_y1": 131.90599999999995, "r_x2": 286.365, "r_y2": 123.35400000000004, "r_x3": 50.112, "r_y3": 123.35400000000004, "coord_origin": "TOPLEFT"}, "text": "In the case of IETD, this OCR engine is implicit in the de-", "orig": "In the case of IETD, this OCR engine is implicit in the de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.861, "r_x1": 286.365, "r_y1": 143.861, "r_x2": 286.365, "r_y2": 135.30899999999997, "r_x3": 50.112, "r_y3": 135.30899999999997, "coord_origin": "TOPLEFT"}, "text": "coder similar to [24]. For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. Additionally, OCR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 239.50199999999995, "r_x1": 286.365, "r_y1": 239.50199999999995, "r_x2": 286.365, "r_y2": 230.95000000000005, "r_x3": 50.112, "r_y3": 230.95000000000005, "coord_origin": "TOPLEFT"}, "text": "can be completely omitted if the tables originate from pro-", "orig": "can be completely omitted if the tables originate from pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 251.457, "r_x1": 286.365, "r_y1": 251.457, "r_x2": 286.365, "r_y2": 242.90499999999997, "r_x3": 50.112, "r_y3": 242.90499999999997, "coord_origin": "TOPLEFT"}, "text": "grammatic PDF documents with known positions of each", "orig": "grammatic PDF documents with known positions of each", "text_direction": "left_to_right", 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To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.11, "r_x1": 286.365, "r_y1": 359.11, "r_x2": 286.365, "r_y2": 350.558, "r_x3": 50.112, "r_y3": 350.558, "coord_origin": "TOPLEFT"}, "text": "node is then associated with en embedding vector coming", "orig": "node is then associated with en embedding vector coming", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.066, "r_x1": 286.365, "r_y1": 371.066, "r_x2": 286.365, "r_y2": 362.514, "r_x3": 50.112, "r_y3": 362.514, "coord_origin": "TOPLEFT"}, "text": "from the encoded image, its coordinates and the encoded", "orig": "from the encoded image, its coordinates and the encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.021, "r_x1": 67.397, "r_y1": 383.021, "r_x2": 67.397, "r_y2": 374.469, "r_x3": 50.112, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "text.", "orig": "text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 72.976, "r_y0": 383.021, "r_x1": 286.365, "r_y1": 383.021, "r_x2": 286.365, "r_y2": 374.469, "r_x3": 72.976, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "Furthermore, nodes that represent adjacent text-cells", "orig": "Furthermore, nodes that represent adjacent text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 394.976, "r_x1": 92.602, "r_y1": 394.976, "r_x2": 92.602, "r_y2": 386.424, "r_x3": 50.112, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "are linked.", "orig": "are linked.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.305, "r_y0": 394.976, "r_x1": 286.365, "r_y1": 394.976, "r_x2": 286.365, "r_y2": 386.424, "r_x3": 97.305, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "Graph Convolutional Networks (GCN's) based", "orig": "Graph Convolutional Networks (GCN's) based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 406.931, "r_x1": 286.365, "r_y1": 406.931, "r_x2": 286.365, "r_y2": 398.379, "r_x3": 50.112, "r_y3": 398.379, "coord_origin": "TOPLEFT"}, "text": "methods take the image as an input, but also the position of", "orig": "methods take the image as an input, but also the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 418.886, "r_x1": 286.365, "r_y1": 418.886, "r_x2": 286.365, "r_y2": 410.334, "r_x3": 50.112, "r_y3": 410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 493.689, "r": 286.366, "b": 622.181, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.882, "r_y0": 502.629, "r_x1": 255.652, "r_y1": 502.629, "r_x2": 255.652, "r_y2": 494.077, "r_x3": 252.882, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.63, "r_y0": 502.629, "r_x1": 286.366, "r_y1": 502.629, "r_x2": 286.366, "r_y2": 494.077, "r_x3": 258.63, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": "Apop-", "orig": "Apop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 514.5840000000001, "r_x1": 286.365, "r_y1": 514.5840000000001, "r_x2": 286.365, "r_y2": 506.032, "r_x3": 50.112, "r_y3": 506.032, "coord_origin": "TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.828, "r_y0": 574.36, "r_x1": 166.983, "r_y1": 574.36, "r_x2": 166.983, "r_y2": 565.808, "r_x3": 149.828, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "uses", "orig": "uses", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.715, "r_y0": 574.36, "r_x1": 205.768, "r_y1": 574.36, "r_x2": 205.768, "r_y2": 565.808, "r_x3": 171.715, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "different", "orig": "different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.5, "r_y0": 574.36, "r_x1": 244.243, "r_y1": 574.36, "r_x2": 244.243, "r_y2": 565.808, "r_x3": 210.5, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "rule-sets", "orig": "rule-sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.975, "r_y0": 574.36, "r_x1": 256.726, "r_y1": 574.36, "r_x2": 256.726, "r_y2": 565.808, "r_x3": 248.975, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.459, "r_y0": 574.36, "r_x1": 286.365, "r_y1": 574.36, "r_x2": 286.365, "r_y2": 565.808, "r_x3": 261.459, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "obtain", "orig": "obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.315, "r_x1": 122.64, "r_y1": 586.315, "r_x2": 122.64, "r_y2": 577.763, "r_x3": 50.112, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "its 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Fig. 1 shows the ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.173, "r_x1": 545.115, "r_y1": 388.173, "r_x2": 545.115, "r_y2": 379.621, "r_x3": 308.862, "r_y3": 379.621, "coord_origin": "TOPLEFT"}, "text": "pearance style of PubTabNet. Depending on its complexity,", "orig": "pearance style of PubTabNet. Depending on its complexity,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.128, "r_x1": 545.115, "r_y1": 400.128, "r_x2": 545.115, "r_y2": 391.576, "r_x3": 308.862, "r_y3": 391.576, "coord_origin": "TOPLEFT"}, "text": "a table is characterized as 'simple' when it does not contain", "orig": "a table is characterized as 'simple' when it does not contain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.083, "r_x1": 545.115, "r_y1": 412.083, "r_x2": 545.115, "r_y2": 403.531, "r_x3": 308.862, "r_y3": 403.531, "coord_origin": "TOPLEFT"}, "text": "row spans or column spans, otherwise it is 'complex'. The", "orig": "row spans or column spans, otherwise it is 'complex'. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.038, "r_x1": 545.115, "r_y1": 424.038, "r_x2": 545.115, "r_y2": 415.486, "r_x3": 308.862, "r_y3": 415.486, "coord_origin": "TOPLEFT"}, "text": "dataset is divided into Train and Val splits (roughly 98% and", "orig": "dataset is divided into Train and Val splits (roughly 98% and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.994, "r_x1": 545.115, "r_y1": 435.994, "r_x2": 545.115, "r_y2": 427.442, "r_x3": 308.862, "r_y3": 427.442, "coord_origin": "TOPLEFT"}, "text": "2%). The Train split consists of 54% simple and 46% com-", "orig": "2%). The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized text and the bounding boxes on a table cell basis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 519.6800000000001, "r_x1": 545.115, "r_y1": 519.6800000000001, "r_x2": 545.115, "r_y2": 511.128, "r_x3": 308.862, "r_y3": 511.128, "coord_origin": "TOPLEFT"}, "text": "The dataset is divided into Train, Test and Val splits (81%,", "orig": "The dataset is divided into Train, Test and Val splits (81%,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.635, "r_x1": 545.115, "r_y1": 531.635, "r_x2": 545.115, "r_y2": 523.0830000000001, "r_x3": 308.862, "r_y3": 523.0830000000001, "coord_origin": "TOPLEFT"}, "text": "9.5%, 9.5%), and each one is almost equally divided into", "orig": "9.5%, 9.5%), and each one is almost equally divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.59, "r_x1": 545.115, "r_y1": 543.59, "r_x2": 545.115, "r_y2": 535.038, "r_x3": 308.862, "r_y3": 535.038, "coord_origin": "TOPLEFT"}, "text": "simple and complex tables (Train: 48% simple, 52% com-", "orig": "simple and complex tables (Train: 48% simple, 52% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.545, "r_x1": 350.964, "r_y1": 555.545, "r_x2": 350.964, "r_y2": 546.9929999999999, "r_x3": 308.862, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "plex, Test:", "orig": "plex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.467, "r_y0": 555.545, "r_x1": 490.082, "r_y1": 555.545, "r_x2": 490.082, "r_y2": 546.9929999999999, "r_x3": 355.467, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "48% simple, 52% complex, Test:", "orig": "48% simple, 52% complex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.595, "r_y0": 555.545, "r_x1": 545.115, "r_y1": 555.545, "r_x2": 545.115, "r_y2": 546.9929999999999, "r_x3": 494.595, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "53% simple,", "orig": "53% simple,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.501, "r_x1": 370.6, "r_y1": 567.501, "r_x2": 370.6, "r_y2": 558.9490000000001, "r_x3": 308.862, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "47% complex).", "orig": "47% complex).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.731, "r_y0": 603.366, "r_x1": 545.115, "r_y1": 603.366, "r_x2": 545.115, "r_y2": 594.814, "r_x3": 407.731, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "The entire dataset consists of sim-", "orig": "The entire dataset consists of sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 615.321, "r_x1": 545.115, "r_y1": 615.321, "r_x2": 545.115, "r_y2": 606.769, "r_x3": 308.862, "r_y3": 606.769, "coord_origin": "TOPLEFT"}, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "orig": "ple tables and it is divided into 90% Train, 3% Test and 7%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 627.2760000000001, "r_x1": 348.164, "r_y1": 627.2760000000001, "r_x2": 348.164, "r_y2": 618.7239999999999, "r_x3": 308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.783, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 250.151, "b": 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For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. Additionally, OCR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 239.50199999999995, "r_x1": 286.365, "r_y1": 239.50199999999995, "r_x2": 286.365, "r_y2": 230.95000000000005, "r_x3": 50.112, "r_y3": 230.95000000000005, "coord_origin": "TOPLEFT"}, "text": "can be completely omitted if the tables originate from pro-", "orig": "can be completely omitted if the tables originate from pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 251.457, "r_x1": 286.365, "r_y1": 251.457, "r_x2": 286.365, "r_y2": 242.90499999999997, "r_x3": 50.112, "r_y3": 242.90499999999997, "coord_origin": "TOPLEFT"}, "text": "grammatic PDF documents with known positions of each", "orig": "grammatic PDF documents with known positions of each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 263.413, "r_x1": 66.989, "r_y1": 263.413, "r_x2": 66.989, "r_y2": 254.861, "r_x3": 50.112, "r_y3": 254.861, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.382, "r_y0": 263.413, "r_x1": 286.365, "r_y1": 263.413, "r_x2": 286.365, "r_y2": 254.861, "r_x3": 71.382, "r_y3": 254.861, "coord_origin": "TOPLEFT"}, "text": "The latter was the inspiration for the work of this pa-", "orig": "The latter was the inspiration for the work of this pa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 275.36800000000005, "r_x1": 64.777, "r_y1": 275.36800000000005, "r_x2": 64.777, "r_y2": 266.81600000000003, "r_x3": 50.112, "r_y3": 266.81600000000003, "coord_origin": "TOPLEFT"}, "text": "per.", "orig": "per.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. 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"coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.68, "r_y0": 299.335, "r_x1": 286.365, "r_y1": 299.335, "r_x2": 286.365, "r_y2": 290.783, "r_x3": 263.68, "r_y3": 290.783, "coord_origin": "TOPLEFT"}, "text": "table-", "orig": "table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.29, "r_x1": 84.971, "r_y1": 311.29, "r_x2": 84.971, "r_y2": 302.738, "r_x3": 50.112, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 88.986, "r_y0": 311.29, "r_x1": 131.168, "r_y1": 311.29, "r_x2": 131.168, "r_y2": 302.738, "r_x3": 88.986, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "extraction.", "orig": "extraction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.849, "r_y0": 311.29, "r_x1": 286.365, "r_y1": 311.29, "r_x2": 286.365, "r_y2": 302.738, "r_x3": 138.849, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "Note that one table cell can consti-", "orig": "Note that one table cell can consti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.245, "r_x1": 286.365, "r_y1": 323.245, "r_x2": 286.365, "r_y2": 314.693, "r_x3": 50.112, "r_y3": 314.693, "coord_origin": "TOPLEFT"}, "text": "tute out of multiple text-cells. To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.11, "r_x1": 286.365, "r_y1": 359.11, "r_x2": 286.365, "r_y2": 350.558, "r_x3": 50.112, "r_y3": 350.558, "coord_origin": "TOPLEFT"}, "text": "node is then associated with en embedding vector coming", "orig": "node is then associated with en embedding vector coming", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.066, "r_x1": 286.365, "r_y1": 371.066, "r_x2": 286.365, "r_y2": 362.514, "r_x3": 50.112, "r_y3": 362.514, "coord_origin": "TOPLEFT"}, "text": "from the encoded image, its coordinates and the encoded", "orig": "from the encoded image, its coordinates and the encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.021, "r_x1": 67.397, "r_y1": 383.021, "r_x2": 67.397, "r_y2": 374.469, "r_x3": 50.112, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "text.", "orig": "text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 72.976, "r_y0": 383.021, "r_x1": 286.365, "r_y1": 383.021, "r_x2": 286.365, "r_y2": 374.469, "r_x3": 72.976, "r_y3": 374.469, "coord_origin": "TOPLEFT"}, "text": "Furthermore, nodes that represent adjacent text-cells", "orig": "Furthermore, nodes that represent adjacent text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 394.976, "r_x1": 92.602, "r_y1": 394.976, "r_x2": 92.602, "r_y2": 386.424, "r_x3": 50.112, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "are linked.", "orig": "are linked.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.305, "r_y0": 394.976, "r_x1": 286.365, "r_y1": 394.976, "r_x2": 286.365, "r_y2": 386.424, "r_x3": 97.305, "r_y3": 386.424, "coord_origin": "TOPLEFT"}, "text": "Graph Convolutional Networks (GCN's) based", "orig": "Graph Convolutional Networks (GCN's) based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 406.931, "r_x1": 286.365, "r_y1": 406.931, "r_x2": 286.365, "r_y2": 398.379, "r_x3": 50.112, "r_y3": 398.379, "coord_origin": "TOPLEFT"}, "text": "methods take the image as an input, but also the position of", "orig": "methods take the image as an input, but also the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 418.886, "r_x1": 286.365, "r_y1": 418.886, "r_x2": 286.365, "r_y2": 410.334, "r_x3": 50.112, "r_y3": 410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 493.689, "r": 286.366, "b": 622.181, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.882, "r_y0": 502.629, "r_x1": 255.652, "r_y1": 502.629, "r_x2": 255.652, "r_y2": 494.077, "r_x3": 252.882, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.63, "r_y0": 502.629, "r_x1": 286.366, "r_y1": 502.629, "r_x2": 286.366, "r_y2": 494.077, "r_x3": 258.63, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": "Apop-", "orig": "Apop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 514.5840000000001, "r_x1": 286.365, "r_y1": 514.5840000000001, "r_x2": 286.365, "r_y2": 506.032, "r_x3": 50.112, "r_y3": 506.032, "coord_origin": "TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 574.36, "r_x1": 59.527, "r_y1": 574.36, "r_x2": 59.527, "r_y2": 565.808, "r_x3": 50.112, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "its", "orig": "its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.259, "r_y0": 574.36, "r_x1": 95.8, "r_y1": 574.36, "r_x2": 95.8, "r_y2": 565.808, "r_x3": 64.259, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "images)", "orig": "images)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 100.523, "r_y0": 574.36, "r_x1": 145.105, "r_y1": 574.36, "r_x2": 145.105, "r_y2": 565.808, "r_x3": 100.523, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "and finally", "orig": "and finally", "text_direction": 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table-structure.", "orig": "its table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.482, "r_y0": 586.315, "r_x1": 286.365, "r_y1": 586.315, "r_x2": 286.365, "r_y2": 577.763, "r_x3": 127.482, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "Currently, this approach achieves state-", "orig": "Currently, this approach achieves state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 598.27, "r_x1": 87.731, "r_y1": 598.27, "r_x2": 87.731, "r_y2": 589.718, "r_x3": 50.112, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "of-the-art", "orig": "of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.074, "r_y0": 598.27, "r_x1": 120.577, "r_y1": 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In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.112, "t": 635.9449999999999, "r": 105.225, "b": 646.693, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 646.693, "r_x1": 105.225, "r_y1": 646.693, "r_x2": 105.225, "r_y2": 635.9449999999999, "r_x3": 50.112, "r_y3": 635.9449999999999, "coord_origin": "TOPLEFT"}, "text": "3. Datasets", "orig": "3. Datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 656.694, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 62.067, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "We rely on large-scale datasets such as PubTabNet [37],", "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "orig": "FinTabNet [36], and TableBank [17] datasets to train and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 133.479, "r_y1": 689.156, "r_x2": 133.479, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "evaluate our models.", "orig": "evaluate our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.271, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 138.271, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "These datasets span over various ap-", "orig": "These datasets span over various ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 166.246, "r_y1": 701.111, "r_x2": 166.246, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "pearance styles and content.", "orig": "pearance styles and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.688, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 173.688, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "We also introduce our own", "orig": "We also introduce our own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "synthetically generated SynthTabNet dataset to fix an im-", "orig": "synthetically generated SynthTabNet dataset to fix an im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. 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null}, {"label": "caption", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 308.862, "t": 268.105, "r": 545.115, "b": 288.612, "coord_origin": "TOPLEFT"}, "confidence": 0.9667505025863647, "cells": [{"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 276.65700000000004, "r_x1": 346.062, "r_y1": 276.65700000000004, "r_x2": 346.062, "r_y2": 268.105, "r_x3": 308.862, "r_y3": 268.105, "coord_origin": "TOPLEFT"}, "text": "Figure 2:", "orig": "Figure 2:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.491, "r_y0": 276.65700000000004, "r_x1": 545.115, "r_y1": 276.65700000000004, "r_x2": 545.115, "r_y2": 268.105, "r_x3": 354.491, "r_y3": 268.105, "coord_origin": "TOPLEFT"}, "text": "Distribution of the tables across different table", "orig": "Distribution of the tables across different table", "text_direction": 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308.862, "r_y3": 317.742, "coord_origin": "TOPLEFT"}, "text": "balance in the previous datasets.", "orig": "balance in the previous datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "balance in the previous datasets."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 331.8, "r": 545.115, "b": 627.2760000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9870319366455078, "cells": [{"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 340.352, "r_x1": 545.115, "r_y1": 340.352, "r_x2": 545.115, "r_y2": 331.8, "r_x3": 320.817, "r_y3": 331.8, "coord_origin": "TOPLEFT"}, "text": "The PubTabNet dataset contains 509k tables delivered as", "orig": "The PubTabNet dataset contains 509k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 352.307, "r_x1": 545.115, "r_y1": 352.307, "r_x2": 545.115, "r_y2": 343.755, "r_x3": 308.862, "r_y3": 343.755, "coord_origin": "TOPLEFT"}, "text": "annotated PNG images. The annotations consist of the table", "orig": "annotated PNG images. The annotations consist of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 364.263, "r_x1": 545.115, "r_y1": 364.263, "r_x2": 545.115, "r_y2": 355.711, "r_x3": 308.862, "r_y3": 355.711, "coord_origin": "TOPLEFT"}, "text": "structure represented in HTML format, the tokenized text", "orig": "structure represented in HTML format, the tokenized text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.218, "r_x1": 545.115, "r_y1": 376.218, "r_x2": 545.115, "r_y2": 367.666, "r_x3": 308.862, "r_y3": 367.666, "coord_origin": "TOPLEFT"}, "text": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "orig": "and its bounding boxes per table cell. Fig. 1 shows the ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.173, "r_x1": 545.115, "r_y1": 388.173, "r_x2": 545.115, "r_y2": 379.621, "r_x3": 308.862, "r_y3": 379.621, "coord_origin": "TOPLEFT"}, "text": "pearance style of PubTabNet. Depending on its complexity,", "orig": "pearance style of PubTabNet. Depending on its complexity,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.128, "r_x1": 545.115, "r_y1": 400.128, "r_x2": 545.115, "r_y2": 391.576, "r_x3": 308.862, "r_y3": 391.576, "coord_origin": "TOPLEFT"}, "text": "a table is characterized as 'simple' when it does not contain", "orig": "a table is characterized as 'simple' when it does not contain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.083, "r_x1": 545.115, "r_y1": 412.083, "r_x2": 545.115, "r_y2": 403.531, "r_x3": 308.862, "r_y3": 403.531, "coord_origin": "TOPLEFT"}, "text": "row spans or column spans, otherwise it is 'complex'. The", "orig": "row spans or column spans, otherwise it is 'complex'. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.038, "r_x1": 545.115, "r_y1": 424.038, "r_x2": 545.115, "r_y2": 415.486, "r_x3": 308.862, "r_y3": 415.486, "coord_origin": "TOPLEFT"}, "text": "dataset is divided into Train and Val splits (roughly 98% and", "orig": "dataset is divided into Train and Val splits (roughly 98% and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.994, "r_x1": 545.115, "r_y1": 435.994, "r_x2": 545.115, "r_y2": 427.442, "r_x3": 308.862, "r_y3": 427.442, "coord_origin": "TOPLEFT"}, "text": "2%). The Train split consists of 54% simple and 46% com-", "orig": "2%). The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized text and the bounding boxes on a table cell basis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 519.6800000000001, "r_x1": 545.115, "r_y1": 519.6800000000001, "r_x2": 545.115, "r_y2": 511.128, "r_x3": 308.862, "r_y3": 511.128, "coord_origin": "TOPLEFT"}, "text": "The dataset is divided into Train, Test and Val splits (81%,", "orig": "The dataset is divided into Train, Test and Val splits (81%,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.635, "r_x1": 545.115, "r_y1": 531.635, "r_x2": 545.115, "r_y2": 523.0830000000001, "r_x3": 308.862, "r_y3": 523.0830000000001, "coord_origin": "TOPLEFT"}, "text": "9.5%, 9.5%), and each one is almost equally divided into", "orig": "9.5%, 9.5%), and each one is almost equally divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.59, "r_x1": 545.115, "r_y1": 543.59, "r_x2": 545.115, "r_y2": 535.038, "r_x3": 308.862, "r_y3": 535.038, "coord_origin": "TOPLEFT"}, "text": "simple and complex tables (Train: 48% simple, 52% com-", "orig": "simple and complex tables (Train: 48% simple, 52% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.545, "r_x1": 350.964, "r_y1": 555.545, "r_x2": 350.964, "r_y2": 546.9929999999999, "r_x3": 308.862, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "plex, Test:", "orig": "plex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.467, "r_y0": 555.545, "r_x1": 490.082, "r_y1": 555.545, "r_x2": 490.082, "r_y2": 546.9929999999999, "r_x3": 355.467, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "48% simple, 52% complex, Test:", "orig": "48% simple, 52% complex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.595, "r_y0": 555.545, "r_x1": 545.115, "r_y1": 555.545, "r_x2": 545.115, "r_y2": 546.9929999999999, "r_x3": 494.595, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "53% simple,", "orig": "53% simple,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.501, "r_x1": 370.6, "r_y1": 567.501, "r_x2": 370.6, "r_y2": 558.9490000000001, "r_x3": 308.862, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "47% complex).", "orig": "47% complex).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.731, "r_y0": 603.366, "r_x1": 545.115, "r_y1": 603.366, "r_x2": 545.115, "r_y2": 594.814, "r_x3": 407.731, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "The entire dataset consists of sim-", "orig": "The entire dataset consists of sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 615.321, "r_x1": 545.115, "r_y1": 615.321, "r_x2": 545.115, "r_y2": 606.769, "r_x3": 308.862, "r_y3": 606.769, "coord_origin": "TOPLEFT"}, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "orig": "ple tables and it is divided into 90% Train, 3% Test and 7%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 627.2760000000001, "r_x1": 348.164, "r_y1": 627.2760000000001, "r_x2": 348.164, "r_y2": 618.7239999999999, "r_x3": 308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.783, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}, {"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}], "body": [{"label": "text", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 250.151, "b": 84.029, "coord_origin": "TOPLEFT"}, "confidence": 0.8767216801643372, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 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111.399, "r_x3": 50.112, "r_y3": 111.399, "coord_origin": "TOPLEFT"}, "text": "recognition (OCR) to obtain the content of the table-cells.", "orig": "recognition (OCR) to obtain the content of the table-cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.90599999999995, "r_x1": 286.365, "r_y1": 131.90599999999995, "r_x2": 286.365, "r_y2": 123.35400000000004, "r_x3": 50.112, "r_y3": 123.35400000000004, "coord_origin": "TOPLEFT"}, "text": "In the case of IETD, this OCR engine is implicit in the de-", "orig": "In the case of IETD, this OCR engine is implicit in the de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.861, "r_x1": 286.365, "r_y1": 143.861, "r_x2": 286.365, "r_y2": 135.30899999999997, "r_x3": 50.112, "r_y3": 135.30899999999997, 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For the IEDD, the OCR is solely em-", "orig": "coder similar to [24]. For the IEDD, the OCR is solely em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.81600000000003, "r_x1": 174.206, "r_y1": 155.81600000000003, "r_x2": 174.206, "r_y2": 147.264, "r_x3": 50.112, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "bedded in the content-decoder.", "orig": "bedded in the content-decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.098, "r_y0": 155.81600000000003, "r_x1": 286.365, "r_y1": 155.81600000000003, "r_x2": 286.365, "r_y2": 147.264, "r_x3": 179.098, "r_y3": 147.264, "coord_origin": "TOPLEFT"}, "text": "This reliance on a custom,", "orig": "This reliance on a custom,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 167.77099999999996, "r_x1": 243.267, "r_y1": 167.77099999999996, "r_x2": 243.267, "r_y2": 159.21900000000005, "r_x3": 50.112, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "implicit OCR decoder is of course problematic.", "orig": "implicit OCR decoder is of course problematic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.437, "r_y0": 167.77099999999996, "r_x1": 286.365, "r_y1": 167.77099999999996, "r_x2": 286.365, "r_y2": 159.21900000000005, "r_x3": 248.437, "r_y3": 159.21900000000005, "coord_origin": "TOPLEFT"}, "text": "OCR is a", "orig": "OCR is a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 179.726, "r_x1": 286.365, "r_y1": 179.726, "r_x2": 286.365, "r_y2": 171.17399999999998, "r_x3": 50.112, "r_y3": 171.17399999999998, "coord_origin": "TOPLEFT"}, "text": "well known and extremely tough problem, that often needs", "orig": "well known and extremely tough problem, that often needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 191.68200000000002, "r_x1": 286.365, "r_y1": 191.68200000000002, "r_x2": 286.365, "r_y2": 183.13, "r_x3": 50.112, "r_y3": 183.13, "coord_origin": "TOPLEFT"}, "text": "custom training for each individual language. However, the", "orig": "custom training for each individual language. However, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 203.63699999999994, "r_x1": 126.655, "r_y1": 203.63699999999994, "r_x2": 126.655, "r_y2": 195.08500000000004, "r_x3": 50.112, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "limited availability", "orig": "limited availability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.59, "r_y0": 203.63699999999994, "r_x1": 142.206, "r_y1": 203.63699999999994, "r_x2": 142.206, "r_y2": 195.08500000000004, "r_x3": 130.59, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.132, "r_y0": 203.63699999999994, "r_x1": 286.365, "r_y1": 203.63699999999994, "r_x2": 286.365, "r_y2": 195.08500000000004, "r_x3": 146.132, "r_y3": 195.08500000000004, "coord_origin": "TOPLEFT"}, "text": "non-english content in the current", "orig": "non-english content in the current", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 215.59199999999998, "r_x1": 286.365, "r_y1": 215.59199999999998, "r_x2": 286.365, "r_y2": 207.03999999999996, "r_x3": 50.112, "r_y3": 207.03999999999996, "coord_origin": "TOPLEFT"}, "text": "datasets, makes it impractical to apply the IETD and IEDD", "orig": "datasets, makes it impractical to apply the IETD and IEDD", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 227.54700000000003, "r_x1": 286.365, "r_y1": 227.54700000000003, "r_x2": 286.365, "r_y2": 218.995, "r_x3": 50.112, "r_y3": 218.995, "coord_origin": "TOPLEFT"}, "text": "methods on tables with other languages. Additionally, OCR", "orig": "methods on tables with other languages. Additionally, OCR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 239.50199999999995, "r_x1": 286.365, "r_y1": 239.50199999999995, "r_x2": 286.365, "r_y2": 230.95000000000005, "r_x3": 50.112, "r_y3": 230.95000000000005, "coord_origin": "TOPLEFT"}, "text": "can be completely omitted if the tables originate from pro-", "orig": "can be completely omitted if the tables originate from pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 251.457, "r_x1": 286.365, "r_y1": 251.457, "r_x2": 286.365, "r_y2": 242.90499999999997, "r_x3": 50.112, "r_y3": 242.90499999999997, "coord_origin": "TOPLEFT"}, "text": "grammatic PDF documents with known positions of each", "orig": "grammatic PDF documents with known positions of each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 263.413, "r_x1": 66.989, "r_y1": 263.413, "r_x2": 66.989, "r_y2": 254.861, "r_x3": 50.112, "r_y3": 254.861, "coord_origin": "TOPLEFT"}, "text": "cell.", "orig": "cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.382, "r_y0": 263.413, "r_x1": 286.365, "r_y1": 263.413, "r_x2": 286.365, "r_y2": 254.861, "r_x3": 71.382, "r_y3": 254.861, "coord_origin": "TOPLEFT"}, "text": "The latter was the inspiration for the work of this pa-", "orig": "The latter was the inspiration for the work of this pa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 275.36800000000005, "r_x1": 64.777, "r_y1": 275.36800000000005, "r_x2": 64.777, "r_y2": 266.81600000000003, "r_x3": 50.112, "r_y3": 266.81600000000003, "coord_origin": "TOPLEFT"}, "text": "per.", "orig": "per.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. 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302.738, "coord_origin": "TOPLEFT"}, "text": "extraction.", "orig": "extraction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.849, "r_y0": 311.29, "r_x1": 286.365, "r_y1": 311.29, "r_x2": 286.365, "r_y2": 302.738, "r_x3": 138.849, "r_y3": 302.738, "coord_origin": "TOPLEFT"}, "text": "Note that one table cell can consti-", "orig": "Note that one table cell can consti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.245, "r_x1": 286.365, "r_y1": 323.245, "r_x2": 286.365, "r_y2": 314.693, "r_x3": 50.112, "r_y3": 314.693, "coord_origin": "TOPLEFT"}, "text": "tute out of multiple text-cells. To obtain the table-structure,", "orig": "tute out of multiple text-cells. To obtain the table-structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.2, "r_x1": 136.996, "r_y1": 335.2, "r_x2": 136.996, "r_y2": 326.648, "r_x3": 50.112, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "one creates an initial", "orig": "one creates an initial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 141.051, "r_y0": 335.2, "r_x1": 166.226, "r_y1": 335.2, "r_x2": 166.226, "r_y2": 326.648, "r_x3": 141.051, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "graph,", "orig": "graph,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.679, "r_y0": 335.2, "r_x1": 286.365, "r_y1": 335.2, "r_x2": 286.365, "r_y2": 326.648, "r_x3": 170.679, "r_y3": 326.648, "coord_origin": "TOPLEFT"}, "text": "where each of the text-cells", "orig": "where each of the text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.155, "r_x1": 260.442, "r_y1": 347.155, "r_x2": 260.442, "r_y2": 338.603, "r_x3": 50.112, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "becomes a node in the graph similar to [33, 34, 2].", "orig": "becomes a node in the graph similar to [33, 34, 2].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.45, "r_y0": 347.155, "r_x1": 286.365, "r_y1": 347.155, "r_x2": 286.365, "r_y2": 338.603, "r_x3": 266.45, "r_y3": 338.603, "coord_origin": "TOPLEFT"}, "text": "Each", "orig": "Each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.11, "r_x1": 286.365, "r_y1": 359.11, "r_x2": 286.365, "r_y2": 350.558, "r_x3": 50.112, "r_y3": 350.558, "coord_origin": "TOPLEFT"}, "text": "node is then associated with en embedding vector coming", "orig": "node is then associated with en embedding vector coming", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.066, "r_x1": 286.365, "r_y1": 371.066, "r_x2": 286.365, "r_y2": 362.514, "r_x3": 50.112, "r_y3": 362.514, "coord_origin": "TOPLEFT"}, "text": "from the encoded image, its coordinates and the encoded", "orig": "from the encoded image, its coordinates and the encoded", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.021, "r_x1": 67.397, "r_y1": 383.021, "r_x2": 67.397, "r_y2": 374.469, 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410.334, "coord_origin": "TOPLEFT"}, "text": "the text-cells and their content [18]. The purpose of a GCN", "orig": "the text-cells and their content [18]. The purpose of a GCN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 430.841, "r_x1": 286.365, "r_y1": 430.841, "r_x2": 286.365, "r_y2": 422.289, "r_x3": 50.112, "r_y3": 422.289, "coord_origin": "TOPLEFT"}, "text": "is to transform the input graph into a new graph, which re-", "orig": "is to transform the input graph into a new graph, which re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 442.797, "r_x1": 198.236, "r_y1": 442.797, "r_x2": 198.236, "r_y2": 434.245, "r_x3": 50.112, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "places the old links with new ones.", "orig": "places the old links with new ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.927, "r_y0": 442.797, "r_x1": 286.365, "r_y1": 442.797, "r_x2": 286.365, "r_y2": 434.245, "r_x3": 205.927, "r_y3": 434.245, "coord_origin": "TOPLEFT"}, "text": "The new links then", "orig": "The new links then", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 454.752, "r_x1": 165.618, "r_y1": 454.752, "r_x2": 165.618, "r_y2": 446.2, "r_x3": 50.112, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "represent the table-structure.", "orig": "represent the table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.417, "r_y0": 454.752, "r_x1": 286.365, "r_y1": 454.752, "r_x2": 286.365, "r_y2": 446.2, "r_x3": 171.417, "r_y3": 446.2, "coord_origin": "TOPLEFT"}, "text": "With this approach, one can", "orig": "With this approach, one can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 466.707, "r_x1": 242.36, "r_y1": 466.707, "r_x2": 242.36, "r_y2": 458.155, "r_x3": 50.112, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "avoid the need to build custom OCR decoders.", "orig": "avoid the need to build custom OCR decoders.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.408, "r_y0": 466.707, "r_x1": 286.365, "r_y1": 466.707, "r_x2": 286.365, "r_y2": 458.155, "r_x3": 248.408, "r_y3": 458.155, "coord_origin": "TOPLEFT"}, "text": "However,", "orig": "However,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 478.662, "r_x1": 286.365, "r_y1": 478.662, "r_x2": 286.365, "r_y2": 470.11, "r_x3": 50.112, "r_y3": 470.11, "coord_origin": "TOPLEFT"}, "text": "the quality of the reconstructed structure is not comparable", "orig": "the quality of the reconstructed structure is not comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 490.617, "r_x1": 186.5, "r_y1": 490.617, "r_x2": 186.5, "r_y2": 482.065, "r_x3": 50.112, "r_y3": 482.065, "coord_origin": "TOPLEFT"}, "text": "to the current state-of-the-art [18].", "orig": "to the current state-of-the-art [18].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 493.689, "r": 286.366, "b": 622.181, "coord_origin": "TOPLEFT"}, "confidence": 0.9875094294548035, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 502.645, "r_x1": 252.881, "r_y1": 502.645, "r_x2": 252.881, "r_y2": 493.689, "r_x3": 62.067, "r_y3": 493.689, "coord_origin": "TOPLEFT"}, "text": "Hybrid Deep Learning-Rule-Based approach", "orig": "Hybrid Deep Learning-Rule-Based approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.882, "r_y0": 502.629, "r_x1": 255.652, "r_y1": 502.629, "r_x2": 255.652, "r_y2": 494.077, "r_x3": 252.882, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.63, "r_y0": 502.629, "r_x1": 286.366, "r_y1": 502.629, "r_x2": 286.366, "r_y2": 494.077, "r_x3": 258.63, "r_y3": 494.077, "coord_origin": "TOPLEFT"}, "text": "Apop-", "orig": "Apop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 514.5840000000001, "r_x1": 286.365, "r_y1": 514.5840000000001, "r_x2": 286.365, "r_y2": 506.032, "r_x3": 50.112, "r_y3": 506.032, "coord_origin": "TOPLEFT"}, "text": "ular current model for table-structure identification is the", "orig": "ular current model for table-structure identification is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 526.539, "r_x1": 286.365, "r_y1": 526.539, "r_x2": 286.365, "r_y2": 517.9870000000001, "r_x3": 50.112, "r_y3": 517.9870000000001, "coord_origin": "TOPLEFT"}, "text": "use of a hybrid Deep Learning-Rule-Based approach similar", "orig": "use of a hybrid Deep Learning-Rule-Based approach similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 538.494, "r_x1": 286.365, "r_y1": 538.494, "r_x2": 286.365, "r_y2": 529.942, "r_x3": 50.112, "r_y3": 529.942, "coord_origin": "TOPLEFT"}, "text": "to [27, 29]. In this approach, one first detects the position of", "orig": "to [27, 29]. In this approach, one first detects the position of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 550.45, "r_x1": 286.365, "r_y1": 550.45, "r_x2": 286.365, "r_y2": 541.898, "r_x3": 50.112, "r_y3": 541.898, "coord_origin": "TOPLEFT"}, "text": "the table-cells with object detection (e.g. YoloVx or Mask-", "orig": "the table-cells with object detection (e.g. YoloVx or Mask-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 562.405, "r_x1": 286.365, "r_y1": 562.405, "r_x2": 286.365, "r_y2": 553.8530000000001, "r_x3": 50.112, "r_y3": 553.8530000000001, "coord_origin": "TOPLEFT"}, "text": "RCNN), then classifies the table into different types (from", "orig": "RCNN), then classifies the table into different types (from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 574.36, "r_x1": 59.527, "r_y1": 574.36, "r_x2": 59.527, "r_y2": 565.808, "r_x3": 50.112, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "its", "orig": "its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.259, "r_y0": 574.36, "r_x1": 95.8, "r_y1": 574.36, "r_x2": 95.8, "r_y2": 565.808, "r_x3": 64.259, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "images)", "orig": "images)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 100.523, "r_y0": 574.36, "r_x1": 145.105, "r_y1": 574.36, "r_x2": 145.105, "r_y2": 565.808, "r_x3": 100.523, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "and finally", "orig": "and finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.828, "r_y0": 574.36, "r_x1": 166.983, "r_y1": 574.36, "r_x2": 166.983, "r_y2": 565.808, "r_x3": 149.828, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "uses", "orig": "uses", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.715, "r_y0": 574.36, "r_x1": 205.768, "r_y1": 574.36, "r_x2": 205.768, "r_y2": 565.808, "r_x3": 171.715, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "different", "orig": "different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.5, "r_y0": 574.36, "r_x1": 244.243, "r_y1": 574.36, "r_x2": 244.243, "r_y2": 565.808, "r_x3": 210.5, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "rule-sets", "orig": "rule-sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.975, "r_y0": 574.36, "r_x1": 256.726, "r_y1": 574.36, "r_x2": 256.726, "r_y2": 565.808, "r_x3": 248.975, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.459, "r_y0": 574.36, "r_x1": 286.365, "r_y1": 574.36, "r_x2": 286.365, "r_y2": 565.808, "r_x3": 261.459, "r_y3": 565.808, "coord_origin": "TOPLEFT"}, "text": "obtain", "orig": "obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 586.315, "r_x1": 122.64, "r_y1": 586.315, "r_x2": 122.64, "r_y2": 577.763, "r_x3": 50.112, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "its table-structure.", "orig": "its table-structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.482, "r_y0": 586.315, "r_x1": 286.365, "r_y1": 586.315, "r_x2": 286.365, "r_y2": 577.763, "r_x3": 127.482, "r_y3": 577.763, "coord_origin": "TOPLEFT"}, "text": "Currently, this approach achieves state-", "orig": "Currently, this approach achieves state-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 598.27, "r_x1": 87.731, "r_y1": 598.27, "r_x2": 87.731, "r_y2": 589.718, "r_x3": 50.112, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "of-the-art", "orig": "of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.074, "r_y0": 598.27, "r_x1": 120.577, "r_y1": 598.27, "r_x2": 120.577, "r_y2": 589.718, "r_x3": 92.074, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "results,", "orig": "results,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.389, "r_y0": 598.27, "r_x1": 137.922, "r_y1": 598.27, "r_x2": 137.922, "r_y2": 589.718, "r_x3": 125.389, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "but", "orig": "but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.266, "r_y0": 598.27, "r_x1": 148.911, "r_y1": 598.27, "r_x2": 148.911, "r_y2": 589.718, "r_x3": 142.266, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.255, "r_y0": 598.27, "r_x1": 165.987, "r_y1": 598.27, "r_x2": 165.987, "r_y2": 589.718, "r_x3": 153.255, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "not", "orig": "not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.331, "r_y0": 598.27, "r_x1": 286.365, "r_y1": 598.27, "r_x2": 286.365, "r_y2": 589.718, "r_x3": 170.331, "r_y3": 589.718, "coord_origin": "TOPLEFT"}, "text": "an end-to-end deep-learning", "orig": "an end-to-end deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 610.225, "r_x1": 82.49, "r_y1": 610.225, "r_x2": 82.49, "r_y2": 601.673, "r_x3": 50.112, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "method.", "orig": "method.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.173, "r_y0": 610.225, "r_x1": 286.365, "r_y1": 610.225, "r_x2": 286.365, "r_y2": 601.673, "r_x3": 87.173, "r_y3": 601.673, "coord_origin": "TOPLEFT"}, "text": "As such, new rules need to be written if different", "orig": "As such, new rules need to be written if different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 622.181, "r_x1": 175.989, "r_y1": 622.181, "r_x2": 175.989, "r_y2": 613.629, "r_x3": 50.112, "r_y3": 613.629, "coord_origin": "TOPLEFT"}, "text": "types of tables are encountered.", "orig": "types of tables are encountered.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Hybrid Deep Learning-Rule-Based approach : Apopular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."}, {"label": "section_header", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 50.112, "t": 635.9449999999999, "r": 105.225, "b": 646.693, "coord_origin": "TOPLEFT"}, "confidence": 0.9423062205314636, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 646.693, "r_x1": 105.225, "r_y1": 646.693, "r_x2": 105.225, "r_y2": 635.9449999999999, "r_x3": 50.112, "r_y3": 635.9449999999999, "coord_origin": "TOPLEFT"}, "text": "3. Datasets", "orig": "3. Datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Datasets"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 656.694, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9862047433853149, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 62.067, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "We rely on large-scale datasets such as PubTabNet [37],", "orig": "We rely on large-scale datasets such as PubTabNet [37],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "FinTabNet [36], and TableBank [17] datasets to train and", "orig": "FinTabNet [36], and TableBank [17] datasets to train and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 133.479, "r_y1": 689.156, "r_x2": 133.479, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "evaluate our models.", "orig": "evaluate our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.271, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 138.271, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "These datasets span over various ap-", "orig": "These datasets span over various ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 166.246, "r_y1": 701.111, "r_x2": 166.246, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "pearance styles and content.", "orig": "pearance styles and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.688, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 173.688, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "We also introduce our own", "orig": "We also introduce our own", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "synthetically generated SynthTabNet dataset to fix an im-", "orig": "synthetically generated SynthTabNet dataset to fix an im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. 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The Train split consists of 54% simple and 46% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.949, "r_x1": 545.115, "r_y1": 447.949, "r_x2": 545.115, "r_y2": 439.397, "r_x3": 308.862, "r_y3": 439.397, "coord_origin": "TOPLEFT"}, "text": "plex tables and the Val split of 51% and 49% respectively.", "orig": "plex tables and the Val split of 51% and 49% respectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.904, "r_x1": 545.115, "r_y1": 459.904, "r_x2": 545.115, "r_y2": 451.352, "r_x3": 308.862, "r_y3": 451.352, "coord_origin": "TOPLEFT"}, "text": "The FinTabNet dataset contains 112k tables delivered as", "orig": "The FinTabNet dataset contains 112k tables delivered as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 471.859, "r_x1": 545.115, "r_y1": 471.859, "r_x2": 545.115, "r_y2": 463.307, "r_x3": 308.862, "r_y3": 463.307, "coord_origin": "TOPLEFT"}, "text": "single-page PDF documents with mixed table structures and", "orig": "single-page PDF documents with mixed table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 483.814, "r_x1": 358.984, "r_y1": 483.814, "r_x2": 358.984, "r_y2": 475.262, "r_x3": 308.862, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "text content.", "orig": "text content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.121, "r_y0": 483.814, "r_x1": 545.115, "r_y1": 483.814, "r_x2": 545.115, "r_y2": 475.262, "r_x3": 365.121, "r_y3": 475.262, "coord_origin": "TOPLEFT"}, "text": "Similarly to the PubTabNet, the annotations", "orig": "Similarly to the PubTabNet, the annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 495.77, "r_x1": 545.115, "r_y1": 495.77, "r_x2": 545.115, "r_y2": 487.218, "r_x3": 308.862, "r_y3": 487.218, "coord_origin": "TOPLEFT"}, "text": "of FinTabNet include the table structure in HTML, the to-", "orig": "of FinTabNet include the table structure in HTML, the to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 507.725, "r_x1": 545.115, "r_y1": 507.725, "r_x2": 545.115, "r_y2": 499.173, "r_x3": 308.862, "r_y3": 499.173, "coord_origin": "TOPLEFT"}, "text": "kenized text and the bounding boxes on a table cell basis.", "orig": "kenized text and the bounding boxes on a table cell basis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 519.6800000000001, "r_x1": 545.115, "r_y1": 519.6800000000001, "r_x2": 545.115, "r_y2": 511.128, "r_x3": 308.862, "r_y3": 511.128, "coord_origin": "TOPLEFT"}, "text": "The dataset is divided into Train, Test and Val splits (81%,", "orig": "The dataset is divided into Train, Test and Val splits (81%,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.635, "r_x1": 545.115, "r_y1": 531.635, "r_x2": 545.115, "r_y2": 523.0830000000001, "r_x3": 308.862, "r_y3": 523.0830000000001, "coord_origin": "TOPLEFT"}, "text": "9.5%, 9.5%), and each one is almost equally divided into", "orig": "9.5%, 9.5%), and each one is almost equally divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.59, "r_x1": 545.115, "r_y1": 543.59, "r_x2": 545.115, "r_y2": 535.038, "r_x3": 308.862, "r_y3": 535.038, "coord_origin": "TOPLEFT"}, "text": "simple and complex tables (Train: 48% simple, 52% com-", "orig": "simple and complex tables (Train: 48% simple, 52% com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.545, "r_x1": 350.964, "r_y1": 555.545, "r_x2": 350.964, "r_y2": 546.9929999999999, "r_x3": 308.862, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "plex, Test:", "orig": "plex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.467, "r_y0": 555.545, "r_x1": 490.082, "r_y1": 555.545, "r_x2": 490.082, "r_y2": 546.9929999999999, "r_x3": 355.467, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "48% simple, 52% complex, Test:", "orig": "48% simple, 52% complex, Test:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.595, "r_y0": 555.545, "r_x1": 545.115, "r_y1": 555.545, "r_x2": 545.115, "r_y2": 546.9929999999999, "r_x3": 494.595, "r_y3": 546.9929999999999, "coord_origin": "TOPLEFT"}, "text": "53% simple,", "orig": "53% simple,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.501, "r_x1": 370.6, "r_y1": 567.501, "r_x2": 370.6, "r_y2": 558.9490000000001, "r_x3": 308.862, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "47% complex).", "orig": "47% complex).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.737, "r_y0": 567.501, "r_x1": 545.115, "r_y1": 567.501, "r_x2": 545.115, "r_y2": 558.9490000000001, "r_x3": 376.737, "r_y3": 558.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Finally the TableBank dataset consists of", "orig": "Finally the TableBank dataset consists of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 579.456, "r_x1": 545.115, "r_y1": 579.456, "r_x2": 545.115, "r_y2": 570.904, "r_x3": 308.862, "r_y3": 570.904, "coord_origin": "TOPLEFT"}, "text": "145k tables provided as JPEG images. The latter has anno-", "orig": "145k tables provided as JPEG images. The latter has anno-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 591.4110000000001, "r_x1": 545.115, "r_y1": 591.4110000000001, "r_x2": 545.115, "r_y2": 582.859, "r_x3": 308.862, "r_y3": 582.859, "coord_origin": "TOPLEFT"}, "text": "tations for the table structure, but only few with bounding", "orig": "tations for the table structure, but only few with bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 603.366, "r_x1": 403.726, "r_y1": 603.366, "r_x2": 403.726, "r_y2": 594.814, "r_x3": 308.862, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "boxes of the table cells.", "orig": "boxes of the table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.731, "r_y0": 603.366, "r_x1": 545.115, "r_y1": 603.366, "r_x2": 545.115, "r_y2": 594.814, "r_x3": 407.731, "r_y3": 594.814, "coord_origin": "TOPLEFT"}, "text": "The entire dataset consists of sim-", "orig": "The entire dataset consists of sim-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 615.321, "r_x1": 545.115, "r_y1": 615.321, "r_x2": 545.115, "r_y2": 606.769, "r_x3": 308.862, "r_y3": 606.769, "coord_origin": "TOPLEFT"}, "text": "ple tables and it is divided into 90% Train, 3% Test and 7%", "orig": "ple tables and it is divided into 90% Train, 3% Test and 7%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 627.2760000000001, "r_x1": 348.164, "r_y1": 627.2760000000001, "r_x2": 348.164, "r_y2": 618.7239999999999, "r_x3": 308.862, "r_y3": 618.7239999999999, "coord_origin": "TOPLEFT"}, "text": "Val splits.", "orig": "Val splits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as 'simple' when it does not contain row spans or column spans, otherwise it is 'complex'. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 632.783, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9840091466903687, "cells": [{"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 641.335, "r_x1": 545.115, "r_y1": 641.335, "r_x2": 545.115, "r_y2": 632.783, "r_x3": 320.817, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "Due to the heterogeneity across the dataset formats, it", "orig": "Due to the heterogeneity across the dataset formats, it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.29, "r_x1": 545.115, "r_y1": 653.29, "r_x2": 545.115, "r_y2": 644.738, "r_x3": 308.862, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "was necessary to combine all available data into one homog-", "orig": "was necessary to combine all available data into one homog-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 665.245, "r_x1": 545.115, "r_y1": 665.245, "r_x2": 545.115, "r_y2": 656.693, "r_x3": 308.862, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "enized dataset before we could train our models for practi-", "orig": "enized dataset before we could train our models for practi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 361.245, "r_y1": 677.201, "r_x2": 361.245, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "cal purposes.", "orig": "cal purposes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.44, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 365.44, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "Given the size of PubTabNet, we adopted its", "orig": "Given the size of PubTabNet, we adopted its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "annotation format and we extracted and converted all tables", "orig": "annotation format and we extracted and converted all tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 487.322, "r_y1": 701.111, "r_x2": 487.322, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "as PNG images with a resolution of 72 dpi.", "orig": "as PNG images with a resolution of 72 dpi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.901, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 492.901, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "we have filtered out tables with extreme sizes due to small", "orig": "we have filtered out tables with extreme sizes due to small", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small"}], "headers": [{"label": "page_footer", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8715606927871704, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "amount of such tables, and kept only those ones ranging", "orig": "amount of such tables, and kept only those ones ranging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 212.283, "r_y1": 95.98400000000004, "r_x2": 212.283, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "between 1*1 and 20*10 (rows/columns).", "orig": "between 1*1 and 20*10 (rows/columns).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 109.78099999999995, "r_x1": 286.365, "r_y1": 109.78099999999995, "r_x2": 286.365, "r_y2": 101.22900000000004, "r_x3": 62.067, "r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 121.73599999999999, "r_x1": 176.458, "r_y1": 121.73599999999999, "r_x2": 176.458, "r_y2": 113.18399999999997, "r_x3": 50.112, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "is essential to train our models.", "orig": "is essential to train our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.971, "r_y0": 121.73599999999999, "r_x1": 286.365, "r_y1": 121.73599999999999, "r_x2": 286.365, "r_y2": 113.18399999999997, "r_x3": 180.971, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "In order to distinguish be-", "orig": "In order to distinguish be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 133.69100000000003, "r_x1": 286.365, "r_y1": 133.69100000000003, "r_x2": 286.365, "r_y2": 125.13900000000001, "r_x3": 50.112, "r_y3": 125.13900000000001, "coord_origin": "TOPLEFT"}, "text": "tween empty and non-empty bounding boxes, we have in-", "orig": "tween empty and non-empty bounding boxes, we have in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 145.64700000000005, "r_x1": 286.365, "r_y1": 145.64700000000005, "r_x2": 286.365, "r_y2": 137.09500000000003, "r_x3": 50.112, "r_y3": 137.09500000000003, "coord_origin": "TOPLEFT"}, "text": "troduced a binary class in the annotation. Unfortunately, the", "orig": "troduced a binary class in the annotation. Unfortunately, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 157.60199999999998, "r_x1": 286.365, "r_y1": 157.60199999999998, "r_x2": 286.365, "r_y2": 149.04999999999995, "r_x3": 50.112, "r_y3": 149.04999999999995, "coord_origin": "TOPLEFT"}, "text": "original datasets either omit the bounding boxes for whole", "orig": "original datasets either omit the bounding boxes for whole", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 169.55700000000002, "r_x1": 94.127, "r_y1": 169.55700000000002, "r_x2": 94.127, "r_y2": 161.005, "r_x3": 50.112, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "tables (e.g.", "orig": "tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.959, "r_y0": 169.55700000000002, "r_x1": 286.365, "r_y1": 169.55700000000002, "r_x2": 286.365, "r_y2": 161.005, "r_x3": 98.959, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "TableBank) or they narrow their scope only to", "orig": "TableBank) or they narrow their scope only to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 181.51199999999994, "r_x1": 116.951, "r_y1": 181.51199999999994, "r_x2": 116.951, "r_y2": 172.96000000000004, "r_x3": 50.112, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "non-empty cells.", "orig": "non-empty cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 121.345, "r_y0": 181.51199999999994, "r_x1": 286.365, "r_y1": 181.51199999999994, "r_x2": 286.365, "r_y2": 172.96000000000004, "r_x3": 121.345, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Therefore, it was imperative to introduce", "orig": "Therefore, it was imperative to introduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 193.46699999999998, "r_x1": 286.365, "r_y1": 193.46699999999998, "r_x2": 286.365, "r_y2": 184.91499999999996, "r_x3": 50.112, "r_y3": 184.91499999999996, "coord_origin": "TOPLEFT"}, "text": "a data pre-processing procedure that generates the missing", "orig": "a data pre-processing procedure that generates the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 205.423, "r_x1": 286.365, "r_y1": 205.423, "r_x2": 286.365, "r_y2": 196.87099999999998, "r_x3": 50.112, "r_y3": 196.87099999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes out of the annotation information. This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 289.109, "r_x1": 68.652, "r_y1": 289.109, "r_x2": 68.652, "r_y2": 280.557, "r_x3": 50.112, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "bles.", "orig": "bles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.566, "r_y0": 289.109, "r_x1": 286.365, "r_y1": 289.109, "r_x2": 286.365, "r_y2": 280.557, "r_x3": 75.566, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "Regarding FinTabNet, 68% of the simple and 98%", "orig": "Regarding FinTabNet, 68% of the simple and 98%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 301.064, "r_x1": 286.365, "r_y1": 301.064, "r_x2": 286.365, "r_y2": 292.512, "r_x3": 50.112, "r_y3": 292.512, "coord_origin": "TOPLEFT"}, "text": "of the complex tables require the generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, 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Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. The corpora to gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 50.112, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "ate the table text consists of the most frequent terms appear-", "orig": "ate the table text consists of the most frequent terms appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "ing in PubTabNet and FinTabNet together with randomly", "orig": "ing in PubTabNet and FinTabNet together with randomly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 109.519, "r_y1": 689.156, "r_x2": 109.519, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "generated text.", "orig": "generated text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.337, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 115.337, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "The first two synthetic datasets have been", "orig": "The first two synthetic datasets have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "fine-tuned to mimic the appearance of the original datasets", "orig": "fine-tuned to mimic the appearance of the original datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "but encompass more complicated table structures. The third", "orig": "but encompass more complicated table structures. The third", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 412.332, "r_y0": 82.43499999999995, "r_x1": 430.902, "r_y1": 82.43499999999995, "r_x2": 430.902, "r_y2": 73.88300000000004, "r_x3": 412.332, "r_y3": 73.88300000000004, "coord_origin": "TOPLEFT"}, "text": "Tags", "orig": "Tags", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.857, "r_y0": 82.43499999999995, "r_x1": 464.446, "r_y1": 82.43499999999995, "r_x2": 464.446, "r_y2": 73.88300000000004, "r_x3": 442.857, "r_y3": 73.88300000000004, "coord_origin": "TOPLEFT"}, "text": "Bbox", "orig": "Bbox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.786, "r_y0": 82.43499999999995, "r_x1": 494.942, "r_y1": 82.43499999999995, 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Lastly, we have", "orig": "last one contains tables with sparse content. Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 319.153, "r_y0": 497.541, "r_x1": 509.021, "r_y1": 497.541, "r_x2": 509.021, "r_y2": 488.989, "r_x3": 319.153, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "composed of three main components, see Fig.", "orig": "composed of three main components, see Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 515.586, "r_y0": 497.541, "r_x1": 523.058, "r_y1": 497.541, "r_x2": 523.058, "r_y2": 488.989, "r_x3": 515.586, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 497.541, "r_x1": 545.115, "r_y1": 497.541, "r_x2": 545.115, "r_y2": 488.989, "r_x3": 529.623, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.452, "r_x1": 319.93, "r_y1": 521.452, "r_x2": 319.93, "r_y2": 512.9, "r_x3": 308.862, "r_y3": 512.9, "coord_origin": "TOPLEFT"}, "text": "tor", "orig": "tor", "text_direction": 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As for spanning cells, such as row", "orig": "the Cell BBox Decoder. As for spanning cells, such as row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.183, "r_x1": 483.118, "r_y1": 593.183, "r_x2": 483.118, "r_y2": 584.631, "r_x3": 308.862, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "or column span, the tag is broken down to '", "orig": "or column span, the tag is broken down to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.119, "r_y0": 593.019, "r_x1": 490.868, "r_y1": 593.019, "r_x2": 490.868, "r_y2": 584.313, "r_x3": 483.119, "r_y3": 584.313, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.868, "r_y0": 593.183, "r_x1": 545.114, "r_y1": 593.183, "r_x2": 545.114, "r_y2": 584.631, "r_x3": 490.868, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "', 'rowspan='", "orig": "', 'rowspan='", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.138, "r_x1": 545.115, "r_y1": 605.138, "r_x2": 545.115, "r_y2": 596.586, "r_x3": 308.862, "r_y3": 596.586, "coord_origin": "TOPLEFT"}, "text": "or 'colspan=', with the number of spanning cells (attribute),", "orig": "or 'colspan=', with the number of spanning cells (attribute),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.093, "r_x1": 329.644, "r_y1": 617.093, "r_x2": 329.644, "r_y2": 608.5409999999999, "r_x3": 308.862, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "and '", "orig": "and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.646, "r_y0": 616.929, "r_x1": 337.395, "r_y1": 616.929, "r_x2": 337.395, "r_y2": 608.223, "r_x3": 329.646, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.394, "r_y0": 617.093, "r_x1": 343.202, "r_y1": 617.093, "r_x2": 343.202, "r_y2": 608.5409999999999, "r_x3": 337.394, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "'.", "orig": "'.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.064, "r_y0": 617.093, "r_x1": 468.591, "r_y1": 617.093, "r_x2": 468.591, "r_y2": 608.5409999999999, "r_x3": 348.064, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "The hidden state attached to '", "orig": "The hidden state attached to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 468.595, "r_y0": 616.929, "r_x1": 476.344, "r_y1": 616.929, "r_x2": 476.344, "r_y2": 608.223, "r_x3": 468.595, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.344, "r_y0": 617.093, "r_x1": 545.116, "r_y1": 617.093, "r_x2": 545.116, "r_y2": 608.5409999999999, "r_x3": 476.344, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "' is passed to the", "orig": "' is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.048, "r_x1": 545.115, "r_y1": 629.048, "r_x2": 545.115, "r_y2": 620.496, "r_x3": 308.862, "r_y3": 620.496, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "orig": "Cell BBox Decoder. A shared feed forward network (FFN)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.0029999999999, "r_x1": 533.2, "r_y1": 641.0029999999999, "r_x2": 533.2, "r_y2": 632.451, "r_x3": 308.862, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "receives the hidden states from the Structure Decoder,", "orig": "receives the hidden states from the Structure Decoder,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.364, "r_y0": 641.0029999999999, "r_x1": 545.115, "r_y1": 641.0029999999999, "r_x2": 545.115, "r_y2": 632.451, "r_x3": 537.364, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.9590000000001, "r_x1": 545.115, "r_y1": 652.9590000000001, "r_x2": 545.115, "r_y2": 644.407, "r_x3": 308.862, "r_y3": 644.407, "coord_origin": "TOPLEFT"}, "text": "provide the final detection predictions of the bounding box", "orig": "provide the final detection predictions of the bounding box", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.914, "r_x1": 449.424, "r_y1": 664.914, "r_x2": 449.424, "r_y2": 656.362, "r_x3": 308.862, "r_y3": 656.362, "coord_origin": "TOPLEFT"}, "text": "coordinates and their classification.", "orig": "coordinates and their classification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.217, "r_x1": 431.91, "r_y1": 677.217, "r_x2": 431.91, "r_y2": 668.261, "r_x3": 320.817, "r_y3": 668.261, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network.", "orig": "CNN Backbone Network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.499, "r_y0": 677.201, "r_x1": 545.113, "r_y1": 677.201, "r_x2": 545.113, "r_y2": 668.649, "r_x3": 439.499, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "A ResNet-18 CNN is the", "orig": "A ResNet-18 CNN is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "backbone that receives the table image and encodes it as a", "orig": "backbone that receives the table image and encodes it as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "vector of predefined length. The network has been modified", "orig": "vector of predefined length. The network has been modified", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "by removing the linear and pooling layer, as we are not per-", "orig": "by removing the linear and pooling layer, as we are not per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 95.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9611433148384094, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "amount of such tables, and kept only those ones ranging", "orig": "amount of such tables, and kept only those ones ranging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 212.283, "r_y1": 95.98400000000004, "r_x2": 212.283, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "between 1*1 and 20*10 (rows/columns).", "orig": "between 1*1 and 20*10 (rows/columns).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 101.22900000000004, "r": 286.365, "b": 313.019, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 109.78099999999995, "r_x1": 286.365, "r_y1": 109.78099999999995, "r_x2": 286.365, "r_y2": 101.22900000000004, "r_x3": 62.067, "r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 121.73599999999999, "r_x1": 176.458, "r_y1": 121.73599999999999, "r_x2": 176.458, "r_y2": 113.18399999999997, "r_x3": 50.112, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "is essential to train our models.", "orig": "is essential to train our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.971, "r_y0": 121.73599999999999, "r_x1": 286.365, "r_y1": 121.73599999999999, "r_x2": 286.365, "r_y2": 113.18399999999997, "r_x3": 180.971, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "In order to distinguish be-", "orig": "In order to distinguish be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 133.69100000000003, "r_x1": 286.365, "r_y1": 133.69100000000003, "r_x2": 286.365, "r_y2": 125.13900000000001, "r_x3": 50.112, "r_y3": 125.13900000000001, "coord_origin": "TOPLEFT"}, "text": "tween empty and non-empty bounding boxes, we have in-", "orig": "tween empty and non-empty bounding boxes, we have in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 145.64700000000005, "r_x1": 286.365, "r_y1": 145.64700000000005, "r_x2": 286.365, "r_y2": 137.09500000000003, "r_x3": 50.112, "r_y3": 137.09500000000003, "coord_origin": "TOPLEFT"}, "text": "troduced a binary class in the annotation. Unfortunately, the", "orig": "troduced a binary class in the annotation. Unfortunately, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 157.60199999999998, "r_x1": 286.365, "r_y1": 157.60199999999998, "r_x2": 286.365, "r_y2": 149.04999999999995, "r_x3": 50.112, "r_y3": 149.04999999999995, "coord_origin": "TOPLEFT"}, "text": "original datasets either omit the bounding boxes for whole", "orig": "original datasets either omit the bounding boxes for whole", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 169.55700000000002, "r_x1": 94.127, "r_y1": 169.55700000000002, "r_x2": 94.127, "r_y2": 161.005, "r_x3": 50.112, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "tables (e.g.", "orig": "tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.959, "r_y0": 169.55700000000002, "r_x1": 286.365, "r_y1": 169.55700000000002, "r_x2": 286.365, "r_y2": 161.005, "r_x3": 98.959, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "TableBank) or they narrow their scope only to", "orig": "TableBank) or they narrow their scope only to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 181.51199999999994, "r_x1": 116.951, "r_y1": 181.51199999999994, "r_x2": 116.951, "r_y2": 172.96000000000004, "r_x3": 50.112, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "non-empty cells.", "orig": "non-empty cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 121.345, "r_y0": 181.51199999999994, "r_x1": 286.365, "r_y1": 181.51199999999994, "r_x2": 286.365, "r_y2": 172.96000000000004, "r_x3": 121.345, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Therefore, it was imperative to introduce", "orig": "Therefore, it was imperative to introduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 193.46699999999998, "r_x1": 286.365, "r_y1": 193.46699999999998, "r_x2": 286.365, "r_y2": 184.91499999999996, "r_x3": 50.112, "r_y3": 184.91499999999996, "coord_origin": "TOPLEFT"}, "text": "a data pre-processing procedure that generates the missing", "orig": "a data pre-processing procedure that generates the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 205.423, "r_x1": 286.365, "r_y1": 205.423, "r_x2": 286.365, "r_y2": 196.87099999999998, "r_x3": 50.112, "r_y3": 196.87099999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes out of the annotation information. This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 289.109, "r_x1": 68.652, "r_y1": 289.109, "r_x2": 68.652, "r_y2": 280.557, "r_x3": 50.112, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "bles.", "orig": "bles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.566, "r_y0": 289.109, "r_x1": 286.365, "r_y1": 289.109, "r_x2": 286.365, "r_y2": 280.557, "r_x3": 75.566, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "Regarding FinTabNet, 68% of the simple and 98%", "orig": "Regarding FinTabNet, 68% of the simple and 98%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 301.064, "r_x1": 286.365, "r_y1": 301.064, "r_x2": 286.365, "r_y2": 292.512, "r_x3": 50.112, "r_y3": 292.512, "coord_origin": "TOPLEFT"}, "text": "of the complex tables require the generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 318.264, "r": 286.365, "b": 434.413, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 338.772, "r_x1": 60.075, "r_y1": 338.772, "r_x2": 60.075, "r_y2": 330.22, "r_x3": 50.112, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "all", "orig": "all", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 65.146, "r_y0": 338.772, "r_x1": 96.687, "r_y1": 338.772, "r_x2": 96.687, "r_y2": 330.22, "r_x3": 65.146, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.768, "r_y0": 338.772, "r_x1": 113.932, "r_y1": 338.772, "r_x2": 113.932, "r_y2": 330.22, "r_x3": 101.768, "r_y3": 330.22, 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"r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "simpler", "orig": "simpler", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.836, "r_y0": 338.772, "r_x1": 263.571, "r_y1": 338.772, "r_x2": 263.571, "r_y2": 330.22, "r_x3": 224.836, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "structures", "orig": "structures", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 268.652, "r_y0": 338.772, "r_x1": 286.365, "r_y1": 338.772, "r_x2": 286.365, "r_y2": 330.22, "r_x3": 268.652, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 350.727, "r_x1": 286.365, "r_y1": 350.727, "r_x2": 286.365, "r_y2": 342.175, "r_x3": 50.112, "r_y3": 342.175, "coord_origin": "TOPLEFT"}, "text": "fewer number of rows/columns. Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.658, "r": 286.367, "b": 627.538, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 632.783, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. 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Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 320.817, "t": 298.041, "r": 542.744, "b": 306.593, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 342.208, "r": 545.115, "b": 446.401, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 477.034, "r": 545.116, "b": 664.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": 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488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, 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{"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.4069999999999, "r_x1": 436.194, "r_y1": 533.4069999999999, "r_x2": 436.194, "r_y2": 524.855, "r_x3": 308.862, "r_y3": 524.855, "coord_origin": "TOPLEFT"}, "text": "encoded image is passed to the", "orig": "encoded image is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.526, "r_y0": 533.264, "r_x1": 513.867, "r_y1": 533.264, "r_x2": 513.867, "r_y2": 524.6759999999999, "r_x3": 439.526, "r_y3": 524.6759999999999, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 517.432, "r_y0": 533.4069999999999, "r_x1": 545.108, "r_y1": 533.4069999999999, "r_x2": 545.108, "r_y2": 524.855, "r_x3": 517.432, 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Unfortunately, the", "orig": "troduced a binary class in the annotation. 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This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 289.109, "r_x1": 68.652, "r_y1": 289.109, "r_x2": 68.652, "r_y2": 280.557, "r_x3": 50.112, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "bles.", "orig": "bles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.566, "r_y0": 289.109, "r_x1": 286.365, "r_y1": 289.109, "r_x2": 286.365, "r_y2": 280.557, "r_x3": 75.566, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "Regarding FinTabNet, 68% of the simple and 98%", "orig": "Regarding FinTabNet, 68% of the simple and 98%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 301.064, "r_x1": 286.365, "r_y1": 301.064, "r_x2": 286.365, "r_y2": 292.512, "r_x3": 50.112, "r_y3": 292.512, "coord_origin": "TOPLEFT"}, "text": "of the complex tables require the generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 318.264, "r": 286.365, "b": 434.413, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 338.772, "r_x1": 60.075, "r_y1": 338.772, "r_x2": 60.075, "r_y2": 330.22, "r_x3": 50.112, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "all", "orig": "all", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 65.146, "r_y0": 338.772, "r_x1": 96.687, "r_y1": 338.772, "r_x2": 96.687, "r_y2": 330.22, "r_x3": 65.146, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.768, "r_y0": 338.772, "r_x1": 113.932, "r_y1": 338.772, "r_x2": 113.932, "r_y2": 330.22, "r_x3": 101.768, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.003, "r_y0": 338.772, "r_x1": 148.533, "r_y1": 338.772, "r_x2": 148.533, "r_y2": 330.22, "r_x3": 119.003, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "skewed", "orig": "skewed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.613, "r_y0": 338.772, "r_x1": 184.806, "r_y1": 338.772, "r_x2": 184.806, "r_y2": 330.22, "r_x3": 153.613, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "towards", "orig": "towards", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 189.877, "r_y0": 338.772, "r_x1": 219.765, "r_y1": 338.772, "r_x2": 219.765, "r_y2": 330.22, "r_x3": 189.877, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "simpler", "orig": "simpler", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.836, "r_y0": 338.772, "r_x1": 263.571, "r_y1": 338.772, "r_x2": 263.571, "r_y2": 330.22, "r_x3": 224.836, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "structures", "orig": "structures", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 268.652, "r_y0": 338.772, "r_x1": 286.365, "r_y1": 338.772, "r_x2": 286.365, "r_y2": 330.22, "r_x3": 268.652, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 350.727, "r_x1": 286.365, "r_y1": 350.727, "r_x2": 286.365, "r_y2": 342.175, "r_x3": 50.112, "r_y3": 342.175, "coord_origin": "TOPLEFT"}, "text": "fewer number of rows/columns. Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.658, "r": 286.367, "b": 627.538, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 632.783, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. The corpora to gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 50.112, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "ate the table text consists of the most frequent terms appear-", "orig": "ate the table text consists of the most frequent terms appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "ing in PubTabNet and FinTabNet together with randomly", "orig": "ing in PubTabNet and FinTabNet together with randomly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 109.519, "r_y1": 689.156, "r_x2": 109.519, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "generated text.", "orig": "generated text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.337, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 115.337, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "The first two synthetic datasets have been", "orig": "The first two synthetic datasets have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "fine-tuned to mimic the appearance of the original datasets", "orig": "fine-tuned to mimic the appearance of the original datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "but encompass more complicated table structures. The third", "orig": "but encompass more complicated table structures. The third", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. 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366.276, "r_y2": 203.79499999999996, "r_x3": 308.862, "r_y3": 203.79499999999996, "coord_origin": "TOPLEFT"}, "text": "and Fintabnet.", "orig": "and Fintabnet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.58, "r_y0": 212.34699999999998, "r_x1": 545.115, "r_y1": 212.34699999999998, "r_x2": 545.115, "r_y2": 203.79499999999996, "r_x3": 370.58, "r_y3": 203.79499999999996, "coord_origin": "TOPLEFT"}, "text": "(**) The combined dataset is the processed", "orig": "(**) The combined dataset is the processed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 224.303, "r_x1": 523.935, "r_y1": 224.303, "r_x2": 523.935, "r_y2": 215.75099999999998, "r_x3": 308.862, "r_y3": 215.75099999999998, "coord_origin": "TOPLEFT"}, "text": "combination of PubTabNet, Fintabnet and TableBank.", "orig": "combination of PubTabNet, Fintabnet and TableBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.889, "r": 545.115, "b": 294.306, "coord_origin": "TOPLEFT"}, "confidence": 0.978954017162323, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.44100000000003, "r_x1": 545.115, "r_y1": 258.44100000000003, "r_x2": 545.115, "r_y2": 249.889, "r_x3": 308.862, "r_y3": 249.889, "coord_origin": "TOPLEFT"}, "text": "one adopts a colorful appearance with high contrast and the", "orig": "one adopts a colorful appearance with high contrast and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.39599999999996, "r_x1": 545.115, "r_y1": 270.39599999999996, "r_x2": 545.115, "r_y2": 261.84400000000005, "r_x3": 308.862, "r_y3": 261.84400000000005, "coord_origin": "TOPLEFT"}, "text": "last one contains tables with sparse content. Lastly, we have", "orig": "last one contains tables with sparse content. Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.817, "t": 298.041, "r": 542.744, "b": 306.593, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 342.208, "r": 545.115, "b": 446.401, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 477.034, "r": 545.116, "b": 664.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 319.153, "r_y0": 497.541, "r_x1": 509.021, "r_y1": 497.541, "r_x2": 509.021, "r_y2": 488.989, "r_x3": 319.153, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "composed of three main components, see Fig.", "orig": "composed of three main components, see Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 515.586, "r_y0": 497.541, "r_x1": 523.058, "r_y1": 497.541, "r_x2": 523.058, "r_y2": 488.989, "r_x3": 515.586, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 497.541, "r_x1": 545.115, "r_y1": 497.541, "r_x2": 545.115, "r_y2": 488.989, "r_x3": 529.623, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.452, "r_x1": 319.93, "r_y1": 521.452, 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"text": "to pro-", "orig": "to pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.362, "r_x1": 545.115, "r_y1": 545.362, "r_x2": 545.115, "r_y2": 536.81, "r_x3": 308.862, "r_y3": 536.81, "coord_origin": "TOPLEFT"}, "text": "duce a sequence of HTML tags that represent the structure", "orig": "duce a sequence of HTML tags that represent the structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.317, "r_x1": 358.545, "r_y1": 557.317, "r_x2": 358.545, "r_y2": 548.765, "r_x3": 308.862, "r_y3": 548.765, "coord_origin": "TOPLEFT"}, "text": "of the table.", "orig": "of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.191, 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state of that cell is passed to", "orig": "') the hidden state of that cell is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.2280000000001, "r_x1": 545.115, "r_y1": 581.2280000000001, "r_x2": 545.115, "r_y2": 572.6759999999999, "r_x3": 308.862, "r_y3": 572.6759999999999, "coord_origin": "TOPLEFT"}, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "orig": "the Cell BBox Decoder. As for spanning cells, such as row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.183, "r_x1": 483.118, "r_y1": 593.183, "r_x2": 483.118, "r_y2": 584.631, "r_x3": 308.862, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "or column span, the tag is broken down to '", "orig": "or column span, the tag is broken down to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.119, "r_y0": 593.019, "r_x1": 490.868, "r_y1": 593.019, "r_x2": 490.868, "r_y2": 584.313, "r_x3": 483.119, "r_y3": 584.313, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.868, "r_y0": 593.183, "r_x1": 545.114, "r_y1": 593.183, "r_x2": 545.114, "r_y2": 584.631, "r_x3": 490.868, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "', 'rowspan='", "orig": "', 'rowspan='", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.138, "r_x1": 545.115, "r_y1": 605.138, "r_x2": 545.115, "r_y2": 596.586, "r_x3": 308.862, "r_y3": 596.586, "coord_origin": "TOPLEFT"}, "text": "or 'colspan=', with the number of spanning cells (attribute),", "orig": "or 'colspan=', with the number of spanning cells (attribute),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.093, "r_x1": 329.644, "r_y1": 617.093, "r_x2": 329.644, "r_y2": 608.5409999999999, "r_x3": 308.862, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "and '", "orig": "and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.646, "r_y0": 616.929, "r_x1": 337.395, "r_y1": 616.929, "r_x2": 337.395, "r_y2": 608.223, "r_x3": 329.646, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.394, "r_y0": 617.093, "r_x1": 343.202, "r_y1": 617.093, "r_x2": 343.202, "r_y2": 608.5409999999999, "r_x3": 337.394, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "'.", "orig": "'.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.064, "r_y0": 617.093, "r_x1": 468.591, "r_y1": 617.093, "r_x2": 468.591, "r_y2": 608.5409999999999, "r_x3": 348.064, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "The hidden state attached to '", "orig": "The hidden state attached to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 468.595, "r_y0": 616.929, "r_x1": 476.344, "r_y1": 616.929, "r_x2": 476.344, "r_y2": 608.223, "r_x3": 468.595, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.344, "r_y0": 617.093, "r_x1": 545.116, "r_y1": 617.093, "r_x2": 545.116, "r_y2": 608.5409999999999, "r_x3": 476.344, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "' is passed to the", "orig": "' is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.048, "r_x1": 545.115, "r_y1": 629.048, "r_x2": 545.115, "r_y2": 620.496, "r_x3": 308.862, "r_y3": 620.496, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "orig": "Cell BBox Decoder. A shared feed forward network (FFN)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.0029999999999, "r_x1": 533.2, "r_y1": 641.0029999999999, "r_x2": 533.2, "r_y2": 632.451, "r_x3": 308.862, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "receives the hidden states from the Structure Decoder,", "orig": "receives the hidden states from the Structure Decoder,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.364, "r_y0": 641.0029999999999, "r_x1": 545.115, "r_y1": 641.0029999999999, "r_x2": 545.115, "r_y2": 632.451, "r_x3": 537.364, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.9590000000001, "r_x1": 545.115, "r_y1": 652.9590000000001, "r_x2": 545.115, "r_y2": 644.407, "r_x3": 308.862, "r_y3": 644.407, "coord_origin": "TOPLEFT"}, "text": "provide the final detection predictions of the bounding box", "orig": "provide the final detection predictions of the bounding box", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.914, "r_x1": 449.424, "r_y1": 664.914, "r_x2": 449.424, "r_y2": 656.362, "r_x3": 308.862, "r_y3": 656.362, "coord_origin": "TOPLEFT"}, "text": "coordinates and their classification.", "orig": "coordinates and their classification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 668.261, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.217, "r_x1": 431.91, "r_y1": 677.217, "r_x2": 431.91, "r_y2": 668.261, "r_x3": 320.817, "r_y3": 668.261, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network.", "orig": "CNN Backbone Network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.499, "r_y0": 677.201, "r_x1": 545.113, "r_y1": 677.201, "r_x2": 545.113, "r_y2": 668.649, "r_x3": 439.499, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "A ResNet-18 CNN is the", "orig": "A ResNet-18 CNN is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "backbone that receives the table image and encodes it as a", "orig": "backbone that receives the table image and encodes it as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "vector of predefined length. The network has been modified", "orig": "vector of predefined length. The network has been modified", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "by removing the linear and pooling layer, as we are not per-", "orig": "by removing the linear and pooling layer, as we are not per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-"}, {"label": "page_footer", "id": 14, "page_no": 3, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8565718531608582, "cells": [{"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}], "body": [{"label": "text", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 95.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9611433148384094, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "amount of such tables, and kept only those ones ranging", "orig": "amount of such tables, and kept only those ones ranging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 212.283, "r_y1": 95.98400000000004, "r_x2": 212.283, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "between 1*1 and 20*10 (rows/columns).", "orig": "between 1*1 and 20*10 (rows/columns).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns)."}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 101.22900000000004, "r": 286.365, "b": 313.019, "coord_origin": "TOPLEFT"}, "confidence": 0.988013744354248, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 109.78099999999995, "r_x1": 286.365, "r_y1": 109.78099999999995, "r_x2": 286.365, "r_y2": 101.22900000000004, "r_x3": 62.067, "r_y3": 101.22900000000004, "coord_origin": "TOPLEFT"}, "text": "The availability of the bounding boxes for all table cells", "orig": "The availability of the bounding boxes for all table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 121.73599999999999, "r_x1": 176.458, "r_y1": 121.73599999999999, "r_x2": 176.458, "r_y2": 113.18399999999997, "r_x3": 50.112, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "is essential to train our models.", "orig": "is essential to train our models.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.971, "r_y0": 121.73599999999999, "r_x1": 286.365, "r_y1": 121.73599999999999, "r_x2": 286.365, "r_y2": 113.18399999999997, "r_x3": 180.971, "r_y3": 113.18399999999997, "coord_origin": "TOPLEFT"}, "text": "In order to distinguish be-", "orig": "In order to distinguish be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 133.69100000000003, "r_x1": 286.365, "r_y1": 133.69100000000003, "r_x2": 286.365, "r_y2": 125.13900000000001, "r_x3": 50.112, "r_y3": 125.13900000000001, "coord_origin": "TOPLEFT"}, "text": "tween empty and non-empty bounding boxes, we have in-", "orig": "tween empty and non-empty bounding boxes, we have in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 145.64700000000005, "r_x1": 286.365, "r_y1": 145.64700000000005, "r_x2": 286.365, "r_y2": 137.09500000000003, "r_x3": 50.112, "r_y3": 137.09500000000003, "coord_origin": "TOPLEFT"}, "text": "troduced a binary class in the annotation. Unfortunately, the", "orig": "troduced a binary class in the annotation. Unfortunately, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 157.60199999999998, "r_x1": 286.365, "r_y1": 157.60199999999998, "r_x2": 286.365, "r_y2": 149.04999999999995, "r_x3": 50.112, "r_y3": 149.04999999999995, "coord_origin": "TOPLEFT"}, "text": "original datasets either omit the bounding boxes for whole", "orig": "original datasets either omit the bounding boxes for whole", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 169.55700000000002, "r_x1": 94.127, "r_y1": 169.55700000000002, "r_x2": 94.127, "r_y2": 161.005, "r_x3": 50.112, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "tables (e.g.", "orig": "tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.959, "r_y0": 169.55700000000002, "r_x1": 286.365, "r_y1": 169.55700000000002, "r_x2": 286.365, "r_y2": 161.005, "r_x3": 98.959, "r_y3": 161.005, "coord_origin": "TOPLEFT"}, "text": "TableBank) or they narrow their scope only to", "orig": "TableBank) or they narrow their scope only to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 181.51199999999994, "r_x1": 116.951, "r_y1": 181.51199999999994, "r_x2": 116.951, "r_y2": 172.96000000000004, "r_x3": 50.112, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "non-empty cells.", "orig": "non-empty cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 121.345, "r_y0": 181.51199999999994, "r_x1": 286.365, "r_y1": 181.51199999999994, "r_x2": 286.365, "r_y2": 172.96000000000004, "r_x3": 121.345, "r_y3": 172.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Therefore, it was imperative to introduce", "orig": "Therefore, it was imperative to introduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 193.46699999999998, "r_x1": 286.365, "r_y1": 193.46699999999998, "r_x2": 286.365, "r_y2": 184.91499999999996, "r_x3": 50.112, "r_y3": 184.91499999999996, "coord_origin": "TOPLEFT"}, "text": "a data pre-processing procedure that generates the missing", "orig": "a data pre-processing procedure that generates the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 205.423, "r_x1": 286.365, "r_y1": 205.423, "r_x2": 286.365, "r_y2": 196.87099999999998, "r_x3": 50.112, "r_y3": 196.87099999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes out of the annotation information. This pro-", "orig": "bounding boxes out of the annotation information. This pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 217.37800000000004, "r_x1": 286.365, "r_y1": 217.37800000000004, "r_x2": 286.365, "r_y2": 208.82600000000002, "r_x3": 50.112, "r_y3": 208.82600000000002, "coord_origin": "TOPLEFT"}, "text": "cedure first parses the provided table structure and calcu-", "orig": "cedure first parses the provided table structure and calcu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 229.33299999999997, "r_x1": 286.365, "r_y1": 229.33299999999997, "r_x2": 286.365, "r_y2": 220.78099999999995, "r_x3": 50.112, "r_y3": 220.78099999999995, "coord_origin": "TOPLEFT"}, "text": "lates the dimensions of the most fine-grained grid that cov-", "orig": "lates the dimensions of the most fine-grained grid that cov-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 241.288, "r_x1": 139.805, "r_y1": 241.288, "r_x2": 139.805, "r_y2": 232.736, "r_x3": 50.112, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "ers the table structure.", "orig": "ers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.607, "r_y0": 241.288, "r_x1": 286.365, "r_y1": 241.288, "r_x2": 286.365, "r_y2": 232.736, "r_x3": 144.607, "r_y3": 232.736, "coord_origin": "TOPLEFT"}, "text": "Notice that each table cell may oc-", "orig": "Notice that each table cell may oc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 253.24300000000005, "r_x1": 286.365, "r_y1": 253.24300000000005, "r_x2": 286.365, "r_y2": 244.69100000000003, "r_x3": 50.112, "r_y3": 244.69100000000003, "coord_origin": "TOPLEFT"}, "text": "cupy multiple grid squares due to row or column spans. In", "orig": "cupy multiple grid squares due to row or column spans. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 265.198, "r_x1": 286.365, "r_y1": 265.198, "r_x2": 286.365, "r_y2": 256.64599999999996, "r_x3": 50.112, "r_y3": 256.64599999999996, "coord_origin": "TOPLEFT"}, "text": "case of PubTabNet we had to compute missing bounding", "orig": "case of PubTabNet we had to compute missing bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 277.154, "r_x1": 286.365, "r_y1": 277.154, "r_x2": 286.365, "r_y2": 268.602, "r_x3": 50.112, "r_y3": 268.602, "coord_origin": "TOPLEFT"}, "text": "boxes for 48% of the simple and 69% of the complex ta-", "orig": "boxes for 48% of the simple and 69% of the complex ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 289.109, "r_x1": 68.652, "r_y1": 289.109, "r_x2": 68.652, "r_y2": 280.557, "r_x3": 50.112, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "bles.", "orig": "bles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.566, "r_y0": 289.109, "r_x1": 286.365, "r_y1": 289.109, "r_x2": 286.365, "r_y2": 280.557, "r_x3": 75.566, "r_y3": 280.557, "coord_origin": "TOPLEFT"}, "text": "Regarding FinTabNet, 68% of the simple and 98%", "orig": "Regarding FinTabNet, 68% of the simple and 98%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 301.064, "r_x1": 286.365, "r_y1": 301.064, "r_x2": 286.365, "r_y2": 292.512, "r_x3": 50.112, "r_y3": 292.512, "coord_origin": "TOPLEFT"}, "text": "of the complex tables require the generation of bounding", "orig": "of the complex tables require the generation of bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 313.019, "r_x1": 75.696, "r_y1": 313.019, "r_x2": 75.696, "r_y2": 304.467, "r_x3": 50.112, "r_y3": 304.467, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 318.264, "r": 286.365, "b": 434.413, "coord_origin": "TOPLEFT"}, "confidence": 0.9873981475830078, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 326.816, "r_x1": 286.365, "r_y1": 326.816, "r_x2": 286.365, "r_y2": 318.264, "r_x3": 62.067, "r_y3": 318.264, "coord_origin": "TOPLEFT"}, "text": "As it is illustrated in Fig. 2, the table distributions from", "orig": "As it is illustrated in Fig. 2, the table distributions from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 338.772, "r_x1": 60.075, "r_y1": 338.772, "r_x2": 60.075, "r_y2": 330.22, "r_x3": 50.112, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "all", "orig": "all", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 65.146, "r_y0": 338.772, "r_x1": 96.687, "r_y1": 338.772, "r_x2": 96.687, "r_y2": 330.22, "r_x3": 65.146, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.768, "r_y0": 338.772, "r_x1": 113.932, "r_y1": 338.772, "r_x2": 113.932, "r_y2": 330.22, "r_x3": 101.768, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.003, "r_y0": 338.772, "r_x1": 148.533, "r_y1": 338.772, "r_x2": 148.533, "r_y2": 330.22, "r_x3": 119.003, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "skewed", "orig": "skewed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.613, "r_y0": 338.772, "r_x1": 184.806, "r_y1": 338.772, "r_x2": 184.806, "r_y2": 330.22, "r_x3": 153.613, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "towards", "orig": "towards", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 189.877, "r_y0": 338.772, "r_x1": 219.765, "r_y1": 338.772, "r_x2": 219.765, "r_y2": 330.22, "r_x3": 189.877, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "simpler", "orig": "simpler", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.836, "r_y0": 338.772, "r_x1": 263.571, "r_y1": 338.772, "r_x2": 263.571, "r_y2": 330.22, "r_x3": 224.836, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "structures", "orig": "structures", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 268.652, "r_y0": 338.772, "r_x1": 286.365, "r_y1": 338.772, "r_x2": 286.365, "r_y2": 330.22, "r_x3": 268.652, "r_y3": 330.22, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 350.727, "r_x1": 286.365, "r_y1": 350.727, "r_x2": 286.365, "r_y2": 342.175, "r_x3": 50.112, "r_y3": 342.175, "coord_origin": "TOPLEFT"}, "text": "fewer number of rows/columns. Additionally, there is very", "orig": "fewer number of rows/columns. Additionally, there is very", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 362.682, "r_x1": 286.365, "r_y1": 362.682, "r_x2": 286.365, "r_y2": 354.13, "r_x3": 50.112, "r_y3": 354.13, "coord_origin": "TOPLEFT"}, "text": "limited variance in the table styles, which in case of Pub-", "orig": "limited variance in the table styles, which in case of Pub-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 374.637, "r_x1": 286.365, "r_y1": 374.637, "r_x2": 286.365, "r_y2": 366.085, "r_x3": 50.112, "r_y3": 366.085, "coord_origin": "TOPLEFT"}, "text": "TabNet and FinTabNet means one styling format for the", "orig": "TabNet and FinTabNet means one styling format for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 386.592, "r_x1": 141.589, "r_y1": 386.592, "r_x2": 141.589, "r_y2": 378.04, "r_x3": 50.112, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "majority of the tables.", "orig": "majority of the tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.702, "r_y0": 386.592, "r_x1": 286.365, "r_y1": 386.592, "r_x2": 286.365, "r_y2": 378.04, "r_x3": 148.702, "r_y3": 378.04, "coord_origin": "TOPLEFT"}, "text": "Similar limitations appear also in", "orig": "Similar limitations appear also in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 398.547, "r_x1": 286.365, "r_y1": 398.547, "r_x2": 286.365, "r_y2": 389.995, "r_x3": 50.112, "r_y3": 389.995, "coord_origin": "TOPLEFT"}, "text": "the type of table content, which in some cases (e.g. FinTab-", "orig": "the type of table content, which in some cases (e.g. FinTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 410.503, "r_x1": 201.822, "r_y1": 410.503, "r_x2": 201.822, "r_y2": 401.951, "r_x3": 50.112, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Net) is restricted to a certain domain.", "orig": "Net) is restricted to a certain domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.764, "r_y0": 410.503, "r_x1": 286.365, "r_y1": 410.503, "r_x2": 286.365, "r_y2": 401.951, "r_x3": 206.764, "r_y3": 401.951, "coord_origin": "TOPLEFT"}, "text": "Ultimately, the lack", "orig": "Ultimately, the lack", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 422.458, "r_x1": 286.365, "r_y1": 422.458, "r_x2": 286.365, "r_y2": 413.906, "r_x3": 50.112, "r_y3": 413.906, "coord_origin": "TOPLEFT"}, "text": "of diversity in the training dataset damages the ability of the", "orig": "of diversity in the training dataset damages the ability of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 434.413, "r_x1": 216.398, "r_y1": 434.413, "r_x2": 216.398, "r_y2": 425.861, "r_x3": 50.112, "r_y3": 425.861, "coord_origin": "TOPLEFT"}, "text": "models to generalize well on unseen data.", "orig": "models to generalize well on unseen data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 439.658, "r": 286.367, "b": 627.538, "coord_origin": "TOPLEFT"}, "confidence": 0.9876030087471008, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 448.21, "r_x1": 286.365, "r_y1": 448.21, "r_x2": 286.365, "r_y2": 439.658, "r_x3": 62.067, "r_y3": 439.658, "coord_origin": "TOPLEFT"}, "text": "Motivated by those observations we aimed at generating", "orig": "Motivated by those observations we aimed at generating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.165, "r_x1": 172.144, "r_y1": 460.165, "r_x2": 172.144, "r_y2": 451.613, "r_x3": 50.112, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "a synthetic table dataset named", "orig": "a synthetic table dataset named", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.148, "r_y0": 460.022, "r_x1": 224.708, "r_y1": 460.022, "r_x2": 224.708, "r_y2": 451.434, "r_x3": 174.148, "r_y3": 451.434, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.708, "r_y0": 460.165, "r_x1": 227.199, "r_y1": 460.165, "r_x2": 227.199, "r_y2": 451.613, "r_x3": 224.708, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.128, "r_y0": 460.165, "r_x1": 286.367, "r_y1": 460.165, "r_x2": 286.367, "r_y2": 451.613, "r_x3": 230.128, "r_y3": 451.613, "coord_origin": "TOPLEFT"}, "text": "This approach", "orig": "This approach", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 472.121, "r_x1": 128.179, "r_y1": 472.121, "r_x2": 128.179, "r_y2": 463.569, "r_x3": 50.112, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "offers control over:", "orig": "offers control over:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.971, "r_y0": 472.121, "r_x1": 286.365, "r_y1": 472.121, "r_x2": 286.365, "r_y2": 463.569, "r_x3": 132.971, "r_y3": 463.569, "coord_origin": "TOPLEFT"}, "text": "1) the size of the dataset, 2) the table", "orig": "1) the size of the dataset, 2) the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 484.076, "r_x1": 266.739, "r_y1": 484.076, "r_x2": 266.739, "r_y2": 475.524, "r_x3": 50.112, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "structure, 3) the table style and 4) the type of content.", "orig": "structure, 3) the table style and 4) the type of content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.873, "r_y0": 484.076, "r_x1": 286.365, "r_y1": 484.076, "r_x2": 286.365, "r_y2": 475.524, "r_x3": 270.873, "r_y3": 475.524, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 496.031, "r_x1": 286.365, "r_y1": 496.031, "r_x2": 286.365, "r_y2": 487.479, "r_x3": 50.112, "r_y3": 487.479, "coord_origin": "TOPLEFT"}, "text": "complexity of the table structure is described by the size of", "orig": "complexity of the table structure is described by the size of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.986, "r_x1": 286.365, "r_y1": 507.986, "r_x2": 286.365, "r_y2": 499.434, "r_x3": 50.112, "r_y3": 499.434, "coord_origin": "TOPLEFT"}, "text": "the table header and the table body, as well as the percentage", "orig": "the table header and the table body, as well as the percentage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.941, "r_x1": 286.365, "r_y1": 519.941, "r_x2": 286.365, "r_y2": 511.389, "r_x3": 50.112, "r_y3": 511.389, "coord_origin": "TOPLEFT"}, "text": "of the table cells covered by row spans and column spans.", "orig": "of the table cells covered by row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.8969999999999, "r_x1": 286.365, "r_y1": 531.8969999999999, "r_x2": 286.365, "r_y2": 523.345, "r_x3": 50.112, "r_y3": 523.345, "coord_origin": "TOPLEFT"}, "text": "A set of carefully designed styling templates provides the", "orig": "A set of carefully designed styling templates provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.852, "r_x1": 286.365, "r_y1": 543.852, "r_x2": 286.365, "r_y2": 535.3, "r_x3": 50.112, "r_y3": 535.3, "coord_origin": "TOPLEFT"}, "text": "basis to build a wide range of table appearances. Lastly, the", "orig": "basis to build a wide range of table appearances. Lastly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.807, "r_x1": 286.365, "r_y1": 555.807, "r_x2": 286.365, "r_y2": 547.255, "r_x3": 50.112, "r_y3": 547.255, "coord_origin": "TOPLEFT"}, "text": "table content is generated out of a curated collection of text", "orig": "table content is generated out of a curated collection of text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.762, "r_x1": 83.028, "r_y1": 567.762, "r_x2": 83.028, "r_y2": 559.21, "r_x3": 50.112, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "corpora.", "orig": "corpora.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.153, "r_y0": 567.762, "r_x1": 286.365, "r_y1": 567.762, "r_x2": 286.365, "r_y2": 559.21, "r_x3": 87.153, "r_y3": 559.21, "coord_origin": "TOPLEFT"}, "text": "By controlling the size and scope of the synthetic", "orig": "By controlling the size and scope of the synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.717, "r_x1": 286.365, "r_y1": 579.717, "r_x2": 286.365, "r_y2": 571.165, "r_x3": 50.112, "r_y3": 571.165, "coord_origin": "TOPLEFT"}, "text": "datasets we are able to train and evaluate our models in a", "orig": "datasets we are able to train and evaluate our models in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.672, "r_x1": 174.645, "r_y1": 591.672, "r_x2": 174.645, "r_y2": 583.12, "r_x3": 50.112, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "variety of different conditions.", "orig": "variety of different conditions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.021, "r_y0": 591.672, "r_x1": 286.365, "r_y1": 591.672, "r_x2": 286.365, "r_y2": 583.12, "r_x3": 181.021, "r_y3": 583.12, "coord_origin": "TOPLEFT"}, "text": "For example, we can first", "orig": "For example, we can first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.6279999999999, "r_x1": 286.365, "r_y1": 603.6279999999999, "r_x2": 286.365, "r_y2": 595.076, "r_x3": 50.112, "r_y3": 595.076, "coord_origin": "TOPLEFT"}, "text": "generate a highly diverse dataset to train our models and", "orig": "generate a highly diverse dataset to train our models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.583, "r_x1": 286.365, "r_y1": 615.583, "r_x2": 286.365, "r_y2": 607.031, "r_x3": 50.112, "r_y3": 607.031, "coord_origin": "TOPLEFT"}, "text": "then evaluate their performance on other synthetic datasets", "orig": "then evaluate their performance on other synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.538, "r_x1": 209.753, "r_y1": 627.538, "r_x2": 209.753, "r_y2": 618.986, "r_x3": 50.112, "r_y3": 618.986, "coord_origin": "TOPLEFT"}, "text": "which are focused on a specific domain.", "orig": "which are focused on a specific domain.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 632.783, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9871614575386047, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 62.067, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "In this regard, we have prepared four synthetic datasets,", "orig": "In this regard, we have prepared four synthetic datasets,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "each one containing 150k examples. The corpora to gener-", "orig": "each one containing 150k examples. The corpora to gener-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.246, "r_x1": 286.365, "r_y1": 665.246, "r_x2": 286.365, "r_y2": 656.694, "r_x3": 50.112, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "ate the table text consists of the most frequent terms appear-", "orig": "ate the table text consists of the most frequent terms appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "ing in PubTabNet and FinTabNet together with randomly", "orig": "ing in PubTabNet and FinTabNet together with randomly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 109.519, "r_y1": 689.156, "r_x2": 109.519, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "generated text.", "orig": "generated text.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.337, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 115.337, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "The first two synthetic datasets have been", "orig": "The first two synthetic datasets have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "fine-tuned to mimic the appearance of the original datasets", "orig": "fine-tuned to mimic the appearance of the original datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "but encompass more complicated table structures. The third", "orig": "but encompass more complicated table structures. The third", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. 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366.276, "r_y2": 203.79499999999996, "r_x3": 308.862, "r_y3": 203.79499999999996, "coord_origin": "TOPLEFT"}, "text": "and Fintabnet.", "orig": "and Fintabnet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.58, "r_y0": 212.34699999999998, "r_x1": 545.115, "r_y1": 212.34699999999998, "r_x2": 545.115, "r_y2": 203.79499999999996, "r_x3": 370.58, "r_y3": 203.79499999999996, "coord_origin": "TOPLEFT"}, "text": "(**) The combined dataset is the processed", "orig": "(**) The combined dataset is the processed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 224.303, "r_x1": 523.935, "r_y1": 224.303, "r_x2": 523.935, "r_y2": 215.75099999999998, "r_x3": 308.862, "r_y3": 215.75099999999998, "coord_origin": "TOPLEFT"}, "text": "combination of PubTabNet, Fintabnet and TableBank.", "orig": "combination of PubTabNet, Fintabnet and TableBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 308.862, "t": 249.889, "r": 545.115, "b": 294.306, "coord_origin": "TOPLEFT"}, "confidence": 0.978954017162323, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.44100000000003, "r_x1": 545.115, "r_y1": 258.44100000000003, "r_x2": 545.115, "r_y2": 249.889, "r_x3": 308.862, "r_y3": 249.889, "coord_origin": "TOPLEFT"}, "text": "one adopts a colorful appearance with high contrast and the", "orig": "one adopts a colorful appearance with high contrast and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.39599999999996, "r_x1": 545.115, "r_y1": 270.39599999999996, "r_x2": 545.115, "r_y2": 261.84400000000005, "r_x3": 308.862, "r_y3": 261.84400000000005, "coord_origin": "TOPLEFT"}, "text": "last one contains tables with sparse content. Lastly, we have", "orig": "last one contains tables with sparse content. Lastly, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.351, "r_x1": 545.115, "r_y1": 282.351, "r_x2": 545.115, "r_y2": 273.799, "r_x3": 308.862, "r_y3": 273.799, "coord_origin": "TOPLEFT"}, "text": "combined all synthetic datasets into one big unified syn-", "orig": "combined all synthetic datasets into one big unified syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.306, "r_x1": 436.822, "r_y1": 294.306, "r_x2": 436.822, "r_y2": 285.754, "r_x3": 308.862, "r_y3": 285.754, "coord_origin": "TOPLEFT"}, "text": "thetic dataset of 600k examples.", "orig": "thetic dataset of 600k examples.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples."}, {"label": "text", "id": 13, "page_no": 3, "cluster": {"id": 13, "label": "text", "bbox": {"l": 320.817, "t": 298.041, "r": 542.744, "b": 306.593, "coord_origin": "TOPLEFT"}, "confidence": 0.9043695330619812, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.593, "r_x1": 542.744, "r_y1": 306.593, "r_x2": 542.744, "r_y2": 298.041, "r_x3": 320.817, "r_y3": 298.041, "coord_origin": "TOPLEFT"}, "text": "Tab. 1 summarizes the various attributes of the datasets.", "orig": "Tab. 1 summarizes the various attributes of the datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tab. 1 summarizes the various attributes of the datasets."}, {"label": "section_header", "id": 11, "page_no": 3, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 308.862, "t": 321.184, "r": 444.936, "b": 331.932, "coord_origin": "TOPLEFT"}, "confidence": 0.9466595649719238, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 331.932, "r_x1": 444.936, "r_y1": 331.932, "r_x2": 444.936, "r_y2": 321.184, "r_x3": 308.862, "r_y3": 321.184, "coord_origin": "TOPLEFT"}, "text": "4. The TableFormer model", "orig": "4. The TableFormer model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. The TableFormer model"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 342.208, "r": 545.115, "b": 446.401, "coord_origin": "TOPLEFT"}, "confidence": 0.9884450435638428, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 350.76, "r_x1": 545.115, "r_y1": 350.76, "r_x2": 545.115, "r_y2": 342.208, "r_x3": 320.817, "r_y3": 342.208, "coord_origin": "TOPLEFT"}, "text": "Given the image of a table, TableFormer is able to pre-", "orig": "Given the image of a table, TableFormer is able to pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 362.715, "r_x1": 326.576, "r_y1": 362.715, "r_x2": 326.576, "r_y2": 354.163, "r_x3": 308.862, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "dict:", "orig": "dict:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.172, "r_y0": 362.715, "r_x1": 545.115, "r_y1": 362.715, "r_x2": 545.115, "r_y2": 354.163, "r_x3": 330.172, "r_y3": 354.163, "coord_origin": "TOPLEFT"}, "text": "1) a sequence of tokens that represent the structure of", "orig": "1) a sequence of tokens that represent the structure of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 374.67, "r_x1": 545.115, "r_y1": 374.67, "r_x2": 545.115, "r_y2": 366.118, "r_x3": 308.862, "r_y3": 366.118, "coord_origin": "TOPLEFT"}, "text": "a table, and 2) a bounding box coupled to a subset of those", "orig": "a table, and 2) a bounding box coupled to a subset of those", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 386.625, "r_x1": 337.265, "r_y1": 386.625, "r_x2": 337.265, "r_y2": 378.073, "r_x3": 308.862, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "tokens.", "orig": "tokens.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.808, "r_y0": 386.625, "r_x1": 545.115, "r_y1": 386.625, "r_x2": 545.115, "r_y2": 378.073, "r_x3": 341.808, "r_y3": 378.073, "coord_origin": "TOPLEFT"}, "text": "The conversion of an image into a sequence of to-", "orig": "The conversion of an image into a sequence of to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 398.58, "r_x1": 545.115, "r_y1": 398.58, "r_x2": 545.115, "r_y2": 390.028, "r_x3": 308.862, "r_y3": 390.028, "coord_origin": "TOPLEFT"}, "text": "kens is a well-known task [35, 16]. While attention is often", "orig": "kens is a well-known task [35, 16]. While attention is often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 410.535, "r_x1": 545.115, "r_y1": 410.535, "r_x2": 545.115, "r_y2": 401.983, "r_x3": 308.862, "r_y3": 401.983, "coord_origin": "TOPLEFT"}, "text": "used as an implicit method to associate each token of the", "orig": "used as an implicit method to associate each token of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 422.491, "r_x1": 545.115, "r_y1": 422.491, "r_x2": 545.115, "r_y2": 413.939, "r_x3": 308.862, "r_y3": 413.939, "coord_origin": "TOPLEFT"}, "text": "sequence with a position in the original image, an explicit", "orig": "sequence with a position in the original image, an explicit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 434.446, "r_x1": 545.115, "r_y1": 434.446, "r_x2": 545.115, "r_y2": 425.894, "r_x3": 308.862, "r_y3": 425.894, "coord_origin": "TOPLEFT"}, "text": "association between the individual table-cells and the image", "orig": "association between the individual table-cells and the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 446.401, "r_x1": 437.937, "r_y1": 446.401, "r_x2": 437.937, "r_y2": 437.849, "r_x3": 308.862, "r_y3": 437.849, "coord_origin": "TOPLEFT"}, "text": "bounding boxes is also required.", "orig": "bounding boxes is also required.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required."}, {"label": "section_header", "id": 12, "page_no": 3, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 308.862, "t": 457.694, "r": 420.161, "b": 467.546, "coord_origin": "TOPLEFT"}, "confidence": 0.937696099281311, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 467.546, "r_x1": 420.161, "r_y1": 467.546, "r_x2": 420.161, "r_y2": 457.694, "r_x3": 308.862, "r_y3": 457.694, "coord_origin": "TOPLEFT"}, "text": "4.1. Model architecture.", "orig": "4.1. Model architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1. Model architecture."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 477.034, "r": 545.116, "b": 664.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9878323078155518, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 485.586, "r_x1": 545.115, "r_y1": 485.586, "r_x2": 545.115, "r_y2": 477.034, "r_x3": 320.817, "r_y3": 477.034, "coord_origin": "TOPLEFT"}, "text": "We now describe in detail the proposed method, which", "orig": "We now describe in detail the proposed method, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.541, "r_x1": 315.507, "r_y1": 497.541, "r_x2": 315.507, "r_y2": 488.989, "r_x3": 308.862, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 319.153, "r_y0": 497.541, "r_x1": 509.021, "r_y1": 497.541, "r_x2": 509.021, "r_y2": 488.989, "r_x3": 319.153, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "composed of three main components, see Fig.", "orig": "composed of three main components, see Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 515.586, "r_y0": 497.541, "r_x1": 523.058, "r_y1": 497.541, "r_x2": 523.058, "r_y2": 488.989, "r_x3": 515.586, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 529.623, "r_y0": 497.541, "r_x1": 545.115, "r_y1": 497.541, "r_x2": 545.115, "r_y2": 488.989, "r_x3": 529.623, "r_y3": 488.989, "coord_origin": "TOPLEFT"}, "text": "Our", "orig": "Our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.353, "r_x1": 406.346, "r_y1": 509.353, "r_x2": 406.346, "r_y2": 500.765, "r_x3": 308.862, "r_y3": 500.765, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.872, "r_y0": 509.496, "r_x1": 545.111, "r_y1": 509.496, "r_x2": 545.111, "r_y2": 500.944, "r_x3": 408.872, "r_y3": 500.944, "coord_origin": "TOPLEFT"}, "text": "encodes the input as a feature vec-", "orig": "encodes the input as a feature vec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.452, "r_x1": 319.93, "r_y1": 521.452, 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"text": "to pro-", "orig": "to pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.362, "r_x1": 545.115, "r_y1": 545.362, "r_x2": 545.115, "r_y2": 536.81, "r_x3": 308.862, "r_y3": 536.81, "coord_origin": "TOPLEFT"}, "text": "duce a sequence of HTML tags that represent the structure", "orig": "duce a sequence of HTML tags that represent the structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 557.317, "r_x1": 358.545, "r_y1": 557.317, "r_x2": 358.545, "r_y2": 548.765, "r_x3": 308.862, "r_y3": 548.765, "coord_origin": "TOPLEFT"}, "text": "of the table.", "orig": "of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.191, 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state of that cell is passed to", "orig": "') the hidden state of that cell is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.2280000000001, "r_x1": 545.115, "r_y1": 581.2280000000001, "r_x2": 545.115, "r_y2": 572.6759999999999, "r_x3": 308.862, "r_y3": 572.6759999999999, "coord_origin": "TOPLEFT"}, "text": "the Cell BBox Decoder. As for spanning cells, such as row", "orig": "the Cell BBox Decoder. As for spanning cells, such as row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.183, "r_x1": 483.118, "r_y1": 593.183, "r_x2": 483.118, "r_y2": 584.631, "r_x3": 308.862, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "or column span, the tag is broken down to '", "orig": "or column span, the tag is broken down to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.119, "r_y0": 593.019, "r_x1": 490.868, "r_y1": 593.019, "r_x2": 490.868, "r_y2": 584.313, "r_x3": 483.119, "r_y3": 584.313, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.868, "r_y0": 593.183, "r_x1": 545.114, "r_y1": 593.183, "r_x2": 545.114, "r_y2": 584.631, "r_x3": 490.868, "r_y3": 584.631, "coord_origin": "TOPLEFT"}, "text": "', 'rowspan='", "orig": "', 'rowspan='", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.138, "r_x1": 545.115, "r_y1": 605.138, "r_x2": 545.115, "r_y2": 596.586, "r_x3": 308.862, "r_y3": 596.586, "coord_origin": "TOPLEFT"}, "text": "or 'colspan=', with the number of spanning cells (attribute),", "orig": "or 'colspan=', with the number of spanning cells (attribute),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.093, "r_x1": 329.644, "r_y1": 617.093, "r_x2": 329.644, "r_y2": 608.5409999999999, "r_x3": 308.862, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "and '", "orig": "and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.646, "r_y0": 616.929, "r_x1": 337.395, "r_y1": 616.929, "r_x2": 337.395, "r_y2": 608.223, "r_x3": 329.646, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.394, "r_y0": 617.093, "r_x1": 343.202, "r_y1": 617.093, "r_x2": 343.202, "r_y2": 608.5409999999999, "r_x3": 337.394, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "'.", "orig": "'.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.064, "r_y0": 617.093, "r_x1": 468.591, "r_y1": 617.093, "r_x2": 468.591, "r_y2": 608.5409999999999, "r_x3": 348.064, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "The hidden state attached to '", "orig": "The hidden state attached to '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 468.595, "r_y0": 616.929, "r_x1": 476.344, "r_y1": 616.929, "r_x2": 476.344, "r_y2": 608.223, "r_x3": 468.595, "r_y3": 608.223, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.344, "r_y0": 617.093, "r_x1": 545.116, "r_y1": 617.093, "r_x2": 545.116, "r_y2": 608.5409999999999, "r_x3": 476.344, "r_y3": 608.5409999999999, "coord_origin": "TOPLEFT"}, "text": "' is passed to the", "orig": "' is passed to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.048, "r_x1": 545.115, "r_y1": 629.048, "r_x2": 545.115, "r_y2": 620.496, "r_x3": 308.862, "r_y3": 620.496, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder. A shared feed forward network (FFN)", "orig": "Cell BBox Decoder. A shared feed forward network (FFN)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.0029999999999, "r_x1": 533.2, "r_y1": 641.0029999999999, "r_x2": 533.2, "r_y2": 632.451, "r_x3": 308.862, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "receives the hidden states from the Structure Decoder,", "orig": "receives the hidden states from the Structure Decoder,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.364, "r_y0": 641.0029999999999, "r_x1": 545.115, "r_y1": 641.0029999999999, "r_x2": 545.115, "r_y2": 632.451, "r_x3": 537.364, "r_y3": 632.451, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.9590000000001, "r_x1": 545.115, "r_y1": 652.9590000000001, "r_x2": 545.115, "r_y2": 644.407, "r_x3": 308.862, "r_y3": 644.407, "coord_origin": "TOPLEFT"}, "text": "provide the final detection predictions of the bounding box", "orig": "provide the final detection predictions of the bounding box", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.914, "r_x1": 449.424, "r_y1": 664.914, "r_x2": 449.424, "r_y2": 656.362, "r_x3": 308.862, "r_y3": 656.362, "coord_origin": "TOPLEFT"}, "text": "coordinates and their classification.", "orig": "coordinates and their classification.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification."}, {"label": "text", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 668.261, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.978580117225647, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 677.217, "r_x1": 431.91, "r_y1": 677.217, "r_x2": 431.91, "r_y2": 668.261, "r_x3": 320.817, "r_y3": 668.261, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network.", "orig": "CNN Backbone Network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.499, "r_y0": 677.201, "r_x1": 545.113, "r_y1": 677.201, "r_x2": 545.113, "r_y2": 668.649, "r_x3": 439.499, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "A ResNet-18 CNN is the", "orig": "A ResNet-18 CNN is the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "backbone that receives the table image and encodes it as a", "orig": "backbone that receives the table image and encodes it as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "vector of predefined length. The network has been modified", "orig": "vector of predefined length. The network has been modified", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "by removing the linear and pooling layer, as we are not per-", "orig": "by removing the linear and pooling layer, as we are not per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. 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As our model utilizes a transformer architecture, the", "orig": "tions). 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Afterwards, a transformer en-", "orig": "represent the table structure. 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Simultaneously, the Cell BBox Decoder", "orig": "predict the tags. Simultaneously, the Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 656.275, "r_x1": 220.582, "r_y1": 656.275, "r_x2": 220.582, "r_y2": 647.723, "r_x3": 50.112, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "selects features referring to the data cells ('", "orig": "selects features referring to the data cells ('", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.578, "r_y0": 656.111, "r_x1": 228.327, "r_y1": 656.111, "r_x2": 228.327, "r_y2": 647.405, "r_x3": 220.578, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.327, "r_y0": 656.275, "r_x1": 236.078, "r_y1": 656.275, "r_x2": 236.078, "r_y2": 647.723, "r_x3": 228.327, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "td", "orig": "td", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 236.078, "r_y0": 656.111, "r_x1": 243.827, "r_y1": 656.111, "r_x2": 243.827, "r_y2": 647.405, "r_x3": 236.078, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 243.826, "r_y0": 656.275, "r_x1": 255.293, "r_y1": 656.275, "r_x2": 255.293, "r_y2": 647.723, "r_x3": 243.826, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "', '", "orig": "', '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.291, "r_y0": 656.111, "r_x1": 263.04, "r_y1": 656.111, "r_x2": 263.04, "r_y2": 647.405, "r_x3": 255.291, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.04, "r_y0": 656.275, "r_x1": 286.362, "r_y1": 656.275, "r_x2": 286.362, "r_y2": 647.723, "r_x3": 263.04, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "') and", "orig": "') and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 668.23, "r_x1": 286.365, "r_y1": 668.23, "r_x2": 286.365, "r_y2": 659.678, "r_x3": 50.112, "r_y3": 659.678, "coord_origin": "TOPLEFT"}, "text": "passes them through an attention network, an MLP, and a", "orig": "passes them through an attention network, an MLP, and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 680.185, "r_x1": 218.47, "r_y1": 680.185, "r_x2": 218.47, "r_y2": 671.633, "r_x3": 50.112, "r_y3": 671.633, "coord_origin": "TOPLEFT"}, "text": "linear layer to predict the bounding boxes.", "orig": "linear layer to predict the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.803, "r": 545.115, "b": 294.221, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.355, "r_x1": 545.115, "r_y1": 258.355, "r_x2": 545.115, "r_y2": 249.803, "r_x3": 308.862, "r_y3": 249.803, "coord_origin": "TOPLEFT"}, "text": "forming classification, and adding an adaptive pooling layer", "orig": "forming classification, and adding an adaptive pooling layer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.31100000000004, "r_x1": 366.466, "r_y1": 270.31100000000004, "r_x2": 366.466, "r_y2": 261.759, "r_x3": 308.862, "r_y3": 261.759, "coord_origin": "TOPLEFT"}, "text": "of size 28*28.", "orig": "of size 28*28.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 371.706, "r_y0": 270.31100000000004, "r_x1": 545.115, "r_y1": 270.31100000000004, "r_x2": 545.115, "r_y2": 261.759, "r_x3": 371.706, "r_y3": 261.759, "coord_origin": "TOPLEFT"}, "text": "ResNet by default downsamples the image", "orig": "ResNet by default downsamples the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.266, "r_x1": 545.115, "r_y1": 282.266, "r_x2": 545.115, "r_y2": 273.71400000000006, "r_x3": 308.862, "r_y3": 273.71400000000006, "coord_origin": "TOPLEFT"}, "text": "resolution by 32 and then the encoded image is provided to", "orig": "resolution by 32 and then the encoded image is provided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.221, "r_x1": 341.24, "r_y1": 294.221, "r_x2": 341.24, "r_y2": 285.669, "r_x3": 308.862, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": "both the", "orig": "both the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 343.731, "r_y0": 294.078, "r_x1": 417.235, "r_y1": 294.078, "r_x2": 417.235, "r_y2": 285.49, "r_x3": 343.731, "r_y3": 285.49, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 417.234, "r_y0": 294.221, "r_x1": 436.601, "r_y1": 294.221, "r_x2": 436.601, "r_y2": 285.669, "r_x3": 417.234, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.092, "r_y0": 294.078, "r_x1": 516.561, "r_y1": 294.078, "r_x2": 516.561, "r_y2": 285.49, "r_x3": 439.092, "r_y3": 285.49, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.561, "r_y0": 294.221, "r_x1": 519.052, "r_y1": 294.221, "r_x2": 519.052, "r_y2": 285.669, "r_x3": 516.561, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder ."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 297.34, "r": 545.115, "b": 413.876, "coord_origin": "TOPLEFT"}, "confidence": 0.9862009882926941, "cells": [{"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 306.296, "r_x1": 400.866, "r_y1": 306.296, "r_x2": 400.866, "r_y2": 297.34, "r_x3": 320.817, "r_y3": 297.34, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder.", "orig": "Structure Decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.914, "r_y0": 306.28, "r_x1": 545.114, "r_y1": 306.28, "r_x2": 545.114, "r_y2": 297.728, "r_x3": 403.914, "r_y3": 297.728, "coord_origin": "TOPLEFT"}, "text": "The transformer 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the simplicity of table images.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 417.383, "r": 545.115, "b": 545.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 425.935, "r_x1": 336.309, "r_y1": 425.935, "r_x2": 336.309, "r_y2": 417.383, "r_x3": 320.817, "r_y3": 417.383, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.579, "r_y0": 425.935, "r_x1": 388.055, "r_y1": 425.935, "r_x2": 388.055, "r_y2": 417.383, "r_x3": 341.579, "r_y3": 417.383, "coord_origin": "TOPLEFT"}, 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"de-", "orig": "de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.756, "r_x1": 545.115, "r_y1": 473.756, "r_x2": 545.115, "r_y2": 465.204, "r_x3": 308.862, "r_y3": 465.204, "coord_origin": "TOPLEFT"}, "text": "coder receives as input the output feature produced by the", "orig": "coder receives as input the output feature produced by the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.711, "r_x1": 545.115, "r_y1": 485.711, "r_x2": 545.115, "r_y2": 477.159, "r_x3": 308.862, "r_y3": 477.159, "coord_origin": "TOPLEFT"}, "text": "transformer encoder, and the tokenized input of the HTML", "orig": "transformer encoder, and the tokenized input of the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, 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Using a stack of multi-head attention lay-", "orig": "ground-truth tags. Using a stack of multi-head attention lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 509.621, "r_x1": 545.115, "r_y1": 509.621, "r_x2": 545.115, "r_y2": 501.069, "r_x3": 308.862, "r_y3": 501.069, "coord_origin": "TOPLEFT"}, "text": "ers, different aspects of the tag sequence could be inferred.", "orig": "ers, different aspects of the tag sequence could be inferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.576, "r_x1": 545.115, "r_y1": 521.576, "r_x2": 545.115, "r_y2": 513.024, "r_x3": 308.862, "r_y3": 513.024, "coord_origin": "TOPLEFT"}, "text": "This is achieved by each attention head on a layer operating", "orig": "This is achieved by each attention head on a layer operating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.5319999999999, "r_x1": 545.115, "r_y1": 533.5319999999999, "r_x2": 545.115, "r_y2": 524.98, "r_x3": 308.862, "r_y3": 524.98, "coord_origin": "TOPLEFT"}, "text": "in a different subspace, and then combining altogether their", "orig": "in a different subspace, and then combining altogether their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.487, "r_x1": 369.733, "r_y1": 545.487, "r_x2": 369.733, "r_y2": 536.935, "r_x3": 308.862, "r_y3": 536.935, "coord_origin": "TOPLEFT"}, "text": "attention score.", "orig": "attention score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. 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As our model utilizes a transformer architecture, the", "orig": "tions). 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Simultaneously, the Cell BBox Decoder", "orig": "predict the tags. Simultaneously, the Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 656.275, "r_x1": 220.582, "r_y1": 656.275, "r_x2": 220.582, "r_y2": 647.723, "r_x3": 50.112, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "selects features referring to the data cells ('", "orig": "selects features referring to the data cells ('", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.578, "r_y0": 656.111, "r_x1": 228.327, "r_y1": 656.111, "r_x2": 228.327, "r_y2": 647.405, "r_x3": 220.578, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.327, "r_y0": 656.275, "r_x1": 236.078, "r_y1": 656.275, "r_x2": 236.078, "r_y2": 647.723, "r_x3": 228.327, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "td", "orig": "td", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 236.078, "r_y0": 656.111, "r_x1": 243.827, "r_y1": 656.111, "r_x2": 243.827, "r_y2": 647.405, "r_x3": 236.078, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 243.826, "r_y0": 656.275, "r_x1": 255.293, "r_y1": 656.275, "r_x2": 255.293, "r_y2": 647.723, "r_x3": 243.826, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "', '", "orig": "', '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.291, "r_y0": 656.111, "r_x1": 263.04, "r_y1": 656.111, "r_x2": 263.04, "r_y2": 647.405, "r_x3": 255.291, "r_y3": 647.405, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.04, "r_y0": 656.275, "r_x1": 286.362, "r_y1": 656.275, "r_x2": 286.362, "r_y2": 647.723, "r_x3": 263.04, "r_y3": 647.723, "coord_origin": "TOPLEFT"}, "text": "') and", "orig": "') and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 668.23, "r_x1": 286.365, "r_y1": 668.23, "r_x2": 286.365, "r_y2": 659.678, "r_x3": 50.112, "r_y3": 659.678, "coord_origin": "TOPLEFT"}, "text": "passes them through an attention network, an MLP, and a", "orig": "passes them through an attention network, an MLP, and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 680.185, "r_x1": 218.47, "r_y1": 680.185, "r_x2": 218.47, "r_y2": 671.633, "r_x3": 50.112, "r_y3": 671.633, "coord_origin": "TOPLEFT"}, "text": "linear layer to predict the bounding boxes.", "orig": "linear layer to predict the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 249.803, "r": 545.115, "b": 294.221, "coord_origin": "TOPLEFT"}, "confidence": 0.9831458330154419, "cells": [{"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 258.355, "r_x1": 545.115, "r_y1": 258.355, "r_x2": 545.115, "r_y2": 249.803, "r_x3": 308.862, "r_y3": 249.803, "coord_origin": "TOPLEFT"}, "text": "forming classification, and adding an adaptive pooling layer", "orig": "forming classification, and adding an adaptive pooling layer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 270.31100000000004, "r_x1": 366.466, "r_y1": 270.31100000000004, "r_x2": 366.466, "r_y2": 261.759, "r_x3": 308.862, "r_y3": 261.759, "coord_origin": "TOPLEFT"}, "text": "of size 28*28.", "orig": "of size 28*28.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 371.706, "r_y0": 270.31100000000004, "r_x1": 545.115, "r_y1": 270.31100000000004, "r_x2": 545.115, "r_y2": 261.759, "r_x3": 371.706, "r_y3": 261.759, "coord_origin": "TOPLEFT"}, "text": "ResNet by default downsamples the image", "orig": "ResNet by default downsamples the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 282.266, "r_x1": 545.115, "r_y1": 282.266, "r_x2": 545.115, "r_y2": 273.71400000000006, "r_x3": 308.862, "r_y3": 273.71400000000006, "coord_origin": "TOPLEFT"}, "text": "resolution by 32 and then the encoded image is provided to", "orig": "resolution by 32 and then the encoded image is provided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 294.221, "r_x1": 341.24, "r_y1": 294.221, "r_x2": 341.24, "r_y2": 285.669, "r_x3": 308.862, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": "both the", "orig": "both the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 343.731, "r_y0": 294.078, "r_x1": 417.235, "r_y1": 294.078, "r_x2": 417.235, "r_y2": 285.49, "r_x3": 343.731, "r_y3": 285.49, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 417.234, "r_y0": 294.221, "r_x1": 436.601, "r_y1": 294.221, "r_x2": 436.601, "r_y2": 285.669, "r_x3": 417.234, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.092, "r_y0": 294.078, "r_x1": 516.561, "r_y1": 294.078, "r_x2": 516.561, "r_y2": 285.49, "r_x3": 439.092, "r_y3": 285.49, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.561, "r_y0": 294.221, "r_x1": 519.052, "r_y1": 294.221, "r_x2": 519.052, "r_y2": 285.669, "r_x3": 516.561, "r_y3": 285.669, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "forming classification, and adding an adaptive pooling layer of size 28*28. 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The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. 'Scene Understanding', 'Image Captioning'), something which we relate to the simplicity of table images."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 417.383, "r": 545.115, "b": 545.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9851906895637512, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 425.935, "r_x1": 336.309, "r_y1": 425.935, "r_x2": 336.309, "r_y2": 417.383, "r_x3": 320.817, "r_y3": 417.383, "coord_origin": "TOPLEFT"}, "text": "The", "orig": "The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.579, "r_y0": 425.935, "r_x1": 388.055, "r_y1": 425.935, "r_x2": 388.055, "r_y2": 417.383, "r_x3": 341.579, "r_y3": 417.383, "coord_origin": "TOPLEFT"}, 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During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 548.605, "r": 545.115, "b": 653.187, "coord_origin": "TOPLEFT"}, "confidence": 0.9869195818901062, "cells": [{"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 557.561, "r_x1": 404.762, "r_y1": 557.561, "r_x2": 404.762, "r_y2": 548.605, "r_x3": 320.817, "r_y3": 548.605, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder.", "orig": "Cell BBox Decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.341, "r_y0": 557.545, "r_x1": 545.115, "r_y1": 557.545, "r_x2": 545.115, "r_y2": 548.9929999999999, "r_x3": 410.341, "r_y3": 548.9929999999999, "coord_origin": "TOPLEFT"}, "text": "Our architecture allows to simul-", "orig": "Our architecture allows to simul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 569.501, "r_x1": 545.115, "r_y1": 569.501, "r_x2": 545.115, "r_y2": 560.9490000000001, "r_x3": 308.862, "r_y3": 560.9490000000001, "coord_origin": "TOPLEFT"}, "text": "taneously predict HTML tags and bounding boxes for each", "orig": "taneously predict HTML tags and bounding boxes for each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 581.456, "r_x1": 545.115, "r_y1": 581.456, "r_x2": 545.115, "r_y2": 572.904, "r_x3": 308.862, "r_y3": 572.904, "coord_origin": "TOPLEFT"}, "text": "table cell without the need of a separate object detector end", "orig": "table cell without the need of a separate object detector end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 593.4110000000001, "r_x1": 336.458, "r_y1": 593.4110000000001, "r_x2": 336.458, "r_y2": 584.859, "r_x3": 308.862, "r_y3": 584.859, "coord_origin": "TOPLEFT"}, "text": "to end.", "orig": "to end.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.971, "r_y0": 593.4110000000001, "r_x1": 545.115, "r_y1": 593.4110000000001, "r_x2": 545.115, "r_y2": 584.859, "r_x3": 340.971, "r_y3": 584.859, "coord_origin": "TOPLEFT"}, "text": "This approach is inspired by DETR [1] which em-", "orig": "This approach is inspired by DETR [1] which em-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.366, "r_x1": 545.115, "r_y1": 605.366, "r_x2": 545.115, "r_y2": 596.814, "r_x3": 308.862, "r_y3": 596.814, "coord_origin": "TOPLEFT"}, "text": "ploys a Transformer Encoder, and Decoder that looks for", "orig": "ploys a Transformer Encoder, and Decoder that looks for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 617.321, "r_x1": 545.115, "r_y1": 617.321, "r_x2": 545.115, "r_y2": 608.769, "r_x3": 308.862, "r_y3": 608.769, "coord_origin": "TOPLEFT"}, "text": "a specific number of object queries (potential object detec-", "orig": "a specific number of object queries (potential object detec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.2760000000001, "r_x1": 545.115, "r_y1": 629.2760000000001, "r_x2": 545.115, "r_y2": 620.7239999999999, "r_x3": 308.862, "r_y3": 620.7239999999999, "coord_origin": "TOPLEFT"}, "text": "tions). As our model utilizes a transformer architecture, the", "orig": "tions). As our model utilizes a transformer architecture, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.232, "r_x1": 381.679, "r_y1": 641.232, "r_x2": 381.679, "r_y2": 632.6800000000001, "r_x3": 308.862, "r_y3": 632.6800000000001, "coord_origin": "TOPLEFT"}, "text": "hidden state of the", "orig": "hidden state of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.997, "r_y0": 641.068, "r_x1": 391.746, "r_y1": 641.068, "r_x2": 391.746, "r_y2": 632.362, "r_x3": 383.997, "r_y3": 632.362, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.746, "r_y0": 641.232, "r_x1": 399.497, "r_y1": 641.232, "r_x2": 399.497, "r_y2": 632.6800000000001, "r_x3": 391.746, "r_y3": 632.6800000000001, "coord_origin": "TOPLEFT"}, "text": "td", "orig": "td", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 399.497, "r_y0": 641.068, "r_x1": 407.246, "r_y1": 641.068, "r_x2": 407.246, "r_y2": 632.362, "r_x3": 399.497, "r_y3": 632.362, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.246, "r_y0": 641.232, "r_x1": 432.91, "r_y1": 641.232, "r_x2": 432.91, "r_y2": 632.6800000000001, "r_x3": 407.246, "r_y3": 632.6800000000001, "coord_origin": "TOPLEFT"}, "text": "' and '", "orig": "' and '", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 432.908, "r_y0": 641.068, "r_x1": 440.657, "r_y1": 641.068, "r_x2": 440.657, "r_y2": 632.362, "r_x3": 432.908, "r_y3": 632.362, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.657, "r_y0": 641.232, "r_x1": 545.115, "r_y1": 641.232, "r_x2": 545.115, "r_y2": 632.6800000000001, "r_x3": 440.657, "r_y3": 632.6800000000001, "coord_origin": "TOPLEFT"}, "text": "' HTML structure tags be-", "orig": "' HTML structure tags be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.187, "r_x1": 398.964, "r_y1": 653.187, "r_x2": 398.964, "r_y2": 644.635, "r_x3": 308.862, "r_y3": 644.635, "coord_origin": "TOPLEFT"}, "text": "come the object query.", "orig": "come the object query.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 656.515, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9852352142333984, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 665.246, "r_x1": 444.343, "r_y1": 665.246, "r_x2": 444.343, "r_y2": 656.694, "r_x3": 320.817, "r_y3": 656.694, "coord_origin": "TOPLEFT"}, "text": "The encoding generated by the", "orig": "The encoding generated by the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.006, "r_y0": 665.103, "r_x1": 545.108, "r_y1": 665.103, "r_x2": 545.108, "r_y2": 656.515, "r_x3": 447.006, "r_y3": 656.515, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network", "orig": "CNN Backbone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 677.201, "r_x1": 545.115, "r_y1": 677.201, "r_x2": 545.115, "r_y2": 668.649, "r_x3": 308.862, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "along with the features acquired for every data cell from the", "orig": "along with the features acquired for every data cell from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 308.862, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Transformer Decoder are then passed to the attention net-", "orig": "Transformer Decoder are then passed to the attention net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "work. The attention network takes both inputs and learns to", "orig": "work. The attention network takes both inputs and learns to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 470.425, "r_y1": 713.066, "r_x2": 470.425, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "provide an attention weighted encoding.", "orig": "provide an attention weighted encoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.63, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 474.63, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "This weighted at-", "orig": "This weighted at-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at-"}], "headers": [{"label": "page_footer", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8719564080238342, "cells": [{"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 286.365, "r_y1": 95.98400000000004, "r_x2": 286.365, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "produce a feature for each table cell. Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": 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As for the", "orig": "structure tokens. 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In", "orig": ") to be scale invariant as explained in [25]. 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173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.711, "r_y0": 405.174, "r_x1": 341.462, "r_y1": 405.174, "r_x2": 341.462, "r_y2": 396.622, "r_x3": 333.711, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.447, "r_y0": 405.174, "r_x1": 360.391, "r_y1": 405.174, "r_x2": 360.391, "r_y2": 396.622, "r_x3": 345.447, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "0.5.", "orig": "0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.963, "r_y0": 405.174, "r_x1": 415.166, "r_y1": 405.174, "r_x2": 415.166, "r_y2": 396.622, "r_x3": 367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 429.084, "r_x1": 360.966, "r_y1": 429.084, "r_x2": 360.966, "r_y2": 420.532, "r_x3": 308.862, "r_y3": 420.532, "coord_origin": "TOPLEFT"}, "text": "convergence.", "orig": "convergence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, "r_y3": 461.553, "coord_origin": "TOPLEFT"}, "text": "undergoes a single forward pass through the", "orig": "undergoes a single forward pass through the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.078, "r_y0": 469.962, "r_x1": 545.111, "r_y1": 469.962, "r_x2": 545.111, "r_y2": 461.374, "r_x3": 498.078, "r_y3": 461.374, "coord_origin": "TOPLEFT"}, "text": "CNN Back-", "orig": "CNN Back-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 481.917, "r_x1": 364.443, "r_y1": 481.917, "r_x2": 364.443, "r_y2": 473.329, "r_x3": 308.862, "r_y3": 473.329, "coord_origin": "TOPLEFT"}, "text": "bone Network", "orig": "bone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.061, "r_y0": 482.06, "r_x1": 545.113, "r_y1": 482.06, "r_x2": 545.113, "r_y2": 473.508, "r_x3": 367.061, "r_y3": 473.508, "coord_origin": "TOPLEFT"}, "text": "and transformer encoder. This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 624.225, "r_x1": 545.115, "r_y1": 624.225, "r_x2": 545.115, "r_y2": 615.673, "r_x3": 308.862, "r_y3": 615.673, "coord_origin": "TOPLEFT"}, "text": "able datasets of different nature to prove the generalization", "orig": "able datasets of different nature to prove the generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 636.1800000000001, "r_x1": 545.115, "r_y1": 636.1800000000001, "r_x2": 545.115, "r_y2": 627.6279999999999, "r_x3": 308.862, "r_y3": 627.6279999999999, "coord_origin": "TOPLEFT"}, "text": "and effectiveness of our model. The datasets used for eval-", "orig": "and effectiveness of our model. The datasets used for eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 648.135, "r_x1": 545.115, "r_y1": 648.135, "r_x2": 545.115, "r_y2": 639.583, "r_x3": 308.862, "r_y3": 639.583, "coord_origin": "TOPLEFT"}, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "orig": "uation are the PubTabNet, FinTabNet and TableBank which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 660.091, "r_x1": 545.115, "r_y1": 660.091, "r_x2": 545.115, "r_y2": 651.539, "r_x3": 308.862, "r_y3": 651.539, "coord_origin": "TOPLEFT"}, "text": "stem from the scientific, financial and general domains re-", "orig": "stem from the scientific, financial and general domains re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 672.046, "r_x1": 350.705, "r_y1": 672.046, "r_x2": 350.705, "r_y2": 663.494, "r_x3": 308.862, "r_y3": 663.494, "coord_origin": "TOPLEFT"}, "text": "spectively.", "orig": "spectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 320.817, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "We also share our baseline results on the challenging", "orig": "We also share our baseline results on the challenging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 361.196, "r_y1": 701.111, "r_x2": 361.196, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.057, "r_y0": 701.111, "r_x1": 396.214, "r_y1": 701.111, "r_x2": 396.214, "r_y2": 692.559, "r_x3": 366.057, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.406, "r_y0": 701.111, "r_x1": 453.449, "r_y1": 701.111, "r_x2": 453.449, "r_y2": 692.559, "r_x3": 406.406, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Throughout", "orig": "Throughout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.311, "r_y0": 701.111, "r_x1": 471.591, "r_y1": 701.111, "r_x2": 471.591, 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713.067, "r_x2": 495.94, "r_y2": 704.515, "r_x3": 308.862, "r_y3": 704.515, "coord_origin": "TOPLEFT"}, "text": "same parameters stated in Sec. 5.1 are utilized.", "orig": "same parameters stated in Sec. 5.1 are utilized.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 155.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 286.365, "r_y1": 95.98400000000004, "r_x2": 286.365, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "produce a feature for each table cell. Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.84900000000005, "r_x1": 135.691, "r_y1": 131.84900000000005, "r_x2": 135.691, "r_y2": 123.29700000000003, "r_x3": 50.112, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "of objects may exist.", "orig": "of objects may exist.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.047, "r_y0": 131.84900000000005, "r_x1": 286.365, "r_y1": 131.84900000000005, "r_x2": 286.365, "r_y2": 123.29700000000003, "r_x3": 142.047, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "In our case, we know up front that", "orig": "In our case, we know up front that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.80499999999995, "r_x1": 286.365, "r_y1": 143.80499999999995, "r_x2": 286.365, "r_y2": 135.25300000000004, "r_x3": 50.112, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "the produced detections always match with the table cells", "orig": "the produced detections always match with the table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.76, "r_x1": 175.163, "r_y1": 155.76, "r_x2": 175.163, "r_y2": 147.20799999999997, "r_x3": 50.112, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "in number and correspondence.", "orig": "in number and correspondence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.89300000000003, "r": 286.365, "b": 240.17700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": 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The FFN consists", "orig": "network (FFN). The FFN consists", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.356, "r_x1": 286.365, "r_y1": 192.356, "r_x2": 286.365, "r_y2": 183.80399999999997, "r_x3": 50.112, "r_y3": 183.80399999999997, "coord_origin": "TOPLEFT"}, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "orig": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.31100000000004, "r_x1": 286.365, "r_y1": 204.31100000000004, "r_x2": 286.365, "r_y2": 195.75900000000001, "r_x3": 50.112, "r_y3": 195.75900000000001, "coord_origin": "TOPLEFT"}, "text": "tion function) that predicts the normalized coordinates for", "orig": "tion function) that predicts the normalized coordinates for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 216.26599999999996, "r_x1": 196.004, "r_y1": 216.26599999999996, "r_x2": 196.004, "r_y2": 207.71400000000006, "r_x3": 50.112, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table cell.", "orig": "the bounding box of each table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.786, "r_y0": 216.26599999999996, "r_x1": 286.365, "r_y1": 216.26599999999996, "r_x2": 286.365, "r_y2": 207.71400000000006, "r_x3": 200.786, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "Finally, the predicted", "orig": "Finally, the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 228.221, "r_x1": 286.365, "r_y1": 228.221, "r_x2": 286.365, "r_y2": 219.66899999999998, "r_x3": 50.112, "r_y3": 219.66899999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes are classified based on whether they are", "orig": "bounding boxes are classified based on whether they are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 240.17700000000002, "r_x1": 181.549, "r_y1": 240.17700000000002, "r_x2": 181.549, "r_y2": 231.625, "r_x3": 50.112, "r_y3": 231.625, "coord_origin": "TOPLEFT"}, "text": "empty or not using a linear layer.", "orig": "empty or not using a linear layer.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92200000000003, "r": 286.366, "b": 444.145, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.451, "r_y0": 252.86199999999997, "r_x1": 276.139, "r_y1": 252.86199999999997, "r_x2": 276.139, "r_y2": 244.30999999999995, "r_x3": 134.451, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "We formulate a multi-task loss Eq.", "orig": "We formulate a multi-task loss Eq.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.379, "r_y0": 252.86199999999997, "r_x1": 286.361, "r_y1": 252.86199999999997, "r_x2": 286.361, "r_y2": 244.30999999999995, "r_x3": 281.379, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.817, "r_x1": 134.685, "r_y1": 264.817, "r_x2": 134.685, "r_y2": 256.265, "r_x3": 50.112, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "to train our network.", "orig": "to train our network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.543, "r_y0": 264.817, "r_x1": 286.365, "r_y1": 264.817, "r_x2": 286.365, "r_y2": 256.265, "r_x3": 140.543, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "The Cross-Entropy loss (denoted as", "orig": "The Cross-Entropy loss (denoted as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": 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280.176, "coord_origin": "TOPLEFT"}, "text": "structure tokens. As for the", "orig": "structure tokens. As for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 161.318, "r_y0": 288.585, "r_x1": 238.797, "r_y1": 288.585, "r_x2": 238.797, "r_y2": 279.99699999999996, "r_x3": 161.318, "r_y3": 279.99699999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.521, "r_y0": 288.728, "r_x1": 286.363, "r_y1": 288.728, "r_x2": 286.363, "r_y2": 280.176, "r_x3": 241.521, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "it is trained", "orig": "it is trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.683, "r_x1": 211.377, "r_y1": 300.683, "r_x2": 211.377, "r_y2": 292.131, 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In", "orig": ") to be scale invariant as explained in [25]. 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415.166, "r_y2": 396.622, "r_x3": 367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": 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"confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, 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This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 308.862, "t": 579.554, "r": 397.443, "b": 589.406, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. 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The datasets used for eval-", "orig": "and effectiveness of our model. 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false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 155.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 286.365, "r_y1": 95.98400000000004, "r_x2": 286.365, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "produce a feature for each table cell. Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.84900000000005, "r_x1": 135.691, "r_y1": 131.84900000000005, "r_x2": 135.691, "r_y2": 123.29700000000003, "r_x3": 50.112, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "of objects may exist.", "orig": "of objects may exist.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.047, "r_y0": 131.84900000000005, "r_x1": 286.365, "r_y1": 131.84900000000005, "r_x2": 286.365, "r_y2": 123.29700000000003, "r_x3": 142.047, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "In our case, we know up front that", "orig": "In our case, we know up front that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.80499999999995, "r_x1": 286.365, "r_y1": 143.80499999999995, "r_x2": 286.365, "r_y2": 135.25300000000004, "r_x3": 50.112, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "the produced detections always match with the table cells", "orig": "the produced detections always match with the table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.76, "r_x1": 175.163, "r_y1": 155.76, "r_x2": 175.163, "r_y2": 147.20799999999997, "r_x3": 50.112, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "in number and correspondence.", "orig": "in number and correspondence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.89300000000003, "r": 286.365, "b": 240.17700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 168.44500000000005, "r_x1": 108.154, "r_y1": 168.44500000000005, "r_x2": 108.154, "r_y2": 159.89300000000003, "r_x3": 62.067, "r_y3": 159.89300000000003, "coord_origin": "TOPLEFT"}, "text": "The output", "orig": "The output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 113.275, "r_y0": 168.44500000000005, "r_x1": 144.806, "r_y1": 168.44500000000005, "r_x2": 144.806, "r_y2": 159.89300000000003, "r_x3": 113.275, "r_y3": 159.89300000000003, 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"g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.153, "r_y0": 180.40099999999995, "r_x1": 286.365, "r_y1": 180.40099999999995, "r_x2": 286.365, "r_y2": 171.84900000000005, "r_x3": 143.153, "r_y3": 171.84900000000005, "coord_origin": "TOPLEFT"}, "text": "network (FFN). The FFN consists", "orig": "network (FFN). The FFN consists", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.356, "r_x1": 286.365, "r_y1": 192.356, "r_x2": 286.365, "r_y2": 183.80399999999997, "r_x3": 50.112, "r_y3": 183.80399999999997, "coord_origin": "TOPLEFT"}, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "orig": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.31100000000004, "r_x1": 286.365, "r_y1": 204.31100000000004, "r_x2": 286.365, "r_y2": 195.75900000000001, "r_x3": 50.112, "r_y3": 195.75900000000001, "coord_origin": "TOPLEFT"}, "text": "tion function) that predicts the normalized coordinates for", "orig": "tion function) that predicts the normalized coordinates for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 216.26599999999996, "r_x1": 196.004, "r_y1": 216.26599999999996, "r_x2": 196.004, "r_y2": 207.71400000000006, "r_x3": 50.112, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table cell.", "orig": "the bounding box of each table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.786, "r_y0": 216.26599999999996, "r_x1": 286.365, "r_y1": 216.26599999999996, "r_x2": 286.365, "r_y2": 207.71400000000006, "r_x3": 200.786, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "Finally, the predicted", "orig": "Finally, the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 228.221, "r_x1": 286.365, "r_y1": 228.221, "r_x2": 286.365, "r_y2": 219.66899999999998, "r_x3": 50.112, "r_y3": 219.66899999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes are classified based on whether they are", "orig": "bounding boxes are classified based on whether they are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 240.17700000000002, "r_x1": 181.549, "r_y1": 240.17700000000002, "r_x2": 181.549, "r_y2": 231.625, "r_x3": 50.112, "r_y3": 231.625, "coord_origin": "TOPLEFT"}, "text": "empty or not using a linear layer.", "orig": "empty or not using a linear layer.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92200000000003, "r": 286.366, "b": 444.145, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.451, "r_y0": 252.86199999999997, "r_x1": 276.139, "r_y1": 252.86199999999997, "r_x2": 276.139, "r_y2": 244.30999999999995, "r_x3": 134.451, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "We formulate a multi-task loss Eq.", "orig": "We formulate a multi-task loss Eq.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.379, "r_y0": 252.86199999999997, "r_x1": 286.361, "r_y1": 252.86199999999997, "r_x2": 286.361, "r_y2": 244.30999999999995, "r_x3": 281.379, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.817, "r_x1": 134.685, "r_y1": 264.817, "r_x2": 134.685, "r_y2": 256.265, "r_x3": 50.112, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "to train our network.", "orig": "to train our network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.543, "r_y0": 264.817, "r_x1": 286.365, "r_y1": 264.817, "r_x2": 286.365, "r_y2": 256.265, "r_x3": 140.543, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "The Cross-Entropy loss (denoted as", "orig": "The Cross-Entropy loss (denoted as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.60900000000004, "r_x1": 53.085, "r_y1": 276.60900000000004, "r_x2": 53.085, "r_y2": 267.903, "r_x3": 50.112, "r_y3": 267.903, "coord_origin": "TOPLEFT"}, "text": "l", "orig": "l", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.085, "r_y0": 277.53200000000004, "r_x1": 56.845, "r_y1": 277.53200000000004, "r_x2": 56.845, "r_y2": 271.438, "r_x3": 53.085, "r_y3": 271.438, "coord_origin": "TOPLEFT"}, "text": "s", "orig": "s", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 57.343, "r_y0": 276.773, "r_x1": 135.4, "r_y1": 276.773, "r_x2": 135.4, "r_y2": 268.221, "r_x3": 57.343, "r_y3": 268.221, "coord_origin": "TOPLEFT"}, "text": ") is used to train the", "orig": ") is used to train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.735, "r_y0": 276.63, "r_x1": 211.08, "r_y1": 276.63, "r_x2": 211.08, "r_y2": 268.04200000000003, "r_x3": 137.735, "r_y3": 268.04200000000003, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 213.637, "r_y0": 276.773, "r_x1": 286.364, "r_y1": 276.773, "r_x2": 286.364, "r_y2": 268.221, "r_x3": 213.637, "r_y3": 268.221, "coord_origin": "TOPLEFT"}, "text": "which predicts the", "orig": "which predicts the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.728, "r_x1": 158.824, "r_y1": 288.728, "r_x2": 158.824, "r_y2": 280.176, "r_x3": 50.112, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "structure tokens. As for the", "orig": "structure tokens. As for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 161.318, "r_y0": 288.585, "r_x1": 238.797, "r_y1": 288.585, "r_x2": 238.797, "r_y2": 279.99699999999996, "r_x3": 161.318, "r_y3": 279.99699999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.521, "r_y0": 288.728, "r_x1": 286.363, "r_y1": 288.728, "r_x2": 286.363, "r_y2": 280.176, "r_x3": 241.521, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "it is trained", "orig": "it is trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.683, "r_x1": 211.377, "r_y1": 300.683, "r_x2": 211.377, "r_y2": 292.131, 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In", "orig": ") to be scale invariant as explained in [25]. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.549, "r_x1": 286.365, "r_y1": 336.549, "r_x2": 286.365, "r_y2": 327.997, "r_x3": 50.112, "r_y3": 327.997, "coord_origin": "TOPLEFT"}, "text": "comparison to DETR, we do not use the Hungarian algo-", "orig": "comparison to DETR, we do not use the Hungarian algo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.504, "r_x1": 286.365, "r_y1": 348.504, "r_x2": 286.365, "r_y2": 339.952, "r_x3": 50.112, "r_y3": 339.952, "coord_origin": "TOPLEFT"}, "text": "rithm [15] to match the predicted bounding boxes with the", "orig": "rithm [15] to match the predicted bounding boxes with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, 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"and 2) Our bounding boxes", "orig": "and 2) Our bounding boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.235, "r_x1": 181.967, "r_y1": 420.235, "r_x2": 181.967, "r_y2": 411.683, "r_x3": 50.112, "r_y3": 411.683, "coord_origin": "TOPLEFT"}, "text": "generation mechanism (see Sec.", "orig": "generation mechanism (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 189.09, "r_y0": 420.235, "r_x1": 286.365, "r_y1": 420.235, "r_x2": 286.365, "r_y2": 411.683, "r_x3": 189.09, "r_y3": 411.683, "coord_origin": "TOPLEFT"}, "text": "3) ensures a one-to-one", "orig": "3) ensures a one-to-one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.19, "r_x1": 286.365, "r_y1": 432.19, "r_x2": 286.365, "r_y2": 423.638, "r_x3": 50.112, "r_y3": 423.638, "coord_origin": "TOPLEFT"}, "text": "mapping between the cell content and its bounding box for", "orig": "mapping between the cell content and its bounding box for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.145, "r_x1": 158.296, "r_y1": 444.145, "r_x2": 158.296, "r_y2": 435.593, "r_x3": 50.112, "r_y3": 435.593, "coord_origin": "TOPLEFT"}, "text": "all post-processed datasets.", "orig": "all post-processed datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 448.279, "r": 286.365, "b": 468.786, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 456.831, "r_x1": 286.365, "r_y1": 456.831, "r_x2": 286.365, "r_y2": 448.279, "r_x3": 62.067, "r_y3": 448.279, "coord_origin": "TOPLEFT"}, "text": "The loss used to train the TableFormer can be defined as", "orig": "The loss used to train the TableFormer can be defined as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 468.786, "r_x1": 91.377, "r_y1": 468.786, "r_x2": 91.377, "r_y2": 460.234, "r_x3": 50.112, "r_y3": 460.234, "coord_origin": "TOPLEFT"}, "text": "following:", "orig": "following:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33, "t": 493.391, "r": 286.362, "b": 517.9639999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.715, "r_y0": 502.097, "r_x1": 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Implementation Details"}, {"label": "text", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "text", "bbox": {"l": 50.112, "t": 595.8240000000001, "r": 286.365, "b": 640.421, "coord_origin": "TOPLEFT"}, "confidence": 0.9856163263320923, "cells": [{"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 604.5550000000001, "r_x1": 202.978, "r_y1": 604.5550000000001, "r_x2": 202.978, "r_y2": 596.0029999999999, "r_x3": 62.067, "r_y3": 596.0029999999999, "coord_origin": "TOPLEFT"}, "text": "TableFormer uses ResNet-18 as the", "orig": "TableFormer uses ResNet-18 as the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 205.384, "r_y0": 604.412, "r_x1": 286.36, "r_y1": 604.412, "r_x2": 286.36, "r_y2": 595.8240000000001, "r_x3": 205.384, "r_y3": 595.8240000000001, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Net-", "orig": "CNN Backbone Net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 616.367, "r_x1": 70.037, "r_y1": 616.367, "r_x2": 70.037, "r_y2": 607.779, "r_x3": 50.112, "r_y3": 607.779, "coord_origin": "TOPLEFT"}, "text": "work", "orig": "work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 616.51, "r_x1": 72.528, "r_y1": 616.51, "r_x2": 72.528, "r_y2": 607.958, "r_x3": 70.037, "r_y3": 607.958, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.14, "r_y0": 616.51, "r_x1": 286.365, "r_y1": 616.51, "r_x2": 286.365, "r_y2": 607.958, "r_x3": 77.14, "r_y3": 607.958, "coord_origin": "TOPLEFT"}, "text": "The input images are resized to 448*448 pixels and", "orig": "The input images are resized to 448*448 pixels and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 628.465, "r_x1": 286.365, "r_y1": 628.465, "r_x2": 286.365, "r_y2": 619.913, "r_x3": 50.112, "r_y3": 619.913, "coord_origin": "TOPLEFT"}, "text": "the feature map has a dimension of 28*28. Additionally, we", "orig": "the feature map has a dimension of 28*28. Additionally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 640.421, "r_x1": 207.033, "r_y1": 640.421, "r_x2": 207.033, "r_y2": 631.869, "r_x3": 50.112, "r_y3": 631.869, "coord_origin": "TOPLEFT"}, "text": "enforce the following input constraints:", "orig": "enforce the following input constraints:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. 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(2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "such as EDD, ours are less restrictive due to the improved", "orig": "such as EDD, ours are less restrictive due to the improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "runtime performance and lower memory footprint of Table-", "orig": "runtime performance and lower memory footprint of Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 339.985, "r_y1": 95.98400000000004, "r_x2": 339.985, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "Former.", "orig": "Former.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.889, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 346.889, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "This allows to utilize input samples with longer", "orig": "This allows to utilize input samples with longer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 492.961, "r_y1": 107.93899999999996, "r_x2": 492.961, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "sequences and images with larger dimensions.", "orig": "sequences and images with larger dimensions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. 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instead. Lastly our dropout layers are set to 0.5.", "orig": "weights instead. Lastly our dropout layers are set to 0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 336.846, "r": 545.115, "b": 429.084, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 357.353, "r_x1": 403.736, "r_y1": 357.353, "r_x2": 403.736, "r_y2": 348.801, "r_x3": 308.862, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": "mizers, each one for the", "orig": "mizers, each one for 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367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 429.084, "r_x1": 360.966, "r_y1": 429.084, "r_x2": 360.966, "r_y2": 420.532, "r_x3": 308.862, "r_y3": 420.532, "coord_origin": "TOPLEFT"}, "text": "convergence.", "orig": "convergence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 437.643, "r": 545.115, "b": 553.7909999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, "r_y3": 461.553, "coord_origin": "TOPLEFT"}, "text": "undergoes a single forward pass through the", "orig": "undergoes a single forward pass through the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.078, "r_y0": 469.962, "r_x1": 545.111, "r_y1": 469.962, "r_x2": 545.111, "r_y2": 461.374, "r_x3": 498.078, "r_y3": 461.374, "coord_origin": "TOPLEFT"}, "text": "CNN Back-", "orig": "CNN Back-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 481.917, "r_x1": 364.443, "r_y1": 481.917, "r_x2": 364.443, "r_y2": 473.329, "r_x3": 308.862, "r_y3": 473.329, "coord_origin": "TOPLEFT"}, "text": "bone Network", "orig": "bone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.061, "r_y0": 482.06, "r_x1": 545.113, "r_y1": 482.06, "r_x2": 545.113, "r_y2": 473.508, "r_x3": 367.061, "r_y3": 473.508, "coord_origin": "TOPLEFT"}, "text": "and transformer encoder. This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.862, "t": 579.554, "r": 397.443, "b": 589.406, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 603.718, "r": 545.115, "b": 672.046, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 624.225, "r_x1": 545.115, "r_y1": 624.225, "r_x2": 545.115, "r_y2": 615.673, "r_x3": 308.862, "r_y3": 615.673, "coord_origin": "TOPLEFT"}, "text": "able datasets of different nature to prove the generalization", "orig": "able datasets of different nature to prove the generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 636.1800000000001, "r_x1": 545.115, "r_y1": 636.1800000000001, "r_x2": 545.115, "r_y2": 627.6279999999999, "r_x3": 308.862, "r_y3": 627.6279999999999, "coord_origin": "TOPLEFT"}, "text": "and effectiveness of our model. The datasets used for eval-", "orig": "and effectiveness of our model. The datasets used for eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 648.135, "r_x1": 545.115, "r_y1": 648.135, "r_x2": 545.115, "r_y2": 639.583, "r_x3": 308.862, "r_y3": 639.583, "coord_origin": "TOPLEFT"}, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "orig": "uation are the PubTabNet, FinTabNet and TableBank which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 660.091, "r_x1": 545.115, "r_y1": 660.091, "r_x2": 545.115, "r_y2": 651.539, "r_x3": 308.862, "r_y3": 651.539, "coord_origin": "TOPLEFT"}, "text": "stem from the scientific, financial and general domains re-", "orig": "stem from the scientific, financial and general domains re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 672.046, "r_x1": 350.705, "r_y1": 672.046, "r_x2": 350.705, "r_y2": 663.494, "r_x3": 308.862, "r_y3": 663.494, "coord_origin": "TOPLEFT"}, "text": "spectively.", "orig": "spectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 680.604, "r": 545.115, "b": 713.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 320.817, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "We also share our baseline results on the challenging", "orig": "We also share our baseline results on the challenging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 361.196, "r_y1": 701.111, "r_x2": 361.196, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.057, "r_y0": 701.111, "r_x1": 396.214, "r_y1": 701.111, "r_x2": 396.214, "r_y2": 692.559, "r_x3": 366.057, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.406, "r_y0": 701.111, "r_x1": 453.449, "r_y1": 701.111, "r_x2": 453.449, "r_y2": 692.559, "r_x3": 406.406, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Throughout", "orig": "Throughout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.311, "r_y0": 701.111, "r_x1": 471.591, "r_y1": 701.111, "r_x2": 471.591, "r_y2": 692.559, "r_x3": 458.311, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.453, "r_y0": 701.111, "r_x1": 527.481, "r_y1": 701.111, "r_x2": 527.481, "r_y2": 692.559, "r_x3": 476.453, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "experiments,", "orig": "experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.941, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 532.941, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.067, "r_x1": 495.94, "r_y1": 713.067, "r_x2": 495.94, "r_y2": 704.515, "r_x3": 308.862, "r_y3": 704.515, "coord_origin": "TOPLEFT"}, "text": "same parameters stated in Sec. 5.1 are utilized.", "orig": "same parameters stated in Sec. 5.1 are utilized.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}, {"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 50.112, "t": 75.47699999999998, "r": 286.365, "b": 155.76, "coord_origin": "TOPLEFT"}, "confidence": 0.9868090152740479, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.029, "r_x1": 286.365, "r_y1": 84.029, "r_x2": 286.365, "r_y2": 75.47699999999998, "r_x3": 50.112, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "tention encoding is then multiplied to the encoded image to", "orig": "tention encoding is then multiplied to the encoded image to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 95.98400000000004, "r_x1": 286.365, "r_y1": 95.98400000000004, "r_x2": 286.365, "r_y2": 87.43200000000002, "r_x3": 50.112, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "produce a feature for each table cell. Notice that this is dif-", "orig": "produce a feature for each table cell. Notice that this is dif-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 107.93899999999996, "r_x1": 286.365, "r_y1": 107.93899999999996, "r_x2": 286.365, "r_y2": 99.38699999999994, "r_x3": 50.112, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "ferent than the typical object detection problem where im-", "orig": "ferent than the typical object detection problem where im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 119.894, "r_x1": 286.365, "r_y1": 119.894, "r_x2": 286.365, "r_y2": 111.34199999999998, "r_x3": 50.112, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "balances between the number of detections and the amount", "orig": "balances between the number of detections and the amount", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 131.84900000000005, "r_x1": 135.691, "r_y1": 131.84900000000005, "r_x2": 135.691, "r_y2": 123.29700000000003, "r_x3": 50.112, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "of objects may exist.", "orig": "of objects may exist.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.047, "r_y0": 131.84900000000005, "r_x1": 286.365, "r_y1": 131.84900000000005, "r_x2": 286.365, "r_y2": 123.29700000000003, "r_x3": 142.047, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "In our case, we know up front that", "orig": "In our case, we know up front that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 143.80499999999995, "r_x1": 286.365, "r_y1": 143.80499999999995, "r_x2": 286.365, "r_y2": 135.25300000000004, "r_x3": 50.112, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "the produced detections always match with the table cells", "orig": "the produced detections always match with the table cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 155.76, "r_x1": 175.163, "r_y1": 155.76, "r_x2": 175.163, "r_y2": 147.20799999999997, "r_x3": 50.112, "r_y3": 147.20799999999997, "coord_origin": "TOPLEFT"}, "text": "in number and correspondence.", "orig": "in number and correspondence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 50.112, "t": 159.89300000000003, "r": 286.365, "b": 240.17700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860327839851379, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 168.44500000000005, "r_x1": 108.154, "r_y1": 168.44500000000005, "r_x2": 108.154, "r_y2": 159.89300000000003, "r_x3": 62.067, "r_y3": 159.89300000000003, "coord_origin": "TOPLEFT"}, "text": "The output", "orig": "The output", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 113.275, "r_y0": 168.44500000000005, "r_x1": 144.806, "r_y1": 168.44500000000005, "r_x2": 144.806, "r_y2": 159.89300000000003, "r_x3": 113.275, "r_y3": 159.89300000000003, 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"g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.153, "r_y0": 180.40099999999995, "r_x1": 286.365, "r_y1": 180.40099999999995, "r_x2": 286.365, "r_y2": 171.84900000000005, "r_x3": 143.153, "r_y3": 171.84900000000005, "coord_origin": "TOPLEFT"}, "text": "network (FFN). The FFN consists", "orig": "network (FFN). The FFN consists", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 192.356, "r_x1": 286.365, "r_y1": 192.356, "r_x2": 286.365, "r_y2": 183.80399999999997, "r_x3": 50.112, "r_y3": 183.80399999999997, "coord_origin": "TOPLEFT"}, "text": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "orig": "of a Multi-Layer Perceptron (3 layers with ReLU activa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 204.31100000000004, "r_x1": 286.365, "r_y1": 204.31100000000004, "r_x2": 286.365, "r_y2": 195.75900000000001, "r_x3": 50.112, "r_y3": 195.75900000000001, "coord_origin": "TOPLEFT"}, "text": "tion function) that predicts the normalized coordinates for", "orig": "tion function) that predicts the normalized coordinates for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 216.26599999999996, "r_x1": 196.004, "r_y1": 216.26599999999996, "r_x2": 196.004, "r_y2": 207.71400000000006, "r_x3": 50.112, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "the bounding box of each table cell.", "orig": "the bounding box of each table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 200.786, "r_y0": 216.26599999999996, "r_x1": 286.365, "r_y1": 216.26599999999996, "r_x2": 286.365, "r_y2": 207.71400000000006, "r_x3": 200.786, "r_y3": 207.71400000000006, "coord_origin": "TOPLEFT"}, "text": "Finally, the predicted", "orig": "Finally, the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 228.221, "r_x1": 286.365, "r_y1": 228.221, "r_x2": 286.365, "r_y2": 219.66899999999998, "r_x3": 50.112, "r_y3": 219.66899999999998, "coord_origin": "TOPLEFT"}, "text": "bounding boxes are classified based on whether they are", "orig": "bounding boxes are classified based on whether they are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 240.17700000000002, "r_x1": 181.549, "r_y1": 240.17700000000002, "r_x2": 181.549, "r_y2": 231.625, "r_x3": 50.112, "r_y3": 231.625, "coord_origin": "TOPLEFT"}, "text": "empty or not using a linear layer.", "orig": "empty or not using a linear layer.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer."}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 50.112, "t": 243.92200000000003, "r": 286.366, "b": 444.145, "coord_origin": "TOPLEFT"}, "confidence": 0.9873637557029724, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.87800000000004, "r_x1": 129.215, "r_y1": 252.87800000000004, "r_x2": 129.215, "r_y2": 243.92200000000003, "r_x3": 62.067, "r_y3": 243.92200000000003, "coord_origin": "TOPLEFT"}, "text": "Loss Functions.", "orig": "Loss Functions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.451, "r_y0": 252.86199999999997, "r_x1": 276.139, "r_y1": 252.86199999999997, "r_x2": 276.139, "r_y2": 244.30999999999995, "r_x3": 134.451, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "We formulate a multi-task loss Eq.", "orig": "We formulate a multi-task loss Eq.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.379, "r_y0": 252.86199999999997, "r_x1": 286.361, "r_y1": 252.86199999999997, "r_x2": 286.361, "r_y2": 244.30999999999995, "r_x3": 281.379, "r_y3": 244.30999999999995, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.817, "r_x1": 134.685, "r_y1": 264.817, "r_x2": 134.685, "r_y2": 256.265, "r_x3": 50.112, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "to train our network.", "orig": "to train our network.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.543, "r_y0": 264.817, "r_x1": 286.365, "r_y1": 264.817, "r_x2": 286.365, "r_y2": 256.265, "r_x3": 140.543, "r_y3": 256.265, "coord_origin": "TOPLEFT"}, "text": "The Cross-Entropy loss (denoted as", "orig": "The Cross-Entropy loss (denoted as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.60900000000004, "r_x1": 53.085, "r_y1": 276.60900000000004, "r_x2": 53.085, "r_y2": 267.903, "r_x3": 50.112, "r_y3": 267.903, "coord_origin": "TOPLEFT"}, "text": "l", "orig": "l", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.085, "r_y0": 277.53200000000004, "r_x1": 56.845, "r_y1": 277.53200000000004, "r_x2": 56.845, "r_y2": 271.438, "r_x3": 53.085, "r_y3": 271.438, "coord_origin": "TOPLEFT"}, "text": "s", "orig": "s", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 57.343, "r_y0": 276.773, "r_x1": 135.4, "r_y1": 276.773, "r_x2": 135.4, "r_y2": 268.221, "r_x3": 57.343, "r_y3": 268.221, "coord_origin": "TOPLEFT"}, "text": ") is used to train the", "orig": ") is used to train the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.735, "r_y0": 276.63, "r_x1": 211.08, "r_y1": 276.63, "r_x2": 211.08, "r_y2": 268.04200000000003, "r_x3": 137.735, "r_y3": 268.04200000000003, "coord_origin": "TOPLEFT"}, "text": "Structure Decoder", "orig": "Structure Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 213.637, "r_y0": 276.773, "r_x1": 286.364, "r_y1": 276.773, "r_x2": 286.364, "r_y2": 268.221, "r_x3": 213.637, "r_y3": 268.221, "coord_origin": "TOPLEFT"}, "text": "which predicts the", "orig": "which predicts the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.728, "r_x1": 158.824, "r_y1": 288.728, "r_x2": 158.824, "r_y2": 280.176, "r_x3": 50.112, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "structure tokens. As for the", "orig": "structure tokens. As for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 161.318, "r_y0": 288.585, "r_x1": 238.797, "r_y1": 288.585, "r_x2": 238.797, "r_y2": 279.99699999999996, "r_x3": 161.318, "r_y3": 279.99699999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.521, "r_y0": 288.728, "r_x1": 286.363, "r_y1": 288.728, "r_x2": 286.363, "r_y2": 280.176, "r_x3": 241.521, "r_y3": 280.176, "coord_origin": "TOPLEFT"}, "text": "it is trained", "orig": "it is trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.683, "r_x1": 211.377, "r_y1": 300.683, "r_x2": 211.377, "r_y2": 292.131, 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In", "orig": ") to be scale invariant as explained in [25]. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.549, "r_x1": 286.365, "r_y1": 336.549, "r_x2": 286.365, "r_y2": 327.997, "r_x3": 50.112, "r_y3": 327.997, "coord_origin": "TOPLEFT"}, "text": "comparison to DETR, we do not use the Hungarian algo-", "orig": "comparison to DETR, we do not use the Hungarian algo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.504, "r_x1": 286.365, "r_y1": 348.504, "r_x2": 286.365, "r_y2": 339.952, "r_x3": 50.112, "r_y3": 339.952, "coord_origin": "TOPLEFT"}, "text": "rithm [15] to match the predicted bounding boxes with the", "orig": "rithm [15] to match the predicted bounding boxes with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.459, "r_x1": 286.365, "r_y1": 360.459, "r_x2": 286.365, "r_y2": 351.907, "r_x3": 50.112, "r_y3": 351.907, "coord_origin": "TOPLEFT"}, "text": "ground-truth boxes, as we have already achieved a one-to-", "orig": "ground-truth boxes, as we have already achieved a one-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.414, "r_x1": 286.365, "r_y1": 372.414, "r_x2": 286.365, "r_y2": 363.862, "r_x3": 50.112, "r_y3": 363.862, "coord_origin": "TOPLEFT"}, "text": "one match through two steps: 1) Our token input sequence", "orig": "one match through two steps: 1) Our token input sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.369, "r_x1": 286.365, "r_y1": 384.369, "r_x2": 286.365, "r_y2": 375.817, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.646, "r_y0": 408.137, "r_x1": 170.052, "r_y1": 408.137, "r_x2": 170.052, "r_y2": 399.549, "r_x3": 91.646, "r_y3": 399.549, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.051, "r_y0": 408.28, "r_x1": 172.542, "r_y1": 408.28, "r_x2": 172.542, "r_y2": 399.728, "r_x3": 170.051, "r_y3": 399.728, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.62, "r_y0": 408.28, "r_x1": 286.364, "r_y1": 408.28, "r_x2": 286.364, "r_y2": 399.728, "r_x3": 175.62, "r_y3": 399.728, "coord_origin": "TOPLEFT"}, "text": "and 2) Our bounding boxes", "orig": "and 2) Our bounding boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 420.235, "r_x1": 181.967, "r_y1": 420.235, "r_x2": 181.967, "r_y2": 411.683, "r_x3": 50.112, "r_y3": 411.683, "coord_origin": "TOPLEFT"}, "text": "generation mechanism (see Sec.", "orig": "generation mechanism (see Sec.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 189.09, "r_y0": 420.235, "r_x1": 286.365, "r_y1": 420.235, "r_x2": 286.365, "r_y2": 411.683, "r_x3": 189.09, "r_y3": 411.683, "coord_origin": "TOPLEFT"}, "text": "3) ensures a one-to-one", "orig": "3) ensures a one-to-one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 432.19, "r_x1": 286.365, "r_y1": 432.19, "r_x2": 286.365, "r_y2": 423.638, "r_x3": 50.112, "r_y3": 423.638, "coord_origin": "TOPLEFT"}, "text": "mapping between the cell content and its bounding box for", "orig": "mapping between the cell content and its bounding box for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.145, "r_x1": 158.296, "r_y1": 444.145, "r_x2": 158.296, "r_y2": 435.593, "r_x3": 50.112, "r_y3": 435.593, "coord_origin": "TOPLEFT"}, "text": "all post-processed datasets.", "orig": "all post-processed datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l iou ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets."}, {"label": "text", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 448.279, "r": 286.365, "b": 468.786, "coord_origin": "TOPLEFT"}, "confidence": 0.9724196195602417, "cells": [{"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 456.831, "r_x1": 286.365, "r_y1": 456.831, "r_x2": 286.365, "r_y2": 448.279, "r_x3": 62.067, "r_y3": 448.279, "coord_origin": "TOPLEFT"}, "text": "The loss used to train the TableFormer can be defined as", "orig": "The loss used to train the TableFormer can be defined as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 468.786, "r_x1": 91.377, "r_y1": 468.786, "r_x2": 91.377, "r_y2": 460.234, "r_x3": 50.112, "r_y3": 460.234, "coord_origin": "TOPLEFT"}, "text": "following:", "orig": "following:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The loss used to train the TableFormer can be defined as following:"}, {"label": "formula", "id": 15, "page_no": 5, "cluster": {"id": 15, "label": "formula", "bbox": {"l": 124.33, "t": 493.391, "r": 286.362, "b": 517.9639999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9423348307609558, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 125.715, "r_y0": 502.097, "r_x1": 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(2)"}, {"label": "text", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "text", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9724979996681213, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Although input constraints are used also by other methods,", "orig": "Although input constraints are used also by other methods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "such as EDD, ours are less restrictive due to the improved", "orig": "such as EDD, ours are less restrictive due to the improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved"}, {"label": "text", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "text", "bbox": {"l": 308.862, "t": 75.47699999999998, "r": 545.115, "b": 107.93899999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9799237847328186, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 545.115, "r_y1": 84.029, "r_x2": 545.115, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "runtime performance and lower memory footprint of Table-", "orig": "runtime performance and lower memory footprint of Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 339.985, "r_y1": 95.98400000000004, "r_x2": 339.985, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "Former.", "orig": "Former.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.889, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 346.889, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "This allows to utilize input samples with longer", "orig": "This allows to utilize input samples with longer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 492.961, "r_y1": 107.93899999999996, "r_x2": 492.961, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "sequences and images with larger dimensions.", "orig": "sequences and images with larger dimensions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "runtime performance and lower memory footprint of TableFormer. 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COCO", "orig": "the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.51199999999994, "r_x1": 342.336, "r_y1": 268.51199999999994, "r_x2": 342.336, "r_y2": 259.96000000000004, "r_x3": 308.862, "r_y3": 259.96000000000004, "coord_origin": "TOPLEFT"}, "text": "dataset).", "orig": "dataset).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.952, "r_y0": 268.51199999999994, "r_x1": 545.115, "r_y1": 268.51199999999994, "r_x2": 545.115, "r_y2": 259.96000000000004, "r_x3": 348.952, "r_y3": 259.96000000000004, "coord_origin": "TOPLEFT"}, "text": "Moreover, we have added ResNet blocks to the", "orig": "Moreover, we have added ResNet blocks to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.467, "r_x1": 545.115, "r_y1": 280.467, "r_x2": 545.115, "r_y2": 271.91499999999996, "r_x3": 308.862, "r_y3": 271.91499999999996, "coord_origin": "TOPLEFT"}, "text": "inputs of the Structure Decoder and Cell BBox Decoder.", "orig": "inputs of the Structure Decoder and Cell BBox Decoder.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.422, "r_x1": 545.115, "r_y1": 292.422, "r_x2": 545.115, "r_y2": 283.87, "r_x3": 308.862, "r_y3": 283.87, "coord_origin": "TOPLEFT"}, "text": "This prevents a decoder having a stronger influence over the", "orig": "This prevents a decoder having a stronger influence over the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.377, "r_x1": 545.115, "r_y1": 304.377, "r_x2": 545.115, "r_y2": 295.825, "r_x3": 308.862, "r_y3": 295.825, "coord_origin": "TOPLEFT"}, "text": "learned weights which would damage the other prediction", "orig": "learned weights which would damage the other prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.332, "r_x1": 545.115, "r_y1": 316.332, "r_x2": 545.115, "r_y2": 307.78, "r_x3": 308.862, "r_y3": 307.78, "coord_origin": "TOPLEFT"}, "text": "task (structure vs bounding boxes), but learn task specific", "orig": "task (structure vs bounding boxes), but learn task specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.288, "r_x1": 532.483, "r_y1": 328.288, "r_x2": 532.483, "r_y2": 319.736, "r_x3": 308.862, "r_y3": 319.736, "coord_origin": "TOPLEFT"}, "text": "weights instead. Lastly our dropout layers are set to 0.5.", "orig": "weights instead. Lastly our dropout layers are set to 0.5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The Transformer Encoder consists of two 'Transformer Encoder Layers', with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four 'Transformer Decoder Layers' with similar input and output dimensions as the 'Transformer Encoder Layers'. Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 308.862, "t": 336.846, "r": 545.115, "b": 429.084, "coord_origin": "TOPLEFT"}, "confidence": 0.9877589344978333, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 345.398, "r_x1": 545.115, "r_y1": 345.398, "r_x2": 545.115, "r_y2": 336.846, "r_x3": 320.817, "r_y3": 336.846, "coord_origin": "TOPLEFT"}, "text": "For training, TableFormer is trained with 3 Adam opti-", "orig": "For training, TableFormer is trained with 3 Adam opti-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 357.353, "r_x1": 403.736, "r_y1": 357.353, "r_x2": 403.736, "r_y2": 348.801, "r_x3": 308.862, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": "mizers, each one for the", "orig": "mizers, each one for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.076, "r_y0": 357.21, "r_x1": 545.112, "r_y1": 357.21, "r_x2": 545.112, "r_y2": 348.622, "r_x3": 406.076, "r_y3": 348.622, "coord_origin": "TOPLEFT"}, "text": "CNN Backbone Network Structure", "orig": "CNN Backbone Network Structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 503.539, "r_y0": 357.353, "r_x1": 506.03, "r_y1": 357.353, "r_x2": 506.03, "r_y2": 348.801, "r_x3": 503.539, "r_y3": 348.801, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 369.165, "r_x1": 343.163, "r_y1": 369.165, "r_x2": 343.163, "r_y2": 360.577, "r_x3": 308.862, "r_y3": 360.577, "coord_origin": "TOPLEFT"}, "text": "Decoder", "orig": "Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 343.163, "r_y0": 369.308, "r_x1": 362.202, "r_y1": 369.308, "r_x2": 362.202, "r_y2": 360.756, "r_x3": 343.163, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": ", and", "orig": ", and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.286, "r_y0": 369.165, "r_x1": 440.938, "r_y1": 369.165, "r_x2": 440.938, "r_y2": 360.577, "r_x3": 364.286, "r_y3": 360.577, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.939, "r_y0": 369.308, "r_x1": 443.43, "r_y1": 369.308, "r_x2": 443.43, "r_y2": 360.756, "r_x3": 440.939, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.379, "r_y0": 369.308, "r_x1": 545.108, "r_y1": 369.308, "r_x2": 545.108, "r_y2": 360.756, "r_x3": 446.379, "r_y3": 360.756, "coord_origin": "TOPLEFT"}, "text": "Taking the PubTabNet as", "orig": "Taking the PubTabNet as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 381.263, "r_x1": 545.115, "r_y1": 381.263, "r_x2": 545.115, "r_y2": 372.711, "r_x3": 308.862, "r_y3": 372.711, "coord_origin": "TOPLEFT"}, "text": "an example for our parameter set up, the initializing learn-", "orig": "an example for our parameter set up, the initializing learn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 393.219, "r_x1": 545.115, "r_y1": 393.219, "r_x2": 545.115, "r_y2": 384.667, "r_x3": 308.862, "r_y3": 384.667, "coord_origin": "TOPLEFT"}, "text": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "orig": "ing rate is 0.001 for 12 epochs with a batch size of 24, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 405.01, "r_x1": 314.673, "r_y1": 405.01, "r_x2": 314.673, "r_y2": 396.304, "r_x3": 308.862, "r_y3": 396.304, "coord_origin": "TOPLEFT"}, "text": "\u03bb", "orig": "\u03bb", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.658, "r_y0": 405.174, "r_x1": 329.726, "r_y1": 405.174, "r_x2": 329.726, "r_y2": 396.622, "r_x3": 318.658, 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367.963, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "Afterwards,", "orig": "Afterwards,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 419.519, "r_y0": 405.174, "r_x1": 545.108, "r_y1": 405.174, "r_x2": 545.108, "r_y2": 396.622, "r_x3": 419.519, "r_y3": 396.622, "coord_origin": "TOPLEFT"}, "text": "we reduce the learning rate to", "orig": "we reduce the learning rate to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 417.129, "r_x1": 545.115, "r_y1": 417.129, "r_x2": 545.115, "r_y2": 408.577, "r_x3": 308.862, "r_y3": 408.577, "coord_origin": "TOPLEFT"}, "text": "0.0001, the batch size to 18 and train for 12 more epochs or", "orig": "0.0001, the batch size to 18 and train for 12 more epochs or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 429.084, "r_x1": 360.966, "r_y1": 429.084, "r_x2": 360.966, "r_y2": 420.532, "r_x3": 308.862, "r_y3": 420.532, "coord_origin": "TOPLEFT"}, "text": "convergence.", "orig": "convergence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network Structure , Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and \u03bb set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 437.643, "r": 545.115, "b": 553.7909999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9884491562843323, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 446.195, "r_x1": 545.115, "r_y1": 446.195, "r_x2": 545.115, "r_y2": 437.643, "r_x3": 320.817, "r_y3": 437.643, "coord_origin": "TOPLEFT"}, "text": "TableFormer is implemented with PyTorch and Torchvi-", "orig": "TableFormer is implemented with PyTorch and Torchvi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 458.15, "r_x1": 384.628, "r_y1": 458.15, "r_x2": 384.628, "r_y2": 449.598, "r_x3": 308.862, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "sion libraries [22].", "orig": "sion libraries [22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.372, "r_y0": 458.15, "r_x1": 545.115, "r_y1": 458.15, "r_x2": 545.115, "r_y2": 449.598, "r_x3": 391.372, "r_y3": 449.598, "coord_origin": "TOPLEFT"}, "text": "To speed up the inference, the image", "orig": "To speed up the inference, the image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 470.105, "r_x1": 494.007, "r_y1": 470.105, "r_x2": 494.007, "r_y2": 461.553, "r_x3": 308.862, "r_y3": 461.553, "coord_origin": "TOPLEFT"}, "text": "undergoes a single forward pass through the", "orig": "undergoes a single forward pass through the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.078, "r_y0": 469.962, "r_x1": 545.111, "r_y1": 469.962, "r_x2": 545.111, "r_y2": 461.374, "r_x3": 498.078, "r_y3": 461.374, "coord_origin": "TOPLEFT"}, "text": "CNN Back-", "orig": "CNN Back-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 481.917, "r_x1": 364.443, "r_y1": 481.917, "r_x2": 364.443, "r_y2": 473.329, "r_x3": 308.862, "r_y3": 473.329, "coord_origin": "TOPLEFT"}, "text": "bone Network", "orig": "bone Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.061, "r_y0": 482.06, "r_x1": 545.113, "r_y1": 482.06, "r_x2": 545.113, "r_y2": 473.508, "r_x3": 367.061, "r_y3": 473.508, "coord_origin": "TOPLEFT"}, "text": "and transformer encoder. This eliminates the", "orig": "and transformer encoder. This eliminates the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 494.015, "r_x1": 545.115, "r_y1": 494.015, "r_x2": 545.115, "r_y2": 485.463, "r_x3": 308.862, "r_y3": 485.463, "coord_origin": "TOPLEFT"}, "text": "overhead of generating the same features for each decoding", "orig": "overhead of generating the same features for each decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 505.97, "r_x1": 545.115, "r_y1": 505.97, "r_x2": 545.115, "r_y2": 497.418, "r_x3": 308.862, "r_y3": 497.418, "coord_origin": "TOPLEFT"}, "text": "step. Similarly, we employ a 'caching' technique to preform", "orig": "step. Similarly, we employ a 'caching' technique to preform", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 517.9259999999999, "r_x1": 432.588, "r_y1": 517.9259999999999, "r_x2": 432.588, "r_y2": 509.374, "r_x3": 308.862, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "faster autoregressive decoding.", "orig": "faster autoregressive decoding.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.732, "r_y0": 517.9259999999999, "r_x1": 545.115, "r_y1": 517.9259999999999, "r_x2": 545.115, "r_y2": 509.374, "r_x3": 436.732, "r_y3": 509.374, "coord_origin": "TOPLEFT"}, "text": "This is achieved by storing", "orig": "This is achieved by storing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 529.881, "r_x1": 545.115, "r_y1": 529.881, "r_x2": 545.115, "r_y2": 521.329, "r_x3": 308.862, "r_y3": 521.329, "coord_origin": "TOPLEFT"}, "text": "the features of decoded tokens so we can reuse them for", "orig": "the features of decoded tokens so we can reuse them for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 541.836, "r_x1": 370.062, "r_y1": 541.836, "r_x2": 370.062, "r_y2": 533.284, "r_x3": 308.862, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "each time step.", "orig": "each time step.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.721, "r_y0": 541.836, "r_x1": 545.115, "r_y1": 541.836, "r_x2": 545.115, "r_y2": 533.284, "r_x3": 375.721, "r_y3": 533.284, "coord_origin": "TOPLEFT"}, "text": "Therefore, we only compute the attention", "orig": "Therefore, we only compute the attention", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 553.7909999999999, "r_x1": 377.215, "r_y1": 553.7909999999999, "r_x2": 377.215, "r_y2": 545.239, "r_x3": 308.862, "r_y3": 545.239, "coord_origin": "TOPLEFT"}, "text": "for each new tag.", "orig": "for each new tag.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag."}, {"label": "section_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 308.862, "t": 579.554, "r": 397.443, "b": 589.406, "coord_origin": "TOPLEFT"}, "confidence": 0.9450808763504028, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 589.406, "r_x1": 397.443, "r_y1": 589.406, "r_x2": 397.443, "r_y2": 579.554, "r_x3": 308.862, "r_y3": 579.554, "coord_origin": "TOPLEFT"}, "text": "5.2. Generalization", "orig": "5.2. Generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2. Generalization"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 308.862, "t": 603.718, "r": 545.115, "b": 672.046, "coord_origin": "TOPLEFT"}, "confidence": 0.9880595803260803, "cells": [{"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 612.27, "r_x1": 545.115, "r_y1": 612.27, "r_x2": 545.115, "r_y2": 603.718, "r_x3": 320.817, "r_y3": 603.718, "coord_origin": "TOPLEFT"}, "text": "TableFormer is evaluated on three major publicly avail-", "orig": "TableFormer is evaluated on three major publicly avail-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 624.225, "r_x1": 545.115, "r_y1": 624.225, "r_x2": 545.115, "r_y2": 615.673, "r_x3": 308.862, "r_y3": 615.673, "coord_origin": "TOPLEFT"}, "text": "able datasets of different nature to prove the generalization", "orig": "able datasets of different nature to prove the generalization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 636.1800000000001, "r_x1": 545.115, "r_y1": 636.1800000000001, "r_x2": 545.115, "r_y2": 627.6279999999999, "r_x3": 308.862, "r_y3": 627.6279999999999, "coord_origin": "TOPLEFT"}, "text": "and effectiveness of our model. The datasets used for eval-", "orig": "and effectiveness of our model. The datasets used for eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 648.135, "r_x1": 545.115, "r_y1": 648.135, "r_x2": 545.115, "r_y2": 639.583, "r_x3": 308.862, "r_y3": 639.583, "coord_origin": "TOPLEFT"}, "text": "uation are the PubTabNet, FinTabNet and TableBank which", "orig": "uation are the PubTabNet, FinTabNet and TableBank which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 660.091, "r_x1": 545.115, "r_y1": 660.091, "r_x2": 545.115, "r_y2": 651.539, "r_x3": 308.862, "r_y3": 651.539, "coord_origin": "TOPLEFT"}, "text": "stem from the scientific, financial and general domains re-", "orig": "stem from the scientific, financial and general domains re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 672.046, "r_x1": 350.705, "r_y1": 672.046, "r_x2": 350.705, "r_y2": 663.494, "r_x3": 308.862, "r_y3": 663.494, "coord_origin": "TOPLEFT"}, "text": "spectively.", "orig": "spectively.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 680.604, "r": 545.115, "b": 713.067, "coord_origin": "TOPLEFT"}, "confidence": 0.9830910563468933, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 689.156, "r_x1": 545.115, "r_y1": 689.156, "r_x2": 545.115, "r_y2": 680.604, "r_x3": 320.817, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "We also share our baseline results on the challenging", "orig": "We also share our baseline results on the challenging", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 701.111, "r_x1": 361.196, "r_y1": 701.111, "r_x2": 361.196, "r_y2": 692.559, "r_x3": 308.862, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "SynthTabNet", "orig": "SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.057, "r_y0": 701.111, "r_x1": 396.214, "r_y1": 701.111, "r_x2": 396.214, "r_y2": 692.559, "r_x3": 366.057, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.406, "r_y0": 701.111, "r_x1": 453.449, "r_y1": 701.111, "r_x2": 453.449, "r_y2": 692.559, "r_x3": 406.406, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Throughout", "orig": "Throughout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.311, "r_y0": 701.111, "r_x1": 471.591, "r_y1": 701.111, "r_x2": 471.591, "r_y2": 692.559, "r_x3": 458.311, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 476.453, "r_y0": 701.111, "r_x1": 527.481, "r_y1": 701.111, "r_x2": 527.481, "r_y2": 692.559, "r_x3": 476.453, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "experiments,", "orig": "experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.941, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 532.941, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.067, "r_x1": 495.94, "r_y1": 713.067, "r_x2": 495.94, "r_y2": 704.515, "r_x3": 308.862, "r_y3": 704.515, "coord_origin": "TOPLEFT"}, "text": "same parameters stated in Sec. 5.1 are utilized.", "orig": "same parameters stated in Sec. 5.1 are utilized.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized."}], "headers": [{"label": "page_footer", "id": 17, "page_no": 5, "cluster": {"id": 17, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8820409178733826, "cells": [{"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 84.25300000000004, "r_x1": 167.898, "r_y1": 84.25300000000004, "r_x2": 167.898, "r_y2": 74.40099999999995, "r_x3": 50.112, "r_y3": 74.40099999999995, "coord_origin": "TOPLEFT"}, "text": "5.3. Datasets and Metrics", "orig": "5.3. Datasets and Metrics", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 102.17100000000005, "r_x1": 286.365, "r_y1": 102.17100000000005, "r_x2": 286.365, "r_y2": 93.61900000000003, "r_x3": 62.067, "r_y3": 93.61900000000003, "coord_origin": "TOPLEFT"}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 114.12599999999998, "r_x1": 286.365, "r_y1": 114.12599999999998, "r_x2": 286.365, "r_y2": 105.57399999999996, "r_x3": 50.112, "r_y3": 105.57399999999996, "coord_origin": "TOPLEFT"}, "text": "ric was introduced in [37]. It represents the prediction, and", "orig": "ric was introduced in [37]. It represents the prediction, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 126.08100000000002, "r_x1": 239.999, "r_y1": 126.08100000000002, "r_x2": 239.999, "r_y2": 117.529, "r_x3": 50.112, "r_y3": 117.529, "coord_origin": "TOPLEFT"}, "text": "ground-truth as a tree structure of HTML tags.", "orig": "ground-truth as a tree structure of HTML tags.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.03, "r_y0": 126.08100000000002, "r_x1": 286.365, "r_y1": 126.08100000000002, "r_x2": 286.365, "r_y2": 117.529, "r_x3": 245.03, "r_y3": 117.529, "coord_origin": "TOPLEFT"}, "text": "This simi-", "orig": "This simi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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"r_x3": 50.112, "r_y3": 279.903, "coord_origin": "TOPLEFT"}, "text": "All the more, our model outperforms pre-trained methods.", "orig": "All the more, our model outperforms pre-trained methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.411, "r_x1": 286.365, "r_y1": 300.411, "r_x2": 286.365, "r_y2": 291.859, "r_x3": 50.112, "r_y3": 291.859, "coord_origin": "TOPLEFT"}, "text": "During the evaluation we do not apply any table filtering.", "orig": "During the evaluation we do not apply any table filtering.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.366, "r_x1": 286.365, "r_y1": 312.366, "r_x2": 286.365, "r_y2": 303.814, "r_x3": 50.112, "r_y3": 303.814, "coord_origin": "TOPLEFT"}, "text": "We also provide our baseline results on the SynthTabNet", "orig": "We also provide our baseline results on the SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.321, "r_x1": 80.269, "r_y1": 324.321, "r_x2": 80.269, "r_y2": 315.769, "r_x3": 50.112, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 85.579, "r_y0": 324.321, "r_x1": 91.666, "r_y1": 324.321, "r_x2": 91.666, "r_y2": 315.769, "r_x3": 85.579, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "It", "orig": "It", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 94.894, "r_y0": 324.321, "r_x1": 257.812, "r_y1": 324.321, "r_x2": 257.812, "r_y2": 315.769, "r_x3": 94.894, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "has been observed that large tables (e.g.", "orig": "has been observed that large tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.122, "r_y0": 324.321, "r_x1": 286.365, "r_y1": 324.321, "r_x2": 286.365, "r_y2": 315.769, "r_x3": 263.122, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "tables", "orig": "tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.276, "r_x1": 286.365, "r_y1": 336.276, "r_x2": 286.365, "r_y2": 327.724, "r_x3": 50.112, "r_y3": 327.724, "coord_origin": "TOPLEFT"}, "text": "that occupy half of the page or more) yield poor predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.203, "r_y0": 360.186, "r_x1": 134.147, "r_y1": 360.186, "r_x2": 134.147, "r_y2": 351.634, "r_x3": 119.203, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "that", "orig": "that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.952, "r_y0": 360.186, "r_x1": 286.365, "r_y1": 360.186, "r_x2": 286.365, "r_y2": 351.634, "r_x3": 137.952, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "produces downsampled images with", "orig": "produces downsampled images with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.142, "r_x1": 155.357, "r_y1": 372.142, "r_x2": 155.357, "r_y2": 363.59, "r_x3": 50.112, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "indistinguishable features.", "orig": "indistinguishable features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 396.052, "r_x1": 170.012, "r_y1": 396.052, "r_x2": 170.012, "r_y2": 387.5, "r_x3": 50.112, "r_y3": 387.5, "coord_origin": 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We make use of the", "orig": "with post-processing during inference. 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75.298, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.104, "r_y0": 84.029, "r_x1": 545.11, "r_y1": 84.029, "r_x2": 545.11, "r_y2": 75.47699999999998, "r_x3": 408.104, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "accuracy for cells with a class la-", "orig": "accuracy for cells with a class la-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "bel of 'content' only using the PASCAL VOC mAP metric", "orig": "bel of 'content' only using the PASCAL VOC mAP metric", 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"r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "are only provided. To compare the performance of our pro-", "orig": "are only provided. To compare the performance of our pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 143.80499999999995, "r_x1": 502.017, "r_y1": 143.80499999999995, "r_x2": 502.017, "r_y2": 135.25300000000004, "r_x3": 308.862, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "posed approach, we've integrated TableFormer's", "orig": "posed approach, we've integrated TableFormer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 504.473, "r_y0": 143.66200000000003, "r_x1": 545.11, "r_y1": 143.66200000000003, "r_x2": 545.11, "r_y2": 135.07399999999996, "r_x3": 504.473, "r_y3": 135.07399999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox", "orig": "Cell BBox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 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"from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.455, "r_x1": 286.365, "r_y1": 288.455, "r_x2": 286.365, "r_y2": 279.903, "r_x3": 50.112, "r_y3": 279.903, "coord_origin": "TOPLEFT"}, "text": "All the more, our model outperforms pre-trained methods.", "orig": "All the more, our model outperforms pre-trained methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.411, "r_x1": 286.365, "r_y1": 300.411, "r_x2": 286.365, "r_y2": 291.859, "r_x3": 50.112, "r_y3": 291.859, "coord_origin": "TOPLEFT"}, "text": "During the evaluation we do not apply any table filtering.", "orig": "During the evaluation we do not apply any table filtering.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 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"text": "that occupy half of the page or more) yield poor predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.203, "r_y0": 360.186, "r_x1": 134.147, "r_y1": 360.186, "r_x2": 134.147, "r_y2": 351.634, "r_x3": 119.203, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "that", "orig": "that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.952, "r_y0": 360.186, "r_x1": 286.365, "r_y1": 360.186, "r_x2": 286.365, "r_y2": 351.634, "r_x3": 137.952, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "produces downsampled images with", "orig": "produces downsampled images with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.142, "r_x1": 155.357, "r_y1": 372.142, "r_x2": 155.357, "r_y2": 363.59, "r_x3": 50.112, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "indistinguishable features.", "orig": "indistinguishable features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.529, "r_y0": 107.93899999999996, "r_x1": 545.115, "r_y1": 107.93899999999996, "r_x2": 545.115, "r_y2": 99.38699999999994, "r_x3": 477.529, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "Note that we do", "orig": "Note that we do", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 119.894, "r_x1": 545.115, "r_y1": 119.894, "r_x2": 545.115, "r_y2": 111.34199999999998, "r_x3": 308.862, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "not have post-processing results for SynthTabNet as images", "orig": "not have post-processing results for SynthTabNet as images", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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To compare the performance of our pro-", "orig": "are only provided. To compare the performance of our pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 143.80499999999995, "r_x1": 502.017, "r_y1": 143.80499999999995, "r_x2": 502.017, "r_y2": 135.25300000000004, "r_x3": 308.862, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "posed approach, we've integrated TableFormer's", "orig": "posed approach, we've integrated TableFormer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 504.473, "r_y0": 143.66200000000003, "r_x1": 545.11, "r_y1": 143.66200000000003, "r_x2": 545.11, "r_y2": 135.07399999999996, "r_x3": 504.473, "r_y3": 135.07399999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox", "orig": "Cell BBox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 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Datasets and Metrics", "orig": "5.3. Datasets and Metrics", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.61900000000003, "r": 286.365, "b": 138.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 102.17100000000005, "r_x1": 286.365, "r_y1": 102.17100000000005, "r_x2": 286.365, "r_y2": 93.61900000000003, "r_x3": 62.067, "r_y3": 93.61900000000003, "coord_origin": "TOPLEFT"}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 114.12599999999998, "r_x1": 286.365, "r_y1": 114.12599999999998, "r_x2": 286.365, "r_y2": 105.57399999999996, "r_x3": 50.112, "r_y3": 105.57399999999996, "coord_origin": "TOPLEFT"}, "text": "ric was introduced in [37]. It represents the prediction, and", "orig": "ric was introduced in [37]. 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EditDist denotes the tree-edit distance, and", "orig": "format. EditDist denotes the tree-edit distance, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.182, "r_y0": 201.63199999999995, "r_x1": 257.95, "r_y1": 201.63199999999995, "r_x2": 257.95, "r_y2": 193.06799999999998, "r_x3": 255.182, "r_y3": 193.06799999999998, "coord_origin": "TOPLEFT"}, "text": "|", "orig": "|", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.949, "r_y0": 201.774, "r_x1": 263.771, "r_y1": 201.774, "r_x2": 263.771, "r_y2": 193.06799999999998, "r_x3": 257.949, "r_y3": 193.06799999999998, "coord_origin": "TOPLEFT"}, "text": "T", "orig": "T", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.155, "r_y0": 201.63199999999995, "r_x1": 267.923, "r_y1": 201.63199999999995, "r_x2": 267.923, "r_y2": 193.06799999999998, "r_x3": 265.155, "r_y3": 193.06799999999998, "coord_origin": "TOPLEFT"}, "text": "|", "orig": "|", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.322, "r_y0": 201.938, "r_x1": 286.362, "r_y1": 201.938, "r_x2": 286.362, "r_y2": 193.38599999999997, "r_x3": 270.322, "r_y3": 193.38599999999997, "coord_origin": "TOPLEFT"}, "text": "rep-", "orig": "rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 213.89300000000003, "r_x1": 172.134, "r_y1": 213.89300000000003, "r_x2": 172.134, "r_y2": 205.341, "r_x3": 50.112, "r_y3": 205.341, "coord_origin": "TOPLEFT"}, "text": "resents the number of nodes in", "orig": "resents the number of nodes in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.624, "r_y0": 213.72900000000004, "r_x1": 180.446, "r_y1": 213.72900000000004, "r_x2": 180.446, "r_y2": 205.02300000000002, "r_x3": 174.624, "r_y3": 205.02300000000002, "coord_origin": "TOPLEFT"}, "text": "T", "orig": "T", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.829, "r_y0": 213.89300000000003, "r_x1": 184.32, "r_y1": 213.89300000000003, "r_x2": 184.32, "r_y2": 205.341, "r_x3": 181.829, "r_y3": 205.341, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "where T a and T b represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T ."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 50.112, "t": 224.81899999999996, "r": 170.452, "b": 234.67100000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9588837027549744, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 234.67100000000005, "r_x1": 170.452, "r_y1": 234.67100000000005, "r_x2": 170.452, "r_y2": 224.81899999999996, "r_x3": 50.112, "r_y3": 224.81899999999996, "coord_origin": "TOPLEFT"}, "text": "5.4. Quantitative Analysis", "orig": "5.4. Quantitative Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.4. Quantitative Analysis"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 243.64999999999998, "r": 286.365, "b": 396.052, "coord_origin": "TOPLEFT"}, "confidence": 0.9855090975761414, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 252.606, "r_x1": 105.325, "r_y1": 252.606, "r_x2": 105.325, "r_y2": 243.64999999999998, "r_x3": 62.067, "r_y3": 243.64999999999998, "coord_origin": "TOPLEFT"}, "text": "Structure.", "orig": "Structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.126, "r_y0": 252.59000000000003, "r_x1": 184.684, "r_y1": 252.59000000000003, "r_x2": 184.684, "r_y2": 244.038, "r_x3": 112.126, "r_y3": 244.038, "coord_origin": "TOPLEFT"}, "text": "As shown in Tab.", "orig": "As shown in Tab.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.478, "r_y0": 252.59000000000003, "r_x1": 198.95, "r_y1": 252.59000000000003, "r_x2": 198.95, "r_y2": 244.038, "r_x3": 191.478, "r_y3": 244.038, "coord_origin": "TOPLEFT"}, "text": "2,", "orig": "2,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.995, "r_y0": 252.59000000000003, "r_x1": 286.362, "r_y1": 252.59000000000003, "r_x2": 286.362, "r_y2": 244.038, "r_x3": 202.995, "r_y3": 244.038, "coord_origin": "TOPLEFT"}, "text": "TableFormer outper-", "orig": "TableFormer outper-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 264.54499999999996, "r_x1": 286.365, "r_y1": 264.54499999999996, "r_x2": 286.365, "r_y2": 255.99300000000005, "r_x3": 50.112, "r_y3": 255.99300000000005, "coord_origin": "TOPLEFT"}, "text": "forms all SOTA methods across different datasets by a large", "orig": "forms all SOTA methods across different datasets by a large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 276.5, "r_x1": 138.719, "r_y1": 276.5, "r_x2": 138.719, "r_y2": 267.948, "r_x3": 50.112, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "margin for predicting", "orig": "margin for predicting", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.993, "r_y0": 276.5, "r_x1": 155.168, "r_y1": 276.5, "r_x2": 155.168, "r_y2": 267.948, "r_x3": 142.993, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.432, "r_y0": 276.5, "r_x1": 178.799, "r_y1": 276.5, "r_x2": 178.799, "r_y2": 267.948, "r_x3": 159.432, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "table", "orig": "table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 183.073, "r_y0": 276.5, "r_x1": 217.932, "r_y1": 276.5, "r_x2": 217.932, "r_y2": 267.948, "r_x3": 183.073, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 222.206, "r_y0": 276.5, "r_x1": 286.365, "r_y1": 276.5, "r_x2": 286.365, "r_y2": 267.948, "r_x3": 222.206, "r_y3": 267.948, "coord_origin": "TOPLEFT"}, "text": "from an image.", "orig": "from an image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 288.455, "r_x1": 286.365, "r_y1": 288.455, "r_x2": 286.365, "r_y2": 279.903, "r_x3": 50.112, "r_y3": 279.903, "coord_origin": "TOPLEFT"}, "text": "All the more, our model outperforms pre-trained methods.", "orig": "All the more, our model outperforms pre-trained methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 300.411, "r_x1": 286.365, "r_y1": 300.411, "r_x2": 286.365, "r_y2": 291.859, "r_x3": 50.112, "r_y3": 291.859, "coord_origin": "TOPLEFT"}, "text": "During the evaluation we do not apply any table filtering.", "orig": "During the evaluation we do not apply any table filtering.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 312.366, "r_x1": 286.365, "r_y1": 312.366, "r_x2": 286.365, "r_y2": 303.814, "r_x3": 50.112, "r_y3": 303.814, "coord_origin": "TOPLEFT"}, "text": "We also provide our baseline results on the SynthTabNet", "orig": "We also provide our baseline results on the SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.321, "r_x1": 80.269, "r_y1": 324.321, "r_x2": 80.269, "r_y2": 315.769, "r_x3": 50.112, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 85.579, "r_y0": 324.321, "r_x1": 91.666, "r_y1": 324.321, "r_x2": 91.666, "r_y2": 315.769, "r_x3": 85.579, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "It", "orig": "It", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 94.894, "r_y0": 324.321, "r_x1": 257.812, "r_y1": 324.321, "r_x2": 257.812, "r_y2": 315.769, "r_x3": 94.894, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "has been observed that large tables (e.g.", "orig": "has been observed that large tables (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.122, "r_y0": 324.321, "r_x1": 286.365, "r_y1": 324.321, "r_x2": 286.365, "r_y2": 315.769, "r_x3": 263.122, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "tables", "orig": "tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 336.276, "r_x1": 286.365, "r_y1": 336.276, "r_x2": 286.365, "r_y2": 327.724, "r_x3": 50.112, "r_y3": 327.724, "coord_origin": "TOPLEFT"}, "text": "that occupy half of the page or more) yield poor predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.203, "r_y0": 360.186, "r_x1": 134.147, "r_y1": 360.186, "r_x2": 134.147, "r_y2": 351.634, "r_x3": 119.203, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "that", "orig": "that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 137.952, "r_y0": 360.186, "r_x1": 286.365, "r_y1": 360.186, "r_x2": 286.365, "r_y2": 351.634, "r_x3": 137.952, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "produces downsampled images with", "orig": "produces downsampled images with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 372.142, "r_x1": 155.357, "r_y1": 372.142, "r_x2": 155.357, "r_y2": 363.59, "r_x3": 50.112, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "indistinguishable features.", "orig": "indistinguishable features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1357727050781, "r": 283.0443420410156, "b": 582.3977661132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9892510175704956, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.843, "r_y0": 429.511, "r_x1": 104.855, "r_y1": 429.511, "r_x2": 104.855, "r_y2": 420.959, "r_x3": 78.843, "r_y3": 420.959, "coord_origin": "TOPLEFT"}, "text": "Model", "orig": "Model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.2, "r_y0": 423.533, "r_x1": 236.107, "r_y1": 423.533, "r_x2": 236.107, "r_y2": 414.981, "r_x3": 211.2, "r_y3": 414.981, "coord_origin": "TOPLEFT"}, "text": "TEDS", "orig": "TEDS", "text_direction": 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"bounding boxes that can be improved", "orig": "bounding boxes that can be improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.2, "r_x1": 286.365, "r_y1": 677.2, "r_x2": 286.365, "r_y2": 668.648, "r_x3": 50.112, "r_y3": 668.648, "coord_origin": "TOPLEFT"}, "text": "with post-processing during inference. We make use of the", "orig": "with post-processing during inference. We make use of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 259.655, "r_y1": 689.156, "r_x2": 259.655, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "grid-like structure of tables to refine the predictions.", "orig": "grid-like structure of tables to refine the predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.65, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 263.65, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "A de-", "orig": "A de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tailed explanation on the post-processing is available in the", "orig": "tailed explanation on the post-processing is available in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 147.068, "r_y1": 713.066, "r_x2": 147.068, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "supplementary material.", "orig": "supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.159, "r_y0": 713.066, "r_x1": 223.003, "r_y1": 713.066, "r_x2": 223.003, "r_y2": 704.514, "r_x3": 152.159, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "As shown in Tab.", "orig": "As shown in Tab.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.094, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 228.094, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "3, we evaluate", "orig": "3, we evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.298, "r": 545.115, "b": 227.49099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9713197946548462, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 322.142, "r_y1": 84.029, "r_x2": 322.142, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 325.454, "r_y0": 83.88599999999997, "r_x1": 404.567, "r_y1": 83.88599999999997, "r_x2": 404.567, "r_y2": 75.298, "r_x3": 325.454, "r_y3": 75.298, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.104, "r_y0": 84.029, "r_x1": 545.11, "r_y1": 84.029, "r_x2": 545.11, "r_y2": 75.47699999999998, "r_x3": 408.104, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "accuracy for cells with a class la-", "orig": "accuracy for cells with a class la-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "bel of 'content' only using the PASCAL VOC mAP metric", "orig": "bel of 'content' only using the PASCAL VOC mAP metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 320.478, "r_y1": 107.93899999999996, "r_x2": 320.478, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 324.374, "r_y0": 107.93899999999996, "r_x1": 470.226, "r_y1": 107.93899999999996, "r_x2": 470.226, "r_y2": 99.38699999999994, "r_x3": 324.374, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "pre-processing and post-processing.", "orig": "pre-processing and post-processing.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.529, "r_y0": 107.93899999999996, "r_x1": 545.115, "r_y1": 107.93899999999996, "r_x2": 545.115, "r_y2": 99.38699999999994, "r_x3": 477.529, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "Note that we do", "orig": "Note that we do", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 119.894, "r_x1": 545.115, "r_y1": 119.894, "r_x2": 545.115, "r_y2": 111.34199999999998, "r_x3": 308.862, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "not have post-processing results for SynthTabNet as images", "orig": "not have post-processing results for SynthTabNet as images", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 131.84900000000005, "r_x1": 545.115, "r_y1": 131.84900000000005, "r_x2": 545.115, "r_y2": 123.29700000000003, "r_x3": 308.862, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "are only provided. To compare the performance of our pro-", "orig": "are only provided. To compare the performance of our pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 143.80499999999995, "r_x1": 502.017, "r_y1": 143.80499999999995, "r_x2": 502.017, "r_y2": 135.25300000000004, "r_x3": 308.862, "r_y3": 135.25300000000004, "coord_origin": "TOPLEFT"}, "text": "posed approach, we've integrated TableFormer's", "orig": "posed approach, we've integrated TableFormer's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 504.473, "r_y0": 143.66200000000003, "r_x1": 545.11, "r_y1": 143.66200000000003, "r_x2": 545.11, "r_y2": 135.07399999999996, "r_x3": 504.473, "r_y3": 135.07399999999996, "coord_origin": "TOPLEFT"}, "text": "Cell BBox", "orig": "Cell BBox", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 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Our method", "orig": "data cell and compared with the ground-truth. Our method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 460.306, "r_x1": 350.237, "r_y1": 460.306, "r_x2": 350.237, "r_y2": 451.754, "r_x3": 308.862, "r_y3": 451.754, "coord_origin": "TOPLEFT"}, "text": "achieved a", "orig": "achieved a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.176, "r_y0": 460.322, "r_x1": 374.592, "r_y1": 460.322, "r_x2": 374.592, "r_y2": 451.366, "r_x3": 352.176, "r_y3": 451.366, "coord_origin": "TOPLEFT"}, "text": "5.3%", "orig": "5.3%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.533, "r_y0": 460.306, "r_x1": 545.11, "r_y1": 460.306, "r_x2": 545.11, "r_y2": 451.754, "r_x3": 376.533, "r_y3": 451.754, "coord_origin": "TOPLEFT"}, "text": "increase over the state-of-the-art, and com-", "orig": "increase over the state-of-the-art, and com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 472.261, "r_x1": 380.523, "r_y1": 472.261, "r_x2": 380.523, "r_y2": 463.709, "r_x3": 308.862, "r_y3": 463.709, "coord_origin": "TOPLEFT"}, "text": "mercial solutions.", "orig": "mercial solutions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 386.042, "r_y0": 472.261, "r_x1": 545.115, "r_y1": 472.261, "r_x2": 545.115, "r_y2": 463.709, "r_x3": 386.042, "r_y3": 463.709, "coord_origin": "TOPLEFT"}, "text": "We believe our scores would be higher", "orig": "We believe our scores would be higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.217, "r_x1": 314.949, "r_y1": 484.217, "r_x2": 314.949, "r_y2": 475.665, "r_x3": 308.862, "r_y3": 475.665, "coord_origin": "TOPLEFT"}, "text": "if", "orig": "if", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.008, "r_y0": 484.217, "r_x1": 545.115, "r_y1": 484.217, "r_x2": 545.115, "r_y2": 475.665, "r_x3": 318.008, "r_y3": 475.665, "coord_origin": "TOPLEFT"}, "text": "the HTML ground-truth matched the extracted PDF cell", "orig": "the HTML ground-truth matched the extracted PDF cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.172, "r_x1": 340.683, "r_y1": 496.172, "r_x2": 340.683, "r_y2": 487.62, "r_x3": 308.862, "r_y3": 487.62, "coord_origin": "TOPLEFT"}, "text": "content.", "orig": "content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.793, "r_y0": 496.172, "r_x1": 545.115, "r_y1": 496.172, "r_x2": 545.115, "r_y2": 487.62, "r_x3": 345.793, "r_y3": 487.62, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, there are small discrepancies such", "orig": "Unfortunately, there are small discrepancies such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.127, "r_x1": 545.115, "r_y1": 508.127, "r_x2": 545.115, "r_y2": 499.575, "r_x3": 308.862, "r_y3": 499.575, "coord_origin": "TOPLEFT"}, "text": "as spacings around words or special characters with various", "orig": "as spacings around words or special characters with various", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.082, "r_x1": 405.698, "r_y1": 520.082, "r_x2": 405.698, "r_y2": 511.53, "r_x3": 308.862, "r_y3": 511.53, "coord_origin": "TOPLEFT"}, "text": "unicode representations.", "orig": "unicode representations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835693359375, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775567650794983, "cells": [{"index": 215, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.011, "r_y0": 561.054, "r_x1": 384.023, "r_y1": 561.054, "r_x2": 384.023, "r_y2": 552.502, "r_x3": 358.011, "r_y3": 552.502, "coord_origin": "TOPLEFT"}, "text": "Model", "orig": "Model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 216, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.034, "r_y0": 555.076, "r_x1": 473.94, "r_y1": 555.076, "r_x2": 473.94, "r_y2": 546.524, "r_x3": 449.034, "r_y3": 546.524, "coord_origin": "TOPLEFT"}, "text": "TEDS", "orig": "TEDS", 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677.637, "r_x2": 545.115, "r_y2": 669.085, "r_x3": 430.336, "r_y3": 669.085, "coord_origin": "TOPLEFT"}, "text": "In all cases the input is PDF", "orig": "In all cases the input is PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 248, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 689.592, "r_x1": 435.038, "r_y1": 689.592, "r_x2": 435.038, "r_y2": 681.04, "r_x3": 308.862, "r_y3": 681.04, "coord_origin": "TOPLEFT"}, "text": "documents with cropped tables.", "orig": "documents with cropped tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 4: Results of structure with content retrieved using cell detection on PubTabNet. 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Datasets and Metrics", "orig": "5.3. Datasets and Metrics", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.3. Datasets and Metrics"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 50.112, "t": 93.61900000000003, "r": 286.365, "b": 138.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9862998127937317, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 102.17100000000005, "r_x1": 286.365, "r_y1": 102.17100000000005, "r_x2": 286.365, "r_y2": 93.61900000000003, "r_x3": 62.067, "r_y3": 93.61900000000003, "coord_origin": "TOPLEFT"}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "orig": "The Tree-Edit-Distance-Based Similarity (TEDS) met-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 114.12599999999998, "r_x1": 286.365, "r_y1": 114.12599999999998, "r_x2": 286.365, "r_y2": 105.57399999999996, "r_x3": 50.112, "r_y3": 105.57399999999996, "coord_origin": "TOPLEFT"}, "text": "ric was introduced in [37]. It represents the prediction, and", "orig": "ric was introduced in [37]. It represents the prediction, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 126.08100000000002, "r_x1": 239.999, "r_y1": 126.08100000000002, "r_x2": 239.999, "r_y2": 117.529, "r_x3": 50.112, "r_y3": 117.529, "coord_origin": "TOPLEFT"}, "text": "ground-truth as a tree structure of HTML tags.", "orig": "ground-truth as a tree structure of HTML tags.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.03, "r_y0": 126.08100000000002, "r_x1": 286.365, "r_y1": 126.08100000000002, "r_x2": 286.365, "r_y2": 117.529, "r_x3": 245.03, "r_y3": 117.529, "coord_origin": "TOPLEFT"}, "text": "This simi-", "orig": "This simi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 138.03700000000003, "r_x1": 136.717, "r_y1": 138.03700000000003, "r_x2": 136.717, "r_y2": 129.485, "r_x3": 50.112, "r_y3": 129.485, "coord_origin": "TOPLEFT"}, "text": "larity is calculated as:", "orig": "larity is calculated as:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. 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286.365, "r_y2": 303.814, "r_x3": 50.112, "r_y3": 303.814, "coord_origin": "TOPLEFT"}, "text": "We also provide our baseline results on the SynthTabNet", "orig": "We also provide our baseline results on the SynthTabNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 324.321, "r_x1": 80.269, "r_y1": 324.321, "r_x2": 80.269, "r_y2": 315.769, "r_x3": 50.112, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "dataset.", "orig": "dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 85.579, "r_y0": 324.321, "r_x1": 91.666, "r_y1": 324.321, "r_x2": 91.666, "r_y2": 315.769, "r_x3": 85.579, "r_y3": 315.769, "coord_origin": "TOPLEFT"}, "text": "It", "orig": "It", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 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predictions.", "orig": "that occupy half of the page or more) yield poor predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 348.231, "r_x1": 286.365, "r_y1": 348.231, "r_x2": 286.365, "r_y2": 339.679, "r_x3": 50.112, "r_y3": 339.679, "coord_origin": "TOPLEFT"}, "text": "We attribute this issue to the image resizing during the pre-", "orig": "We attribute this issue to the image resizing during the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 360.186, "r_x1": 115.068, "r_y1": 360.186, "r_x2": 115.068, "r_y2": 351.634, "r_x3": 50.112, "r_y3": 351.634, "coord_origin": "TOPLEFT"}, "text": "processing step,", "orig": "processing step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, 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features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.378, "r_y0": 372.142, "r_x1": 286.365, "r_y1": 372.142, "r_x2": 286.365, "r_y2": 363.59, "r_x3": 160.378, "r_y3": 363.59, "coord_origin": "TOPLEFT"}, "text": "This problem can be addressed", "orig": "This problem can be addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 384.097, "r_x1": 286.365, "r_y1": 384.097, "r_x2": 286.365, "r_y2": 375.545, "r_x3": 50.112, "r_y3": 375.545, "coord_origin": "TOPLEFT"}, "text": "by treating such big tables with a separate model which ac-", "orig": "by treating such big tables with a separate model which ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size."}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 53.368526458740234, "t": 409.1357727050781, "r": 283.0443420410156, "b": 582.3977661132812, "coord_origin": "TOPLEFT"}, "confidence": 0.9892510175704956, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.843, "r_y0": 429.511, "r_x1": 104.855, "r_y1": 429.511, "r_x2": 104.855, "r_y2": 420.959, "r_x3": 78.843, "r_y3": 420.959, "coord_origin": "TOPLEFT"}, "text": "Model", "orig": "Model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.2, "r_y0": 423.533, "r_x1": 236.107, "r_y1": 423.533, "r_x2": 236.107, "r_y2": 414.981, "r_x3": 211.2, "r_y3": 414.981, "coord_origin": "TOPLEFT"}, "text": "TEDS", "orig": "TEDS", "text_direction": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.102, "r_x1": 84.971, "r_y1": 665.102, "r_x2": 84.971, "r_y2": 656.514, "r_x3": 50.112, "r_y3": 656.514, "coord_origin": "TOPLEFT"}, "text": "Detector", "orig": "Detector", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 89.515, "r_y0": 665.245, "r_x1": 123.677, "r_y1": 665.245, "r_x2": 123.677, "r_y2": 656.693, "r_x3": 89.515, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "provides", "orig": "provides", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.991, "r_y0": 665.245, "r_x1": 286.366, "r_y1": 665.245, "r_x2": 286.366, "r_y2": 656.693, "r_x3": 127.991, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "bounding boxes that can be improved", "orig": "bounding boxes that can be improved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.2, "r_x1": 286.365, "r_y1": 677.2, "r_x2": 286.365, "r_y2": 668.648, "r_x3": 50.112, "r_y3": 668.648, "coord_origin": "TOPLEFT"}, "text": "with post-processing during inference. We make use of the", "orig": "with post-processing during inference. 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Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate"}, {"label": "text", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "text", "bbox": {"l": 308.862, "t": 75.298, "r": 545.115, "b": 227.49099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9713197946548462, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 84.029, "r_x1": 322.142, "r_y1": 84.029, "r_x2": 322.142, "r_y2": 75.47699999999998, "r_x3": 308.862, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "our", "orig": "our", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 325.454, "r_y0": 83.88599999999997, "r_x1": 404.567, "r_y1": 83.88599999999997, "r_x2": 404.567, "r_y2": 75.298, "r_x3": 325.454, "r_y3": 75.298, "coord_origin": "TOPLEFT"}, "text": "Cell BBox Decoder", "orig": "Cell BBox Decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 408.104, "r_y0": 84.029, "r_x1": 545.11, "r_y1": 84.029, "r_x2": 545.11, "r_y2": 75.47699999999998, "r_x3": 408.104, "r_y3": 75.47699999999998, "coord_origin": "TOPLEFT"}, "text": "accuracy for cells with a class la-", "orig": "accuracy for cells with a class la-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 95.98400000000004, "r_x1": 545.115, "r_y1": 95.98400000000004, "r_x2": 545.115, "r_y2": 87.43200000000002, "r_x3": 308.862, "r_y3": 87.43200000000002, "coord_origin": "TOPLEFT"}, "text": "bel of 'content' only using the PASCAL VOC mAP metric", "orig": "bel of 'content' only using the PASCAL VOC mAP metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 107.93899999999996, "r_x1": 320.478, "r_y1": 107.93899999999996, "r_x2": 320.478, "r_y2": 99.38699999999994, "r_x3": 308.862, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 324.374, "r_y0": 107.93899999999996, "r_x1": 470.226, "r_y1": 107.93899999999996, "r_x2": 470.226, "r_y2": 99.38699999999994, "r_x3": 324.374, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "pre-processing and post-processing.", "orig": "pre-processing and post-processing.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.529, "r_y0": 107.93899999999996, "r_x1": 545.115, "r_y1": 107.93899999999996, "r_x2": 545.115, "r_y2": 99.38699999999994, "r_x3": 477.529, "r_y3": 99.38699999999994, "coord_origin": "TOPLEFT"}, "text": "Note that we do", "orig": "Note that we do", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 119.894, "r_x1": 545.115, "r_y1": 119.894, "r_x2": 545.115, "r_y2": 111.34199999999998, "r_x3": 308.862, "r_y3": 111.34199999999998, "coord_origin": "TOPLEFT"}, "text": "not have post-processing results for SynthTabNet as images", "orig": "not have post-processing results for SynthTabNet as images", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 131.84900000000005, "r_x1": 545.115, "r_y1": 131.84900000000005, "r_x2": 545.115, "r_y2": 123.29700000000003, "r_x3": 308.862, "r_y3": 123.29700000000003, "coord_origin": "TOPLEFT"}, "text": "are only provided. To compare the performance of our pro-", "orig": "are only provided. 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Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . 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PP: Post-processing.", "orig": "Net, and FinTabNet. PP: Post-processing.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 308.862, "t": 367.68, "r": 545.116, "b": 520.082, "coord_origin": "TOPLEFT"}, "confidence": 0.9835010766983032, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 376.636, "r_x1": 378.949, "r_y1": 376.636, "r_x2": 378.949, "r_y2": 367.68, "r_x3": 320.817, "r_y3": 367.68, "coord_origin": "TOPLEFT"}, "text": "Cell Content.", "orig": "Cell Content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.079, "r_y0": 376.62, "r_x1": 395.378, "r_y1": 376.62, "r_x2": 395.378, "r_y2": 368.068, "r_x3": 387.079, "r_y3": 368.068, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 399.552, "r_y0": 376.62, "r_x1": 413.948, "r_y1": 376.62, "r_x2": 413.948, "r_y2": 368.068, "r_x3": 399.552, "r_y3": 368.068, "coord_origin": "TOPLEFT"}, "text": "this", "orig": "this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 418.112, "r_y0": 376.62, "r_x1": 448.827, "r_y1": 376.62, "r_x2": 448.827, "r_y2": 368.068, "r_x3": 418.112, "r_y3": 368.068, "coord_origin": "TOPLEFT"}, "text": "section,", "orig": "section,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.42, "r_y0": 376.62, "r_x1": 545.116, "r_y1": 376.62, "r_x2": 545.116, "r_y2": 368.068, "r_x3": 453.42, "r_y3": 368.068, "coord_origin": "TOPLEFT"}, "text": "we evaluate the entire", "orig": "we evaluate the entire", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.575, "r_x1": 487.193, "r_y1": 388.575, "r_x2": 487.193, "r_y2": 380.023, "r_x3": 308.862, "r_y3": 380.023, "coord_origin": "TOPLEFT"}, "text": "pipeline of recovering a table with content.", "orig": "pipeline of recovering a table with content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.967, "r_y0": 388.575, "r_x1": 545.115, "r_y1": 388.575, "r_x2": 545.115, "r_y2": 380.023, "r_x3": 493.967, "r_y3": 380.023, "coord_origin": "TOPLEFT"}, "text": "Here we put", "orig": "Here we put", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.53, "r_x1": 545.115, "r_y1": 400.53, "r_x2": 545.115, "r_y2": 391.978, "r_x3": 308.862, "r_y3": 391.978, "coord_origin": "TOPLEFT"}, "text": "our approach to test by capitalizing on extracting content", "orig": "our approach to test by capitalizing on extracting content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.486, "r_x1": 545.115, "r_y1": 412.486, "r_x2": 545.115, "r_y2": 403.934, "r_x3": 308.862, "r_y3": 403.934, "coord_origin": "TOPLEFT"}, "text": "from the PDF cells rather than decoding from images. Tab.", "orig": "from the PDF cells rather than decoding from images. Tab.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.441, "r_x1": 545.115, "r_y1": 424.441, "r_x2": 545.115, "r_y2": 415.889, "r_x3": 308.862, "r_y3": 415.889, "coord_origin": "TOPLEFT"}, "text": "4 shows the TEDs score of HTML code representing the", "orig": "4 shows the TEDs score of HTML code representing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.396, "r_x1": 545.115, "r_y1": 436.396, "r_x2": 545.115, "r_y2": 427.844, "r_x3": 308.862, "r_y3": 427.844, "coord_origin": "TOPLEFT"}, "text": "structure of the table along with the content inserted in the", "orig": "structure of the table along with the content inserted in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.351, "r_x1": 545.115, "r_y1": 448.351, "r_x2": 545.115, "r_y2": 439.799, "r_x3": 308.862, "r_y3": 439.799, "coord_origin": "TOPLEFT"}, "text": "data cell and compared with the ground-truth. Our method", "orig": "data cell and compared with the ground-truth. Our method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 460.306, "r_x1": 350.237, "r_y1": 460.306, "r_x2": 350.237, "r_y2": 451.754, "r_x3": 308.862, "r_y3": 451.754, "coord_origin": "TOPLEFT"}, "text": "achieved a", "orig": "achieved a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.176, "r_y0": 460.322, "r_x1": 374.592, "r_y1": 460.322, "r_x2": 374.592, "r_y2": 451.366, "r_x3": 352.176, "r_y3": 451.366, "coord_origin": "TOPLEFT"}, "text": "5.3%", "orig": "5.3%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 376.533, "r_y0": 460.306, "r_x1": 545.11, "r_y1": 460.306, "r_x2": 545.11, "r_y2": 451.754, "r_x3": 376.533, "r_y3": 451.754, "coord_origin": "TOPLEFT"}, "text": "increase over the state-of-the-art, and com-", "orig": "increase over the state-of-the-art, and com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 472.261, "r_x1": 380.523, "r_y1": 472.261, "r_x2": 380.523, "r_y2": 463.709, "r_x3": 308.862, "r_y3": 463.709, "coord_origin": "TOPLEFT"}, "text": "mercial solutions.", "orig": "mercial solutions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 386.042, "r_y0": 472.261, "r_x1": 545.115, "r_y1": 472.261, "r_x2": 545.115, "r_y2": 463.709, "r_x3": 386.042, "r_y3": 463.709, "coord_origin": "TOPLEFT"}, "text": "We believe our scores would be higher", "orig": "We believe our scores would be higher", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.217, "r_x1": 314.949, "r_y1": 484.217, "r_x2": 314.949, "r_y2": 475.665, "r_x3": 308.862, "r_y3": 475.665, "coord_origin": "TOPLEFT"}, "text": "if", "orig": "if", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.008, "r_y0": 484.217, "r_x1": 545.115, "r_y1": 484.217, "r_x2": 545.115, "r_y2": 475.665, "r_x3": 318.008, "r_y3": 475.665, "coord_origin": "TOPLEFT"}, "text": "the HTML ground-truth matched the extracted PDF cell", "orig": "the HTML ground-truth matched the extracted PDF cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.172, "r_x1": 340.683, "r_y1": 496.172, "r_x2": 340.683, "r_y2": 487.62, "r_x3": 308.862, "r_y3": 487.62, "coord_origin": "TOPLEFT"}, "text": "content.", "orig": "content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.793, "r_y0": 496.172, "r_x1": 545.115, "r_y1": 496.172, "r_x2": 545.115, "r_y2": 487.62, "r_x3": 345.793, "r_y3": 487.62, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, there are small discrepancies such", "orig": "Unfortunately, there are small discrepancies such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.127, "r_x1": 545.115, "r_y1": 508.127, "r_x2": 545.115, "r_y2": 499.575, "r_x3": 308.862, "r_y3": 499.575, "coord_origin": "TOPLEFT"}, "text": "as spacings around words or special characters with various", "orig": "as spacings around words or special characters with various", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.082, "r_x1": 405.698, "r_y1": 520.082, "r_x2": 405.698, "r_y2": 511.53, "r_x3": 308.862, "r_y3": 511.53, "coord_origin": "TOPLEFT"}, "text": "unicode representations.", "orig": "unicode representations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations."}, {"label": "table", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "table", "bbox": {"l": 332.9688720703125, "t": 540.2835693359375, "r": 520.942138671875, "b": 643.2697143554688, "coord_origin": "TOPLEFT"}, "confidence": 0.9775567650794983, "cells": [{"index": 215, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.011, "r_y0": 561.054, "r_x1": 384.023, "r_y1": 561.054, "r_x2": 384.023, "r_y2": 552.502, "r_x3": 358.011, "r_y3": 552.502, "coord_origin": "TOPLEFT"}, "text": "Model", "orig": "Model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 216, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.034, "r_y0": 555.076, "r_x1": 473.94, "r_y1": 555.076, "r_x2": 473.94, "r_y2": 546.524, "r_x3": 449.034, "r_y3": 546.524, "coord_origin": "TOPLEFT"}, "text": "TEDS", "orig": "TEDS", 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"confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.384, "r_y0": 569.604, "r_x1": 286.365, "r_y1": 569.604, "r_x2": 286.365, "r_y2": 561.052, "r_x3": 281.384, "r_y3": 561.052, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 581.559, "r_x1": 286.365, "r_y1": 581.559, "r_x2": 286.365, "r_y2": 573.0070000000001, "r_x3": 50.112, "r_y3": 573.0070000000001, "coord_origin": "TOPLEFT"}, "text": "As it is shown, our model is able to predict bounding boxes", "orig": "As it is shown, our model is able to predict bounding boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 122.341, "r_y1": 593.514, "r_x2": 122.341, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "for all table cells,", "orig": "for all table cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.107, "r_y0": 593.514, "r_x1": 228.004, "r_y1": 593.514, "r_x2": 228.004, "r_y2": 584.962, "r_x3": 126.107, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "even for the empty ones.", "orig": "even for the empty ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.151, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 234.151, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "our post-processing techniques can extract the cell content", "orig": "our post-processing techniques can extract the cell content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "by matching the predicted bounding boxes to the PDF cells", "orig": "by matching the predicted bounding boxes to the PDF cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 230.873, "r_y1": 629.38, "r_x2": 230.873, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "based on their overlap and spatial proximity.", "orig": "based on their overlap and spatial proximity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.855, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 235.855, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "The left part", "orig": "The left part", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 77.121, "r_y1": 641.335, "r_x2": 77.121, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "of Fig.", "orig": "of Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.544, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 81.544, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "5 demonstrates also the adaptability of our method", "orig": "5 demonstrates also the adaptability of our method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 57.863, "r_y1": 653.29, "r_x2": 57.863, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.436, "r_y0": 653.29, "r_x1": 119.691, "r_y1": 653.29, "r_x2": 119.691, "r_y2": 644.738, "r_x3": 62.436, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "any language,", "orig": "any language,", "text_direction": 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"TOPLEFT"}, "text": "text, although the training set contains only English content.", "orig": "text, although the training set contains only English content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "We provide more visualizations including the intermediate", "orig": "We provide more visualizations including the intermediate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 195.217, "r_y1": 689.156, "r_x2": 195.217, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.431, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 199.431, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Overall these illustra-", "orig": "Overall these illustra-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tions justify the versatility of our method across a diverse", "orig": "tions justify the versatility of our method across a diverse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, 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Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.669, "r_x1": 545.115, "r_y1": 533.669, "r_x2": 545.115, "r_y2": 525.117, "r_x3": 308.862, "r_y3": 525.117, "coord_origin": "TOPLEFT"}, "text": "transformer based approach to predict table structures and", "orig": "transformer based approach to predict table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.624, "r_x1": 545.115, "r_y1": 545.624, "r_x2": 545.115, "r_y2": 537.072, "r_x3": 308.862, "r_y3": 537.072, "coord_origin": "TOPLEFT"}, "text": "bounding boxes of cells from an image. This approach en-", "orig": "bounding boxes of cells from an image. 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"coord_origin": "TOPLEFT"}, "text": "scenarios when dealing with various types of PDF docu-", "orig": "scenarios when dealing with various types of PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 215, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 605.4, "r_x1": 335.153, "r_y1": 605.4, "r_x2": 335.153, "r_y2": 596.848, "r_x3": 308.862, "r_y3": 596.848, "coord_origin": "TOPLEFT"}, "text": "ments,", "orig": "ments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 216, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.656, "r_y0": 605.4, "r_x1": 400.468, "r_y1": 605.4, "r_x2": 400.468, "r_y2": 596.848, "r_x3": 339.656, "r_y3": 596.848, "coord_origin": "TOPLEFT"}, "text": "and languages.", "orig": "and languages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 217, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 220, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.234, "r_y0": 617.355, "r_x1": 545.115, "r_y1": 617.355, "r_x2": 545.115, "r_y2": 608.803, "r_x3": 500.234, "r_y3": 608.803, "coord_origin": "TOPLEFT"}, "text": "Finally, we", "orig": "Finally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 221, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.31, "r_x1": 545.115, "r_y1": 629.31, "r_x2": 545.115, "r_y2": 620.758, "r_x3": 308.862, "r_y3": 620.758, "coord_origin": "TOPLEFT"}, "text": "introduce 'SynthTabNet' a challenging synthetically gen-", "orig": "introduce 'SynthTabNet' a challenging synthetically gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 222, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.265, "r_x1": 545.115, "r_y1": 641.265, "r_x2": 545.115, "r_y2": 632.713, "r_x3": 308.862, "r_y3": 632.713, "coord_origin": "TOPLEFT"}, "text": "erated dataset that reinforces missing characteristics from", "orig": "erated dataset that reinforces missing characteristics from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 223, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.22, "r_x1": 365.858, "r_y1": 653.22, "r_x2": 365.858, "r_y2": 644.668, "r_x3": 308.862, "r_y3": 644.668, "coord_origin": "TOPLEFT"}, "text": "other datasets.", "orig": "other datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 224, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 682.847, "r_x1": 364.406, "r_y1": 682.847, "r_x2": 364.406, "r_y2": 672.099, "r_x3": 308.862, "r_y3": 672.099, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 225, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 313.345, "r_y0": 701.901, "r_x1": 323.8, "r_y1": 701.901, "r_x2": 323.8, "r_y2": 694.204, "r_x3": 313.345, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 226, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.785, "r_y0": 701.901, "r_x1": 545.109, "r_y1": 701.901, "r_x2": 545.109, "r_y2": 694.204, "r_x3": 328.785, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 227, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.781, "r_y0": 712.86, "r_x1": 545.113, "r_y1": 712.86, "r_x2": 545.113, "r_y2": 705.163, "r_x3": 328.781, "r_y3": 705.163, "coord_origin": "TOPLEFT"}, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "orig": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. 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false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 122.341, "r_y1": 593.514, "r_x2": 122.341, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "for all table cells,", "orig": "for all table cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.107, "r_y0": 593.514, "r_x1": 228.004, "r_y1": 593.514, "r_x2": 228.004, "r_y2": 584.962, "r_x3": 126.107, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "even for the empty ones.", "orig": "even for the empty ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.151, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 234.151, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 605.47, "r_x1": 286.365, "r_y1": 605.47, "r_x2": 286.365, "r_y2": 596.918, "r_x3": 50.112, "r_y3": 596.918, "coord_origin": "TOPLEFT"}, "text": "our post-processing techniques can extract the cell content", "orig": "our post-processing techniques can extract the cell content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 617.425, "r_x1": 286.365, "r_y1": 617.425, "r_x2": 286.365, "r_y2": 608.873, "r_x3": 50.112, "r_y3": 608.873, "coord_origin": "TOPLEFT"}, "text": "by matching the predicted bounding boxes to the PDF cells", "orig": "by matching the predicted bounding boxes to the PDF cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 629.38, "r_x1": 230.873, "r_y1": 629.38, "r_x2": 230.873, "r_y2": 620.828, "r_x3": 50.112, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "based on their overlap and spatial proximity.", "orig": "based on their overlap and spatial proximity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.855, "r_y0": 629.38, "r_x1": 286.365, "r_y1": 629.38, "r_x2": 286.365, "r_y2": 620.828, "r_x3": 235.855, "r_y3": 620.828, "coord_origin": "TOPLEFT"}, "text": "The left part", "orig": "The left part", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 641.335, "r_x1": 77.121, "r_y1": 641.335, "r_x2": 77.121, "r_y2": 632.783, "r_x3": 50.112, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "of Fig.", "orig": "of Fig.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.544, "r_y0": 641.335, "r_x1": 286.365, "r_y1": 641.335, "r_x2": 286.365, "r_y2": 632.783, "r_x3": 81.544, "r_y3": 632.783, "coord_origin": "TOPLEFT"}, "text": "5 demonstrates also the adaptability of our method", "orig": "5 demonstrates also the adaptability of our method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 653.29, "r_x1": 57.863, "r_y1": 653.29, "r_x2": 57.863, "r_y2": 644.738, "r_x3": 50.112, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.436, "r_y0": 653.29, "r_x1": 119.691, 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255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, "r_x1": 286.365, "r_y1": 665.245, "r_x2": 286.365, "r_y2": 656.693, "r_x3": 50.112, "r_y3": 656.693, "coord_origin": "TOPLEFT"}, "text": "text, although the training set contains only English content.", "orig": "text, although the training set contains only English content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 677.201, "r_x1": 286.365, "r_y1": 677.201, "r_x2": 286.365, "r_y2": 668.649, "r_x3": 50.112, "r_y3": 668.649, "coord_origin": "TOPLEFT"}, "text": "We provide more visualizations including the intermediate", "orig": "We provide more visualizations including the intermediate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 689.156, "r_x1": 195.217, "r_y1": 689.156, "r_x2": 195.217, "r_y2": 680.604, "r_x3": 50.112, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.431, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 199.431, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Overall these illustra-", "orig": "Overall these illustra-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tions justify the versatility of our method across a diverse", "orig": "tions justify the versatility of our method across a diverse", 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Future Work & Conclusion", "orig": "6. 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"r_y3": 333.101, "coord_origin": "TOPLEFT"}, "text": "demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is", "orig": "demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 353.608, "r_x1": 545.114, "r_y1": 353.608, "r_x2": 545.114, "r_y2": 345.056, "r_x3": 50.112, "r_y3": 345.056, "coord_origin": "TOPLEFT"}, "text": "robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from", "orig": "robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 365.564, "r_x1": 139.795, "r_y1": 365.564, "r_x2": 139.795, "r_y2": 357.012, "r_x3": 50.112, "r_y3": 357.012, "coord_origin": "TOPLEFT"}, "text": "the FinTabNet dataset.", "orig": "the FinTabNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). 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"b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 122.341, "r_y1": 593.514, "r_x2": 122.341, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "for all table cells,", "orig": "for all table cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.107, "r_y0": 593.514, "r_x1": 228.004, "r_y1": 593.514, "r_x2": 228.004, "r_y2": 584.962, "r_x3": 126.107, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "even for the empty ones.", "orig": "even for the empty ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.151, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 234.151, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", 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"r_y2": 644.738, "r_x3": 147.765, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "can successfully", "orig": "can successfully", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.426, "r_y0": 653.29, "r_x1": 246.385, "r_y1": 653.29, "r_x2": 246.385, "r_y2": 644.738, "r_x3": 219.426, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "extract", "orig": "extract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.958, "r_y0": 653.29, "r_x1": 286.365, "r_y1": 653.29, "r_x2": 286.365, "r_y2": 644.738, "r_x3": 250.958, "r_y3": 644.738, "coord_origin": "TOPLEFT"}, "text": "Japanese", "orig": "Japanese", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 665.245, 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"coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.431, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 199.431, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Overall these illustra-", "orig": "Overall these illustra-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tions justify the versatility of our method across a diverse", "orig": "tions justify the versatility of our method across a diverse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 226.888, "r_y1": 713.066, "r_x2": 226.888, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "range of table appearances and content type.", "orig": "range of table appearances and content type.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.709, "r": 460.848, "b": 501.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 501.457, "r_x1": 460.848, "r_y1": 501.457, "r_x2": 460.848, "r_y2": 490.709, "r_x3": 308.862, "r_y3": 490.709, "coord_origin": "TOPLEFT"}, "text": "6. Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 513.162, "r": 545.115, "b": 653.22, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.669, "r_x1": 545.115, "r_y1": 533.669, "r_x2": 545.115, "r_y2": 525.117, "r_x3": 308.862, "r_y3": 525.117, "coord_origin": "TOPLEFT"}, "text": "transformer based approach to predict table structures and", "orig": "transformer based approach to predict table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.624, "r_x1": 545.115, "r_y1": 545.624, "r_x2": 545.115, "r_y2": 537.072, "r_x3": 308.862, "r_y3": 537.072, "coord_origin": "TOPLEFT"}, "text": "bounding boxes of cells from an image. This approach en-", "orig": "bounding boxes of cells from an image. 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 220, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.234, "r_y0": 617.355, "r_x1": 545.115, "r_y1": 617.355, "r_x2": 545.115, "r_y2": 608.803, "r_x3": 500.234, "r_y3": 608.803, "coord_origin": "TOPLEFT"}, "text": "Finally, we", "orig": "Finally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 221, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.31, "r_x1": 545.115, "r_y1": 629.31, "r_x2": 545.115, "r_y2": 620.758, "r_x3": 308.862, "r_y3": 620.758, "coord_origin": "TOPLEFT"}, "text": "introduce 'SynthTabNet' a challenging synthetically gen-", "orig": "introduce 'SynthTabNet' a challenging synthetically gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 222, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.265, "r_x1": 545.115, "r_y1": 641.265, "r_x2": 545.115, "r_y2": 632.713, "r_x3": 308.862, "r_y3": 632.713, "coord_origin": "TOPLEFT"}, "text": "erated dataset that reinforces missing characteristics from", "orig": "erated dataset that reinforces missing characteristics from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 223, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.22, "r_x1": 365.858, "r_y1": 653.22, "r_x2": 365.858, "r_y2": 644.668, "r_x3": 308.862, "r_y3": 644.668, "coord_origin": "TOPLEFT"}, "text": "other datasets.", "orig": "other datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.099, "r": 364.406, "b": 682.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9442470073699951, "cells": [{"index": 224, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 682.847, "r_x1": 364.406, "r_y1": 682.847, "r_x2": 364.406, "r_y2": 672.099, "r_x3": 308.862, "r_y3": 672.099, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 694.204, "r": 545.113, "b": 712.86, "coord_origin": "TOPLEFT"}, "confidence": 0.8318753838539124, "cells": [{"index": 225, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 313.345, "r_y0": 701.901, "r_x1": 323.8, "r_y1": 701.901, "r_x2": 323.8, "r_y2": 694.204, "r_x3": 313.345, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 226, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.785, "r_y0": 701.901, "r_x1": 545.109, "r_y1": 701.901, "r_x2": 545.109, "r_y2": 694.204, "r_x3": 328.785, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 227, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.781, "r_y0": 712.86, "r_x1": 545.113, "r_y1": 712.86, "r_x2": 545.113, "r_y2": 705.163, "r_x3": 328.781, "r_y3": 705.163, "coord_origin": "TOPLEFT"}, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "orig": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8709858059883118, "cells": [{"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.5836486816406, "t": 98.65415954589844, "r": 554.8258666992188, "b": 180.6267547607422, "coord_origin": "TOPLEFT"}, "confidence": 0.7699173092842102, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97503662109375, "t": 103.71266174316406, "r": 301.6335754394531, "b": 187.5789337158203, "coord_origin": "TOPLEFT"}, "confidence": 0.7873926162719727, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "caption", "bbox": {"l": 53.812, "t": 208.54100000000005, "r": 385.934, "b": 216.02300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.5986505746841431, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 216.02300000000002, "r_x1": 385.934, "r_y1": 216.02300000000002, "r_x2": 385.934, "r_y2": 208.54100000000005, "r_x3": 53.812, "r_y3": 208.54100000000005, "coord_origin": "TOPLEFT"}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "orig": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "b. Structure predicted by TableFormer, with superimposed matched PDF cell text:"}, {"label": "section_header", "id": 29, "page_no": 7, "cluster": {"id": 29, "label": "section_header", "bbox": {"l": 53.812, "t": 94.58900000000006, "r": 284.346, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.46452972292900085, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.812, "r_y0": 102.07100000000003, "r_x1": 284.346, "r_y1": 102.07100000000003, "r_x2": 284.346, "r_y2": 94.58900000000006, "r_x3": 53.812, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Japanese language (previously unseen by TableFormer):", "orig": "Japanese language (previously unseen by TableFormer):", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Japanese language (previously unseen by TableFormer):"}, {"label": "section_header", "id": 31, "page_no": 7, "cluster": {"id": 31, "label": "section_header", "bbox": {"l": 304.831, "t": 94.58900000000006, "r": 431.091, "b": 102.07100000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.4629225730895996, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 304.831, "r_y0": 102.07100000000003, "r_x1": 431.091, "r_y1": 102.07100000000003, "r_x2": 431.091, "r_y2": 94.58900000000006, "r_x3": 304.831, "r_y3": 94.58900000000006, "coord_origin": "TOPLEFT"}, "text": "Example table from FinTabNet:", "orig": "Example table from FinTabNet:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example table from FinTabNet:"}, {"label": "list_item", "id": 21, "page_no": 7, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 53.286, "t": 78.995, "r": 499.556, "b": 86.47699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.541211724281311, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.286, "r_y0": 86.47699999999998, "r_x1": 499.556, "r_y1": 86.47699999999998, "r_x2": 499.556, "r_y2": 78.995, "r_x3": 53.286, "r_y3": 78.995, "coord_origin": "TOPLEFT"}, "text": "a. 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"r_y3": 333.101, "coord_origin": "TOPLEFT"}, "text": "demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is", "orig": "demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 353.608, "r_x1": 545.114, "r_y1": 353.608, "r_x2": 545.114, "r_y2": 345.056, "r_x3": 50.112, "r_y3": 345.056, "coord_origin": "TOPLEFT"}, "text": "robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from", "orig": "robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 365.564, "r_x1": 139.795, "r_y1": 365.564, "r_x2": 139.795, "r_y2": 357.012, "r_x3": 50.112, "r_y3": 357.012, "coord_origin": "TOPLEFT"}, "text": "the FinTabNet dataset.", "orig": "the FinTabNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). 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"b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 593.514, "r_x1": 122.341, "r_y1": 593.514, "r_x2": 122.341, "r_y2": 584.962, "r_x3": 50.112, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "for all table cells,", "orig": "for all table cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.107, "r_y0": 593.514, "r_x1": 228.004, "r_y1": 593.514, "r_x2": 228.004, "r_y2": 584.962, "r_x3": 126.107, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "even for the empty ones.", "orig": "even for the empty ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.151, "r_y0": 593.514, "r_x1": 286.365, "r_y1": 593.514, "r_x2": 286.365, "r_y2": 584.962, "r_x3": 234.151, "r_y3": 584.962, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", 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"coord_origin": "TOPLEFT"}, "text": "steps in the supplementary material.", "orig": "steps in the supplementary material.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.431, "r_y0": 689.156, "r_x1": 286.365, "r_y1": 689.156, "r_x2": 286.365, "r_y2": 680.604, "r_x3": 199.431, "r_y3": 680.604, "coord_origin": "TOPLEFT"}, "text": "Overall these illustra-", "orig": "Overall these illustra-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 50.112, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "tions justify the versatility of our method across a diverse", "orig": "tions justify the versatility of our method across a diverse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 226.888, "r_y1": 713.066, "r_x2": 226.888, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "range of table appearances and content type.", "orig": "range of table appearances and content type.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We showcase several visualizations for the different components of our network on various 'complex' tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type."}, {"label": "section_header", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 308.862, "t": 490.709, "r": 460.848, "b": 501.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9436525702476501, "cells": [{"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 501.457, "r_x1": 460.848, "r_y1": 501.457, "r_x2": 460.848, "r_y2": 490.709, "r_x3": 308.862, "r_y3": 490.709, "coord_origin": "TOPLEFT"}, "text": "6. Future Work & Conclusion", "orig": "6. Future Work & Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Future Work & Conclusion"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 308.862, "t": 513.162, "r": 545.115, "b": 653.22, "coord_origin": "TOPLEFT"}, "confidence": 0.9875594973564148, "cells": [{"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 521.7139999999999, "r_x1": 545.115, "r_y1": 521.7139999999999, "r_x2": 545.115, "r_y2": 513.162, "r_x3": 320.817, "r_y3": 513.162, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented TableFormer an end-to-end", "orig": "In this paper, we presented TableFormer an end-to-end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.669, "r_x1": 545.115, "r_y1": 533.669, "r_x2": 545.115, "r_y2": 525.117, "r_x3": 308.862, "r_y3": 525.117, "coord_origin": "TOPLEFT"}, "text": "transformer based approach to predict table structures and", "orig": "transformer based approach to predict table structures and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.624, "r_x1": 545.115, "r_y1": 545.624, "r_x2": 545.115, "r_y2": 537.072, "r_x3": 308.862, "r_y3": 537.072, "coord_origin": "TOPLEFT"}, "text": "bounding boxes of cells from an image. This approach en-", "orig": "bounding boxes of cells from an image. 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 220, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.234, "r_y0": 617.355, "r_x1": 545.115, "r_y1": 617.355, "r_x2": 545.115, "r_y2": 608.803, "r_x3": 500.234, "r_y3": 608.803, "coord_origin": "TOPLEFT"}, "text": "Finally, we", "orig": "Finally, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 221, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 629.31, "r_x1": 545.115, "r_y1": 629.31, "r_x2": 545.115, "r_y2": 620.758, "r_x3": 308.862, "r_y3": 620.758, "coord_origin": "TOPLEFT"}, "text": "introduce 'SynthTabNet' a challenging synthetically gen-", "orig": "introduce 'SynthTabNet' a challenging synthetically gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 222, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 641.265, "r_x1": 545.115, "r_y1": 641.265, "r_x2": 545.115, "r_y2": 632.713, "r_x3": 308.862, "r_y3": 632.713, "coord_origin": "TOPLEFT"}, "text": "erated dataset that reinforces missing characteristics from", "orig": "erated dataset that reinforces missing characteristics from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 223, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 653.22, "r_x1": 365.858, "r_y1": 653.22, "r_x2": 365.858, "r_y2": 644.668, "r_x3": 308.862, "r_y3": 644.668, "coord_origin": "TOPLEFT"}, "text": "other datasets.", "orig": "other datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce 'SynthTabNet' a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 308.862, "t": 672.099, "r": 364.406, "b": 682.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9442470073699951, "cells": [{"index": 224, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 682.847, "r_x1": 364.406, "r_y1": 682.847, "r_x2": 364.406, "r_y2": 672.099, "r_x3": 308.862, "r_y3": 672.099, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "References"}, {"label": "list_item", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 313.345, "t": 694.204, "r": 545.113, "b": 712.86, "coord_origin": "TOPLEFT"}, "confidence": 0.8318753838539124, "cells": [{"index": 225, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 313.345, "r_y0": 701.901, "r_x1": 323.8, "r_y1": 701.901, "r_x2": 323.8, "r_y2": 694.204, "r_x3": 313.345, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "[1]", "orig": "[1]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 226, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.785, "r_y0": 701.901, "r_x1": 545.109, "r_y1": 701.901, "r_x2": 545.109, "r_y2": 694.204, "r_x3": 328.785, "r_y3": 694.204, "coord_origin": "TOPLEFT"}, "text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "orig": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 227, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.781, "r_y0": 712.86, "r_x1": 545.113, "r_y1": 712.86, "r_x2": 545.113, "r_y2": 705.163, "r_x3": 328.781, "r_y3": 705.163, "coord_origin": "TOPLEFT"}, "text": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "orig": "Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"}, {"label": "picture", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "picture", "bbox": {"l": 305.5836486816406, "t": 98.65415954589844, "r": 554.8258666992188, "b": 180.6267547607422, "coord_origin": "TOPLEFT"}, "confidence": 0.7699173092842102, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "picture", "bbox": {"l": 49.97503662109375, "t": 103.71266174316406, "r": 301.6335754394531, "b": 187.5789337158203, "coord_origin": "TOPLEFT"}, "confidence": 0.7873926162719727, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 295.121, "t": 734.402, "r": 300.102, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8709858059883118, "cells": [{"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 295.121, "r_y0": 742.954, "r_x1": 300.102, "r_y1": 742.954, "r_x2": 300.102, "r_y2": 734.402, "r_x3": 295.121, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.031, "r_y0": 83.82299999999998, "r_x1": 212.373, "r_y1": 83.82299999999998, "r_x2": 212.373, "r_y2": 76.12599999999998, "r_x3": 70.031, "r_y3": 76.12599999999998, "coord_origin": "TOPLEFT"}, "text": "end object detection with transformers.", "orig": "end object detection with transformers.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 217.313, "r_y0": 83.82299999999998, "r_x1": 286.363, "r_y1": 83.82299999999998, "r_x2": 286.363, "r_y2": 76.12599999999998, "r_x3": 217.313, "r_y3": 76.12599999999998, "coord_origin": "TOPLEFT"}, "text": "In Andrea Vedaldi,", "orig": "In Andrea Vedaldi,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.031, "r_y0": 94.78200000000004, "r_x1": 286.363, "r_y1": 94.78200000000004, "r_x2": 286.363, "r_y2": 87.08500000000004, "r_x3": 70.031, "r_y3": 87.08500000000004, "coord_origin": "TOPLEFT"}, "text": "Horst Bischof, Thomas Brox, and Jan-Michael Frahm, edi-", "orig": "Horst Bischof, Thomas Brox, and Jan-Michael Frahm, edi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.031, "r_y0": 105.74000000000001, "r_x1": 85.722, "r_y1": 105.74000000000001, "r_x2": 85.722, "r_y2": 98.043, "r_x3": 70.031, "r_y3": 98.043, "coord_origin": "TOPLEFT"}, "text": "tors,", "orig": "tors,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.889, "r_y0": 105.61099999999999, "r_x1": 199.933, "r_y1": 105.61099999999999, "r_x2": 199.933, "r_y2": 97.88099999999997, "r_x3": 87.889, "r_y3": 97.88099999999997, "coord_origin": "TOPLEFT"}, "text": "Computer Vision - ECCV 2020", "orig": "Computer Vision - ECCV 2020", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.936, "r_y0": 105.74000000000001, "r_x1": 286.363, "r_y1": 105.74000000000001, "r_x2": 286.363, "r_y2": 98.043, "r_x3": 199.936, "r_y3": 98.043, "coord_origin": "TOPLEFT"}, "text": ", pages 213-229, Cham,", "orig": ", pages 213-229, Cham,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.031, "r_y0": 116.69899999999996, "r_x1": 221.949, "r_y1": 116.69899999999996, "r_x2": 221.949, "r_y2": 109.00199999999995, "r_x3": 70.031, "r_y3": 109.00199999999995, "coord_origin": "TOPLEFT"}, "text": "2020. 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In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1"}], "headers": [{"label": "page_footer", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 292.63, "t": 734.402, "r": 302.593, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.9069584012031555, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.63, "r_y0": 742.954, "r_x1": 302.593, "r_y1": 742.954, "r_x2": 302.593, "r_y2": 734.402, "r_x3": 292.63, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "10", "orig": "10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "10"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.184, "r_y0": 135.48400000000004, "r_x1": 375.043, "r_y1": 135.48400000000004, "r_x2": 375.043, "r_y2": 122.42399999999998, "r_x3": 220.184, "r_y3": 122.42399999999998, "coord_origin": "TOPLEFT"}, "text": "Supplementary Material", "orig": "Supplementary Material", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.382, "r_y0": 244.726, "r_x1": 492.911, "r_y1": 244.726, "r_x2": 492.911, "r_y2": 236.17399999999998, "r_x3": 335.382, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "Prepare styling and content templates:", "orig": "Prepare styling and content templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.669, "r_y0": 244.726, "r_x1": 545.115, "r_y1": 244.726, "r_x2": 545.115, "r_y2": 236.17399999999998, "r_x3": 498.669, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "The styling", "orig": "The styling", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.457, "r_x1": 425.693, "r_y1": 316.457, "r_x2": 425.693, "r_y2": 307.905, "r_x3": 308.862, "r_y3": 307.905, "coord_origin": "TOPLEFT"}, "text": "PubTabNet, FinTabNet, etc.).", "orig": "PubTabNet, FinTabNet, etc.).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.692, "r_y0": 329.171, "r_x1": 435.377, "r_y1": 329.171, "r_x2": 435.377, "r_y2": 320.619, "r_x3": 332.692, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "Generate table structures:", "orig": "Generate table structures:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.342, "r_y0": 329.171, "r_x1": 545.115, "r_y1": 329.171, "r_x2": 545.115, "r_y2": 320.619, "r_x3": 439.342, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "The structure of each syn-", "orig": "The structure of each syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 341.126, "r_x1": 545.115, "r_y1": 341.126, "r_x2": 545.115, "r_y2": 332.574, "r_x3": 308.862, "r_y3": 332.574, "coord_origin": "TOPLEFT"}, "text": "thetic dataset assumes a horizontal table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, "r_y1": 353.081, "r_x2": 545.115, "r_y2": 344.529, "r_x3": 431.462, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "rows and a table body that", "orig": "rows and a table body that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 365.037, "r_x1": 545.115, "r_y1": 365.037, "r_x2": 545.115, "r_y2": 356.485, "r_x3": 308.862, "r_y3": 356.485, "coord_origin": "TOPLEFT"}, "text": "may contain a combination of row spans and column spans.", "orig": "may contain a combination of row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.992, "r_x1": 545.115, "r_y1": 376.992, "r_x2": 545.115, "r_y2": 368.44, "r_x3": 308.862, "r_y3": 368.44, "coord_origin": "TOPLEFT"}, "text": "However, spans are not allowed to cross the header - body", "orig": "However, spans are not allowed to cross the header - body", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.947, "r_x1": 348.334, "r_y1": 388.947, "r_x2": 348.334, "r_y2": 380.395, "r_x3": 308.862, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "boundary.", "orig": "boundary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 353.504, 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rows and columns, number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.857, "r_x1": 545.115, "r_y1": 412.857, "r_x2": 545.115, "r_y2": 404.305, "r_x3": 308.862, "r_y3": 404.305, "coord_origin": "TOPLEFT"}, "text": "header rows, type of spans (header only spans, row only", "orig": "header rows, type of spans (header only spans, row only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.812, "r_x1": 333.49, "r_y1": 424.812, "r_x2": 333.49, "r_y2": 416.26, "r_x3": 308.862, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "spans,", "orig": "spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.082, "r_y0": 424.812, "r_x1": 418.66, "r_y1": 424.812, "r_x2": 418.66, "r_y2": 416.26, "r_x3": 338.082, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "column only spans,", "orig": "column only spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.253, "r_y0": 424.812, "r_x1": 545.115, "r_y1": 424.812, "r_x2": 545.115, "r_y2": 416.26, "r_x3": 423.253, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "both row and column spans),", "orig": "both row and column spans),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.768, "r_x1": 545.115, "r_y1": 436.768, "r_x2": 545.115, "r_y2": 428.216, "r_x3": 308.862, "r_y3": 428.216, "coord_origin": "TOPLEFT"}, "text": "maximum span size and the ratio of the table area covered", "orig": "maximum span size and the ratio of the table area covered", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.723, "r_x1": 345.943, "r_y1": 448.723, "r_x2": 345.943, "r_y2": 440.171, "r_x3": 308.862, "r_y3": 440.171, "coord_origin": "TOPLEFT"}, "text": "by spans.", "orig": "by spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.073, "r_y0": 461.294, "r_x1": 511.864, "r_y1": 461.294, "r_x2": 511.864, "r_y2": 452.706, "r_x3": 488.073, "r_y3": 452.706, "coord_origin": "TOPLEFT"}, "text": "theme", "orig": "theme", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 511.863, "r_y0": 461.437, "r_x1": 545.108, "r_y1": 461.437, "r_x2": 545.108, "r_y2": 452.885, "r_x3": 511.863, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": ", a set of", "orig": ", a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.392, "r_x1": 545.115, "r_y1": 473.392, "r_x2": 545.115, "r_y2": 464.84, "r_x3": 308.862, "r_y3": 464.84, "coord_origin": "TOPLEFT"}, "text": "suitable content templates is chosen first. Then, this content", "orig": "suitable content templates is chosen first. Then, this content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.348, "r_x1": 545.115, "r_y1": 485.348, "r_x2": 545.115, "r_y2": 476.796, "r_x3": 308.862, "r_y3": 476.796, "coord_origin": "TOPLEFT"}, "text": "can be combined with purely random text to produce the", "orig": "can be combined with purely random text to produce the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.303, "r_x1": 379.148, "r_y1": 497.303, "r_x2": 379.148, "r_y2": 488.751, "r_x3": 308.862, "r_y3": 488.751, "coord_origin": "TOPLEFT"}, "text": "synthetic content.", "orig": "synthetic content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.217, "r_y0": 510.017, "r_x1": 434.082, "r_y1": 510.017, "r_x2": 434.082, "r_y2": 501.465, "r_x3": 334.217, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Apply styling templates:", "orig": "Apply styling templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.063, "r_y0": 510.017, "r_x1": 545.115, "r_y1": 510.017, "r_x2": 545.115, "r_y2": 501.465, "r_x3": 439.063, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Depending on the domain", "orig": "Depending on the domain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.972, "r_x1": 406.884, "r_y1": 521.972, "r_x2": 406.884, "r_y2": 513.4200000000001, "r_x3": 308.862, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "of the synthetic dataset,", "orig": "of the synthetic dataset,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.019, "r_y0": 521.972, "r_x1": 545.115, "r_y1": 521.972, "r_x2": 545.115, "r_y2": 513.4200000000001, "r_x3": 411.019, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "a set of styling templates is first", "orig": "a set of styling templates is first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.928, "r_x1": 384.299, "r_y1": 533.928, "r_x2": 384.299, "r_y2": 525.376, "r_x3": 308.862, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "manually selected.", "orig": "manually selected.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.253, "r_y0": 533.928, "r_x1": 545.115, "r_y1": 533.928, "r_x2": 545.115, "r_y2": 525.376, "r_x3": 391.253, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "Then, a style is randomly selected to", "orig": "Then, a style is randomly selected to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.883, "r_x1": 496.159, "r_y1": 545.883, "r_x2": 496.159, "r_y2": 537.331, "r_x3": 308.862, "r_y3": 537.331, "coord_origin": "TOPLEFT"}, "text": "format the appearance of the synthesized table.", "orig": "format the appearance of the synthesized table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.402, "r_y0": 558.597, "r_x1": 450.371, "r_y1": 558.597, "r_x2": 450.371, "r_y2": 550.045, "r_x3": 335.402, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "Render the complete tables:", "orig": "Render the complete tables:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.139, "r_y0": 558.597, "r_x1": 545.115, "r_y1": 558.597, "r_x2": 545.115, "r_y2": 550.045, "r_x3": 456.139, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "The synthetic table is", "orig": "The synthetic table is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 570.552, "r_x1": 334.326, "r_y1": 570.552, "r_x2": 334.326, "r_y2": 562.0, "r_x3": 308.862, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "finally", "orig": "finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.983, "r_y0": 570.552, "r_x1": 545.115, "r_y1": 570.552, "r_x2": 545.115, "r_y2": 562.0, "r_x3": 337.983, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "rendered by a web browser engine to generate the", "orig": "rendered by a web browser engine to generate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 582.5070000000001, "r_x1": 545.115, "r_y1": 582.5070000000001, "r_x2": 545.115, "r_y2": 573.955, "r_x3": 308.862, "r_y3": 573.955, "coord_origin": "TOPLEFT"}, "text": "bounding boxes for each table cell. A batching technique is", "orig": "bounding boxes for each table cell. A batching technique is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 594.463, "r_x1": 545.115, "r_y1": 594.463, "r_x2": 545.115, "r_y2": 585.9110000000001, "r_x3": 308.862, "r_y3": 585.9110000000001, "coord_origin": "TOPLEFT"}, "text": "utilized to optimize the runtime overhead of the rendering", "orig": "utilized to optimize the runtime overhead of the rendering", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 606.418, "r_x1": 341.23, "r_y1": 606.418, "r_x2": 341.23, "r_y2": 597.866, "r_x3": 308.862, "r_y3": 597.866, "coord_origin": "TOPLEFT"}, "text": "process.", "orig": "process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 323.591, "r_y0": 633.039, "r_x1": 376.493, "r_y1": 633.039, "r_x2": 376.493, "r_y2": 622.2909999999999, "r_x3": 323.591, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "Prediction", "orig": "Prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 382.255, "r_y0": 633.039, "r_x1": 461.076, "r_y1": 633.039, "r_x2": 461.076, "r_y2": 622.2909999999999, "r_x3": 382.255, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "post-processing", "orig": "post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.826, "r_y0": 633.039, "r_x1": 481.794, "r_y1": 633.039, "r_x2": 481.794, "r_y2": 622.2909999999999, "r_x3": 466.826, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.556, "r_y0": 633.039, "r_x1": 545.109, "r_y1": 633.039, "r_x2": 545.109, "r_y2": 622.2909999999999, "r_x3": 487.556, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "PDF docu-", "orig": "PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.795, "r_y0": 646.987, "r_x1": 357.341, "r_y1": 646.987, "r_x2": 357.341, "r_y2": 636.239, "r_x3": 326.795, "r_y3": 636.239, "coord_origin": "TOPLEFT"}, "text": "ments", "orig": "ments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 678.197, "r_x1": 545.115, "r_y1": 678.197, "r_x2": 545.115, "r_y2": 669.645, "r_x3": 308.862, "r_y3": 669.645, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes for tables recognized inside PDF docu-", "orig": "the bounding boxes for tables recognized inside PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 690.152, "r_x1": 545.115, "r_y1": 690.152, "r_x2": 545.115, "r_y2": 681.6, "r_x3": 308.862, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "ments, this is not enough when a full reconstruction of the", "orig": "ments, this is not enough when a full reconstruction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 702.107, "r_x1": 545.115, "r_y1": 702.107, "r_x2": 545.115, "r_y2": 693.5550000000001, "r_x3": 308.862, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "original table is required. This happens mainly due the fol-", "orig": "original table is required. This happens mainly due the fol-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 714.063, "r_x1": 371.427, "r_y1": 714.063, "r_x2": 371.427, "r_y2": 705.511, "r_x3": 308.862, "r_y3": 705.511, "coord_origin": "TOPLEFT"}, "text": "lowing reasons:", "orig": "lowing reasons:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 135.48400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.184, "r_y0": 135.48400000000004, "r_x1": 375.043, "r_y1": 135.48400000000004, "r_x2": 375.043, "r_y2": 122.42399999999998, "r_x3": 220.184, "r_y3": 122.42399999999998, "coord_origin": "TOPLEFT"}, "text": "Supplementary Material", "orig": "Supplementary Material", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 200.18899999999996, "r": 286.365, "b": 388.069, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 391.674, "r": 286.365, "b": 627.374, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 630.979, "r": 286.365, "b": 651.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 681.6, "r": 286.365, "b": 714.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 545.115, "b": 207.34199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.50400000000002, "r": 545.115, "b": 232.01099999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 236.17399999999998, "r": 545.115, "b": 316.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, 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"left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": 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table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, 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"r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "original table is required. This happens mainly due the fol-", "orig": "original table is required. This happens mainly due the fol-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 714.063, "r_x1": 371.427, "r_y1": 714.063, "r_x2": 371.427, "r_y2": 705.511, "r_x3": 308.862, "r_y3": 705.511, "coord_origin": "TOPLEFT"}, "text": "lowing reasons:", "orig": "lowing reasons:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 135.48400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.184, "r_y0": 135.48400000000004, "r_x1": 375.043, "r_y1": 135.48400000000004, "r_x2": 375.043, "r_y2": 122.42399999999998, "r_x3": 220.184, "r_y3": 122.42399999999998, "coord_origin": "TOPLEFT"}, "text": "Supplementary Material", "orig": "Supplementary Material", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 200.18899999999996, "r": 286.365, "b": 388.069, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 391.674, "r": 286.365, "b": 627.374, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 630.979, "r": 286.365, "b": 651.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 681.6, "r": 286.365, "b": 714.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 545.115, "b": 207.34199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.50400000000002, "r": 545.115, "b": 232.01099999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 236.17399999999998, "r": 545.115, "b": 316.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.382, "r_y0": 244.726, "r_x1": 492.911, "r_y1": 244.726, "r_x2": 492.911, "r_y2": 236.17399999999998, "r_x3": 335.382, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "Prepare styling and content templates:", "orig": "Prepare styling and content templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.669, "r_y0": 244.726, "r_x1": 545.115, "r_y1": 244.726, "r_x2": 545.115, "r_y2": 236.17399999999998, "r_x3": 498.669, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "The styling", "orig": "The styling", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.457, "r_x1": 425.693, "r_y1": 316.457, "r_x2": 425.693, "r_y2": 307.905, "r_x3": 308.862, "r_y3": 307.905, "coord_origin": "TOPLEFT"}, "text": "PubTabNet, FinTabNet, etc.).", "orig": "PubTabNet, FinTabNet, etc.).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.619, "r": 545.115, "b": 448.723, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.692, "r_y0": 329.171, "r_x1": 435.377, "r_y1": 329.171, "r_x2": 435.377, "r_y2": 320.619, "r_x3": 332.692, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "Generate table structures:", "orig": "Generate table structures:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.342, "r_y0": 329.171, "r_x1": 545.115, "r_y1": 329.171, "r_x2": 545.115, "r_y2": 320.619, "r_x3": 439.342, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "The structure of each syn-", "orig": "The structure of each syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 341.126, "r_x1": 545.115, "r_y1": 341.126, "r_x2": 545.115, "r_y2": 332.574, "r_x3": 308.862, "r_y3": 332.574, "coord_origin": "TOPLEFT"}, "text": "thetic dataset assumes a horizontal table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, "r_y1": 353.081, "r_x2": 545.115, "r_y2": 344.529, "r_x3": 431.462, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "rows and a table body that", "orig": "rows and a table body that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 365.037, "r_x1": 545.115, "r_y1": 365.037, "r_x2": 545.115, "r_y2": 356.485, "r_x3": 308.862, "r_y3": 356.485, "coord_origin": "TOPLEFT"}, "text": "may contain a combination of row spans and column spans.", "orig": "may contain a combination of row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.992, "r_x1": 545.115, "r_y1": 376.992, "r_x2": 545.115, "r_y2": 368.44, "r_x3": 308.862, "r_y3": 368.44, "coord_origin": "TOPLEFT"}, "text": "However, spans are not allowed to cross the header - body", "orig": "However, spans are not allowed to cross the header - body", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.947, "r_x1": 348.334, "r_y1": 388.947, "r_x2": 348.334, "r_y2": 380.395, "r_x3": 308.862, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "boundary.", "orig": "boundary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 353.504, "r_y0": 388.947, "r_x1": 545.115, "r_y1": 388.947, "r_x2": 545.115, "r_y2": 380.395, "r_x3": 353.504, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "The table structure is described by the parame-", "orig": "The table structure is described by the parame-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.902, "r_x1": 326.018, "r_y1": 400.902, "r_x2": 326.018, "r_y2": 392.35, "r_x3": 308.862, "r_y3": 392.35, "coord_origin": "TOPLEFT"}, "text": "ters:", "orig": "ters:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.909, "r_y0": 400.902, "r_x1": 545.115, "r_y1": 400.902, "r_x2": 545.115, "r_y2": 392.35, "r_x3": 330.909, "r_y3": 392.35, "coord_origin": "TOPLEFT"}, "text": "Total number of table rows and columns, number of", "orig": "Total number of table rows and columns, number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.857, "r_x1": 545.115, "r_y1": 412.857, "r_x2": 545.115, "r_y2": 404.305, "r_x3": 308.862, "r_y3": 404.305, "coord_origin": "TOPLEFT"}, "text": "header rows, type of spans (header only spans, row only", "orig": "header rows, type of spans (header only spans, row only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.812, "r_x1": 333.49, "r_y1": 424.812, "r_x2": 333.49, "r_y2": 416.26, "r_x3": 308.862, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "spans,", "orig": "spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.082, "r_y0": 424.812, "r_x1": 418.66, "r_y1": 424.812, "r_x2": 418.66, "r_y2": 416.26, "r_x3": 338.082, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "column only spans,", "orig": "column only spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.253, "r_y0": 424.812, "r_x1": 545.115, "r_y1": 424.812, "r_x2": 545.115, "r_y2": 416.26, "r_x3": 423.253, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "both row and column spans),", "orig": "both row and column spans),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.768, "r_x1": 545.115, "r_y1": 436.768, "r_x2": 545.115, "r_y2": 428.216, "r_x3": 308.862, "r_y3": 428.216, "coord_origin": "TOPLEFT"}, "text": "maximum span size and the ratio of the table area covered", "orig": "maximum span size and the ratio of the table area covered", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.723, "r_x1": 345.943, "r_y1": 448.723, "r_x2": 345.943, "r_y2": 440.171, "r_x3": 308.862, "r_y3": 440.171, "coord_origin": "TOPLEFT"}, "text": "by spans.", "orig": "by spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.706, "r": 545.115, "b": 497.303, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.073, "r_y0": 461.294, "r_x1": 511.864, "r_y1": 461.294, "r_x2": 511.864, "r_y2": 452.706, "r_x3": 488.073, "r_y3": 452.706, "coord_origin": "TOPLEFT"}, "text": "theme", "orig": "theme", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 511.863, "r_y0": 461.437, "r_x1": 545.108, "r_y1": 461.437, "r_x2": 545.108, "r_y2": 452.885, "r_x3": 511.863, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": ", a set of", "orig": ", a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.392, "r_x1": 545.115, "r_y1": 473.392, "r_x2": 545.115, "r_y2": 464.84, "r_x3": 308.862, "r_y3": 464.84, "coord_origin": "TOPLEFT"}, "text": "suitable content templates is chosen first. Then, this content", "orig": "suitable content templates is chosen first. Then, this content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.348, "r_x1": 545.115, "r_y1": 485.348, "r_x2": 545.115, "r_y2": 476.796, "r_x3": 308.862, "r_y3": 476.796, "coord_origin": "TOPLEFT"}, "text": "can be combined with purely random text to produce the", "orig": "can be combined with purely random text to produce the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.303, "r_x1": 379.148, "r_y1": 497.303, "r_x2": 379.148, "r_y2": 488.751, "r_x3": 308.862, "r_y3": 488.751, "coord_origin": "TOPLEFT"}, "text": "synthetic content.", "orig": "synthetic content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.465, "r": 545.115, "b": 545.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.217, "r_y0": 510.017, "r_x1": 434.082, "r_y1": 510.017, "r_x2": 434.082, "r_y2": 501.465, "r_x3": 334.217, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Apply styling templates:", "orig": "Apply styling templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.063, "r_y0": 510.017, "r_x1": 545.115, "r_y1": 510.017, "r_x2": 545.115, "r_y2": 501.465, "r_x3": 439.063, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Depending on the domain", "orig": "Depending on the domain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.972, "r_x1": 406.884, "r_y1": 521.972, "r_x2": 406.884, "r_y2": 513.4200000000001, "r_x3": 308.862, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "of the synthetic dataset,", "orig": "of the synthetic dataset,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.019, "r_y0": 521.972, "r_x1": 545.115, "r_y1": 521.972, "r_x2": 545.115, "r_y2": 513.4200000000001, "r_x3": 411.019, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "a set of styling templates is first", "orig": "a set of styling templates is first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.928, "r_x1": 384.299, "r_y1": 533.928, "r_x2": 384.299, "r_y2": 525.376, "r_x3": 308.862, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "manually selected.", "orig": "manually selected.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.253, "r_y0": 533.928, "r_x1": 545.115, "r_y1": 533.928, "r_x2": 545.115, "r_y2": 525.376, "r_x3": 391.253, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "Then, a style is randomly selected to", "orig": "Then, a style is randomly selected to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.883, "r_x1": 496.159, "r_y1": 545.883, "r_x2": 496.159, "r_y2": 537.331, "r_x3": 308.862, "r_y3": 537.331, "coord_origin": "TOPLEFT"}, "text": "format the appearance of the synthesized table.", "orig": "format the appearance of the synthesized table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 550.045, "r": 545.115, "b": 606.418, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.402, "r_y0": 558.597, "r_x1": 450.371, "r_y1": 558.597, "r_x2": 450.371, "r_y2": 550.045, "r_x3": 335.402, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "Render the complete tables:", "orig": "Render the complete tables:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.139, "r_y0": 558.597, "r_x1": 545.115, "r_y1": 558.597, "r_x2": 545.115, "r_y2": 550.045, "r_x3": 456.139, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "The synthetic table is", "orig": "The synthetic table is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 570.552, "r_x1": 334.326, "r_y1": 570.552, "r_x2": 334.326, "r_y2": 562.0, "r_x3": 308.862, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "finally", "orig": "finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.983, "r_y0": 570.552, "r_x1": 545.115, "r_y1": 570.552, "r_x2": 545.115, "r_y2": 562.0, "r_x3": 337.983, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "rendered by a web browser engine to generate the", "orig": "rendered by a web browser engine to generate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 582.5070000000001, "r_x1": 545.115, "r_y1": 582.5070000000001, "r_x2": 545.115, "r_y2": 573.955, "r_x3": 308.862, "r_y3": 573.955, "coord_origin": "TOPLEFT"}, "text": "bounding boxes for each table cell. A batching technique is", "orig": "bounding boxes for each table cell. A batching technique is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 594.463, "r_x1": 545.115, "r_y1": 594.463, "r_x2": 545.115, "r_y2": 585.9110000000001, "r_x3": 308.862, "r_y3": 585.9110000000001, "coord_origin": "TOPLEFT"}, "text": "utilized to optimize the runtime overhead of the rendering", "orig": "utilized to optimize the runtime overhead of the rendering", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 606.418, "r_x1": 341.23, "r_y1": 606.418, "r_x2": 341.23, "r_y2": 597.866, "r_x3": 308.862, "r_y3": 597.866, "coord_origin": "TOPLEFT"}, "text": "process.", "orig": "process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process."}, {"label": "section_header", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 308.862, "t": 622.2909999999999, "r": 545.109, "b": 646.987, "coord_origin": "TOPLEFT"}, "confidence": 0.954940915107727, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 633.039, "r_x1": 317.828, "r_y1": 633.039, "r_x2": 317.828, "r_y2": 622.2909999999999, "r_x3": 308.862, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 323.591, "r_y0": 633.039, "r_x1": 376.493, "r_y1": 633.039, "r_x2": 376.493, "r_y2": 622.2909999999999, "r_x3": 323.591, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "Prediction", "orig": "Prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 382.255, "r_y0": 633.039, "r_x1": 461.076, "r_y1": 633.039, "r_x2": 461.076, "r_y2": 622.2909999999999, "r_x3": 382.255, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "post-processing", "orig": "post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.826, "r_y0": 633.039, "r_x1": 481.794, "r_y1": 633.039, "r_x2": 481.794, "r_y2": 622.2909999999999, "r_x3": 466.826, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.556, "r_y0": 633.039, "r_x1": 545.109, "r_y1": 633.039, "r_x2": 545.109, "r_y2": 622.2909999999999, "r_x3": 487.556, "r_y3": 622.2909999999999, "coord_origin": "TOPLEFT"}, "text": "PDF docu-", "orig": "PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.795, "r_y0": 646.987, "r_x1": 357.341, "r_y1": 646.987, "r_x2": 357.341, "r_y2": 636.239, "r_x3": 326.795, "r_y3": 636.239, "coord_origin": "TOPLEFT"}, "text": "ments", "orig": "ments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Prediction post-processing for PDF documents"}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 308.862, "t": 657.69, "r": 545.115, "b": 714.063, "coord_origin": "TOPLEFT"}, "confidence": 0.9829329252243042, "cells": [{"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 666.242, "r_x1": 545.115, "r_y1": 666.242, "r_x2": 545.115, "r_y2": 657.69, "r_x3": 320.817, "r_y3": 657.69, "coord_origin": "TOPLEFT"}, "text": "Although TableFormer can predict the table structure and", "orig": "Although TableFormer can predict the table structure and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 678.197, "r_x1": 545.115, "r_y1": 678.197, "r_x2": 545.115, "r_y2": 669.645, "r_x3": 308.862, "r_y3": 669.645, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes for tables recognized inside PDF docu-", "orig": "the bounding boxes for tables recognized inside PDF docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 690.152, "r_x1": 545.115, "r_y1": 690.152, "r_x2": 545.115, "r_y2": 681.6, "r_x3": 308.862, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "ments, this is not enough when a full reconstruction of the", "orig": "ments, this is not enough when a full reconstruction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 702.107, "r_x1": 545.115, "r_y1": 702.107, "r_x2": 545.115, "r_y2": 693.5550000000001, "r_x3": 308.862, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "original table is required. This happens mainly due the fol-", "orig": "original table is required. This happens mainly due the fol-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 714.063, "r_x1": 371.427, "r_y1": 714.063, "r_x2": 371.427, "r_y2": 705.511, "r_x3": 308.862, "r_y3": 705.511, "coord_origin": "TOPLEFT"}, "text": "lowing reasons:", "orig": "lowing reasons:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:"}, {"label": "page_footer", "id": 16, "page_no": 10, "cluster": {"id": 16, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.8986663222312927, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "11"}], "body": [{"label": "section_header", "id": 18, "page_no": 10, "cluster": {"id": 18, "label": "section_header", "bbox": {"l": 132.842, "t": 110.57500000000005, "r": 465.376, "b": 135.48400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.6497084498405457, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.842, "r_y0": 121.32299999999998, "r_x1": 465.376, "r_y1": 121.32299999999998, "r_x2": 465.376, "r_y2": 110.57500000000005, "r_x3": 132.842, "r_y3": 110.57500000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer: Table Structure Understanding with Transformers", "orig": "TableFormer: Table Structure Understanding with Transformers", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.184, "r_y0": 135.48400000000004, "r_x1": 375.043, "r_y1": 135.48400000000004, "r_x2": 375.043, "r_y2": 122.42399999999998, "r_x3": 220.184, "r_y3": 122.42399999999998, "coord_origin": "TOPLEFT"}, "text": "Supplementary Material", "orig": "Supplementary Material", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "TableFormer: Table Structure Understanding with Transformers Supplementary Material"}, {"label": "section_header", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 50.112, "t": 161.16099999999994, "r": 175.964, "b": 171.909, "coord_origin": "TOPLEFT"}, "confidence": 0.9454860091209412, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 171.909, "r_x1": 175.964, "r_y1": 171.909, "r_x2": 175.964, "r_y2": 161.16099999999994, "r_x3": 50.112, "r_y3": 161.16099999999994, "coord_origin": "TOPLEFT"}, "text": "1. Details on the datasets", "orig": "1. Details on the datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Details on the datasets"}, {"label": "section_header", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 50.112, "t": 180.97900000000004, "r": 150.364, "b": 190.83100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9535645842552185, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 190.83100000000002, "r_x1": 150.364, "r_y1": 190.83100000000002, "r_x2": 150.364, "r_y2": 180.97900000000004, "r_x3": 50.112, "r_y3": 180.97900000000004, "coord_origin": "TOPLEFT"}, "text": "1.1. Data preparation", "orig": "1.1. Data preparation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1. Data preparation"}, {"label": "text", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 200.18899999999996, "r": 286.365, "b": 388.069, "coord_origin": "TOPLEFT"}, "confidence": 0.9864971041679382, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 208.74099999999999, "r_x1": 286.365, "r_y1": 208.74099999999999, "r_x2": 286.365, "r_y2": 200.18899999999996, "r_x3": 62.067, "r_y3": 200.18899999999996, "coord_origin": "TOPLEFT"}, "text": "As a first step of our data preparation process, we have", "orig": "As a first step of our data preparation process, we have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 220.697, "r_x1": 286.365, "r_y1": 220.697, "r_x2": 286.365, "r_y2": 212.14499999999998, "r_x3": 50.112, "r_y3": 212.14499999999998, "coord_origin": "TOPLEFT"}, "text": "calculated statistics over the datasets across the following", "orig": "calculated statistics over the datasets across the following", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 232.65200000000004, "r_x1": 286.365, "r_y1": 232.65200000000004, "r_x2": 286.365, "r_y2": 224.10000000000002, "r_x3": 50.112, "r_y3": 224.10000000000002, "coord_origin": "TOPLEFT"}, "text": "dimensions: (1) table size measured in the number of rows", "orig": "dimensions: (1) table size measured in the number of rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 244.60699999999997, "r_x1": 286.365, "r_y1": 244.60699999999997, "r_x2": 286.365, "r_y2": 236.05499999999995, "r_x3": 50.112, "r_y3": 236.05499999999995, "coord_origin": "TOPLEFT"}, "text": "and columns, (2) complexity of the table, (3) strictness of", "orig": "and columns, (2) complexity of the table, (3) strictness of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 256.562, "r_x1": 286.365, "r_y1": 256.562, "r_x2": 286.365, "r_y2": 248.01, "r_x3": 50.112, "r_y3": 248.01, "coord_origin": "TOPLEFT"}, "text": "the provided HTML structure and (4) completeness (i.e. no", "orig": "the provided HTML structure and (4) completeness (i.e. no", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 268.51700000000005, "r_x1": 286.365, "r_y1": 268.51700000000005, "r_x2": 286.365, "r_y2": 259.96500000000003, "r_x3": 50.112, "r_y3": 259.96500000000003, "coord_origin": "TOPLEFT"}, "text": "omitted bounding boxes). A table is considered to be simple", "orig": "omitted bounding boxes). A table is considered to be simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 280.472, "r_x1": 286.365, "r_y1": 280.472, "r_x2": 286.365, "r_y2": 271.91999999999996, "r_x3": 50.112, "r_y3": 271.91999999999996, "coord_origin": "TOPLEFT"}, "text": "if it does not contain row spans or column spans. Addition-", "orig": "if it does not contain row spans or column spans. Addition-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 292.428, "r_x1": 286.365, "r_y1": 292.428, "r_x2": 286.365, "r_y2": 283.876, "r_x3": 50.112, "r_y3": 283.876, "coord_origin": "TOPLEFT"}, "text": "ally, a table has a strict HTML structure if every row has the", "orig": "ally, a table has a strict HTML structure if every row has the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 304.383, "r_x1": 286.365, "r_y1": 304.383, "r_x2": 286.365, "r_y2": 295.831, "r_x3": 50.112, "r_y3": 295.831, "coord_origin": "TOPLEFT"}, "text": "same number of columns after taking into account any row", "orig": "same number of columns after taking into account any row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 316.338, "r_x1": 118.834, "r_y1": 316.338, "r_x2": 118.834, "r_y2": 307.786, "r_x3": 50.112, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "or column spans.", "orig": "or column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.307, "r_y0": 316.338, "r_x1": 286.365, "r_y1": 316.338, "r_x2": 286.365, "r_y2": 307.786, "r_x3": 123.307, "r_y3": 307.786, "coord_origin": "TOPLEFT"}, "text": "Therefore a strict HTML structure looks", "orig": "Therefore a strict HTML structure looks", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 328.293, "r_x1": 127.163, "r_y1": 328.293, "r_x2": 127.163, "r_y2": 319.741, "r_x3": 50.112, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "always rectangular.", "orig": "always rectangular.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 131.257, "r_y0": 328.293, "r_x1": 286.365, "r_y1": 328.293, "r_x2": 286.365, "r_y2": 319.741, "r_x3": 131.257, "r_y3": 319.741, "coord_origin": "TOPLEFT"}, "text": "However, HTML is a lenient encoding", "orig": "However, HTML is a lenient encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 340.248, "r_x1": 95.163, "r_y1": 340.248, "r_x2": 95.163, "r_y2": 331.696, "r_x3": 50.112, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "format, i.e.", "orig": "format, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 101.459, "r_y0": 340.248, "r_x1": 286.365, "r_y1": 340.248, "r_x2": 286.365, "r_y2": 331.696, "r_x3": 101.459, "r_y3": 331.696, "coord_origin": "TOPLEFT"}, "text": "tables with rows of different sizes might still", "orig": "tables with rows of different sizes might still", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 352.204, "r_x1": 257.942, "r_y1": 352.204, "r_x2": 257.942, "r_y2": 343.652, "r_x3": 50.112, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "be regarded as correct due to implicit display rules.", "orig": "be regarded as correct due to implicit display rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.574, "r_y0": 352.204, "r_x1": 286.365, "r_y1": 352.204, "r_x2": 286.365, "r_y2": 343.652, "r_x3": 262.574, "r_y3": 343.652, "coord_origin": "TOPLEFT"}, "text": "These", "orig": "These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 364.159, "r_x1": 286.365, "r_y1": 364.159, "r_x2": 286.365, "r_y2": 355.607, "r_x3": 50.112, "r_y3": 355.607, "coord_origin": "TOPLEFT"}, "text": "implicit rules leave room for ambiguity, which we want to", "orig": "implicit rules leave room for ambiguity, which we want to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 376.114, "r_x1": 286.365, "r_y1": 376.114, "r_x2": 286.365, "r_y2": 367.562, "r_x3": 50.112, "r_y3": 367.562, "coord_origin": "TOPLEFT"}, "text": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "orig": "avoid. As such, we prefer to have 'strict' tables, i.e. tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 388.069, "r_x1": 230.804, "r_y1": 388.069, "r_x2": 230.804, "r_y2": 379.517, "r_x3": 50.112, "r_y3": 379.517, "coord_origin": "TOPLEFT"}, "text": "where every row has exactly the same length.", "orig": "where every row has exactly the same length.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have 'strict' tables, i.e. tables where every row has exactly the same length."}, {"label": "text", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "text", "bbox": {"l": 50.112, "t": 391.674, "r": 286.365, "b": 627.374, "coord_origin": "TOPLEFT"}, "confidence": 0.9826022386550903, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 400.226, "r_x1": 236.243, "r_y1": 400.226, "r_x2": 236.243, "r_y2": 391.674, "r_x3": 62.067, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "We have developed a technique that tries", "orig": "We have developed a technique that tries", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.726, "r_y0": 400.226, "r_x1": 248.477, "r_y1": 400.226, "r_x2": 248.477, "r_y2": 391.674, "r_x3": 240.726, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.96, "r_y0": 400.226, "r_x1": 277.458, "r_y1": 400.226, "r_x2": 277.458, "r_y2": 391.674, "r_x3": 252.96, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "derive", "orig": "derive", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.942, "r_y0": 400.226, "r_x1": 286.365, "r_y1": 400.226, "r_x2": 286.365, "r_y2": 391.674, "r_x3": 281.942, "r_y3": 391.674, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 412.181, "r_x1": 223.571, "r_y1": 412.181, "r_x2": 223.571, "r_y2": 403.629, "r_x3": 50.112, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "missing bounding box out of its neighbors.", "orig": "missing bounding box out of its neighbors.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 228.004, "r_y0": 412.181, "r_x1": 286.365, "r_y1": 412.181, "r_x2": 286.365, "r_y2": 403.629, "r_x3": 228.004, "r_y3": 403.629, "coord_origin": "TOPLEFT"}, "text": "As a first step,", "orig": "As a first step,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 424.136, "r_x1": 286.365, "r_y1": 424.136, "r_x2": 286.365, "r_y2": 415.584, "r_x3": 50.112, "r_y3": 415.584, "coord_origin": "TOPLEFT"}, "text": "we use the annotation data to generate the most fine-grained", "orig": "we use the annotation data to generate the most fine-grained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 436.092, "r_x1": 189.519, "r_y1": 436.092, "r_x2": 189.519, "r_y2": 427.54, "r_x3": 50.112, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "grid that covers the table structure.", "orig": "grid that covers the table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 193.414, "r_y0": 436.092, "r_x1": 286.365, "r_y1": 436.092, "r_x2": 286.365, "r_y2": 427.54, "r_x3": 193.414, "r_y3": 427.54, "coord_origin": "TOPLEFT"}, "text": "In case of strict HTML", "orig": "In case of strict HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 448.047, "r_x1": 286.365, "r_y1": 448.047, "r_x2": 286.365, "r_y2": 439.495, "r_x3": 50.112, "r_y3": 439.495, "coord_origin": "TOPLEFT"}, "text": "tables, all grid squares are associated with some table cell", "orig": "tables, all grid squares are associated with some table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 460.002, "r_x1": 286.365, "r_y1": 460.002, "r_x2": 286.365, "r_y2": 451.45, "r_x3": 50.112, "r_y3": 451.45, "coord_origin": "TOPLEFT"}, "text": "and in the presence of table spans a cell extends across mul-", "orig": "and in the presence of table spans a cell extends across mul-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 471.957, "r_x1": 286.365, "r_y1": 471.957, "r_x2": 286.365, "r_y2": 463.405, "r_x3": 50.112, "r_y3": 463.405, "coord_origin": "TOPLEFT"}, "text": "tiple grid squares. When enough bounding boxes are known", "orig": "tiple grid squares. When enough bounding boxes are known", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 483.912, "r_x1": 143.76, "r_y1": 483.912, "r_x2": 143.76, "r_y2": 475.36, "r_x3": 50.112, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "for a rectangular table,", "orig": "for a rectangular table,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.696, "r_y0": 483.912, "r_x1": 153.235, "r_y1": 483.912, "r_x2": 153.235, "r_y2": 475.36, "r_x3": 147.696, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.881, "r_y0": 483.912, "r_x1": 163.526, "r_y1": 483.912, "r_x2": 163.526, "r_y2": 475.36, "r_x3": 156.881, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.173, "r_y0": 483.912, "r_x1": 286.365, "r_y1": 483.912, "r_x2": 286.365, "r_y2": 475.36, "r_x3": 167.173, "r_y3": 475.36, "coord_origin": "TOPLEFT"}, "text": "possible to compute the geo-", "orig": "possible to compute the geo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 495.867, "r_x1": 286.365, "r_y1": 495.867, "r_x2": 286.365, "r_y2": 487.315, "r_x3": 50.112, "r_y3": 487.315, "coord_origin": "TOPLEFT"}, "text": "metrical border lines between the grid rows and columns.", "orig": "metrical border lines between the grid rows and columns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 507.823, "r_x1": 286.365, "r_y1": 507.823, "r_x2": 286.365, "r_y2": 499.271, "r_x3": 50.112, "r_y3": 499.271, "coord_origin": "TOPLEFT"}, "text": "Eventually this information is used to generate the missing", "orig": "Eventually this information is used to generate the missing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 519.778, "r_x1": 286.365, "r_y1": 519.778, "r_x2": 286.365, "r_y2": 511.226, "r_x3": 50.112, "r_y3": 511.226, "coord_origin": "TOPLEFT"}, "text": "bounding boxes. Additionally, the existence of unused grid", "orig": "bounding boxes. Additionally, the existence of unused grid", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 531.733, "r_x1": 286.365, "r_y1": 531.733, "r_x2": 286.365, "r_y2": 523.181, "r_x3": 50.112, "r_y3": 523.181, "coord_origin": "TOPLEFT"}, "text": "squares indicates that the table rows have unequal number", "orig": "squares indicates that the table rows have unequal number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 543.688, "r_x1": 286.365, "r_y1": 543.688, "r_x2": 286.365, "r_y2": 535.136, "r_x3": 50.112, "r_y3": 535.136, "coord_origin": "TOPLEFT"}, "text": "of columns and the overall structure is non-strict. The gen-", "orig": "of columns and the overall structure is non-strict. The gen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 555.643, "r_x1": 286.365, "r_y1": 555.643, "r_x2": 286.365, "r_y2": 547.091, "r_x3": 50.112, "r_y3": 547.091, "coord_origin": "TOPLEFT"}, "text": "eration of missing bounding boxes for non-strict HTML ta-", "orig": "eration of missing bounding boxes for non-strict HTML ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 567.5989999999999, "r_x1": 257.474, "r_y1": 567.5989999999999, "r_x2": 257.474, "r_y2": 559.047, "r_x3": 50.112, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "bles is ambiguous and therefore quite challenging.", "orig": "bles is ambiguous and therefore quite challenging.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.949, "r_y0": 567.5989999999999, "r_x1": 286.365, "r_y1": 567.5989999999999, "r_x2": 286.365, "r_y2": 559.047, "r_x3": 263.949, "r_y3": 559.047, "coord_origin": "TOPLEFT"}, "text": "Thus,", "orig": "Thus,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 579.554, "r_x1": 242.251, "r_y1": 579.554, "r_x2": 242.251, "r_y2": 571.002, "r_x3": 50.112, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "we have decided to simply discard those tables.", "orig": "we have decided to simply discard those tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 246.714, "r_y0": 579.554, "r_x1": 286.365, "r_y1": 579.554, "r_x2": 286.365, "r_y2": 571.002, "r_x3": 246.714, "r_y3": 571.002, "coord_origin": "TOPLEFT"}, "text": "In case of", "orig": "In case of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 591.509, "r_x1": 286.365, "r_y1": 591.509, "r_x2": 286.365, "r_y2": 582.957, "r_x3": 50.112, "r_y3": 582.957, "coord_origin": "TOPLEFT"}, "text": "PubTabNet we have computed missing bounding boxes for", "orig": "PubTabNet we have computed missing bounding boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 603.4639999999999, "r_x1": 286.365, "r_y1": 603.4639999999999, "r_x2": 286.365, "r_y2": 594.912, "r_x3": 50.112, "r_y3": 594.912, "coord_origin": "TOPLEFT"}, "text": "48% of the simple and 69% of the complex tables. Regard-", "orig": "48% of the simple and 69% of the complex tables. Regard-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 615.419, "r_x1": 286.365, "r_y1": 615.419, "r_x2": 286.365, "r_y2": 606.867, "r_x3": 50.112, "r_y3": 606.867, "coord_origin": "TOPLEFT"}, "text": "ing FinTabNet, 68% of the simple and 98% of the complex", "orig": "ing FinTabNet, 68% of the simple and 98% of the complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 627.374, "r_x1": 242.261, "r_y1": 627.374, "r_x2": 242.261, "r_y2": 618.822, "r_x3": 50.112, "r_y3": 618.822, "coord_origin": "TOPLEFT"}, "text": "tables require the generation of bounding boxes.", "orig": "tables require the generation of bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."}, {"label": "text", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "text", "bbox": {"l": 50.112, "t": 630.979, "r": 286.365, "b": 651.487, "coord_origin": "TOPLEFT"}, "confidence": 0.9335853457450867, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 639.531, "r_x1": 286.365, "r_y1": 639.531, "r_x2": 286.365, "r_y2": 630.979, "r_x3": 62.067, "r_y3": 630.979, "coord_origin": "TOPLEFT"}, "text": "Figure 7 illustrates the distribution of the tables across", "orig": "Figure 7 illustrates the distribution of the tables across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 651.487, "r_x1": 179.905, "r_y1": 651.487, "r_x2": 179.905, "r_y2": 642.935, "r_x3": 50.112, "r_y3": 642.935, "coord_origin": "TOPLEFT"}, "text": "different dimensions per dataset.", "orig": "different dimensions per dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7 illustrates the distribution of the tables across different dimensions per dataset."}, {"label": "section_header", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "section_header", "bbox": {"l": 50.112, "t": 662.39, "r": 153.608, "b": 672.242, "coord_origin": "TOPLEFT"}, "confidence": 0.9515743255615234, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 672.242, "r_x1": 153.608, "r_y1": 672.242, "r_x2": 153.608, "r_y2": 662.39, "r_x3": 50.112, "r_y3": 662.39, "coord_origin": "TOPLEFT"}, "text": "1.2. Synthetic datasets", "orig": "1.2. Synthetic datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2. Synthetic datasets"}, {"label": "text", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "text", "bbox": {"l": 50.112, "t": 681.6, "r": 286.365, "b": 714.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9783332347869873, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 690.152, "r_x1": 286.365, "r_y1": 690.152, "r_x2": 286.365, "r_y2": 681.6, "r_x3": 62.067, "r_y3": 681.6, "coord_origin": "TOPLEFT"}, "text": "Aiming to train and evaluate our models in a broader", "orig": "Aiming to train and evaluate our models in a broader", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 702.107, "r_x1": 286.365, "r_y1": 702.107, "r_x2": 286.365, "r_y2": 693.5550000000001, "r_x3": 50.112, "r_y3": 693.5550000000001, "coord_origin": "TOPLEFT"}, "text": "spectrum of table data we have synthesized four types of", "orig": "spectrum of table data we have synthesized four types of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 714.062, "r_x1": 84.144, "r_y1": 714.062, "r_x2": 84.144, "r_y2": 705.51, "r_x3": 50.112, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "datasets.", "orig": "datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.238, "r_y0": 714.062, "r_x1": 286.365, "r_y1": 714.062, "r_x2": 286.365, "r_y2": 705.51, "r_x3": 91.238, "r_y3": 705.51, "coord_origin": "TOPLEFT"}, "text": "Each one contains tables with different appear-", "orig": "Each one contains tables with different appear-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-"}, {"label": "text", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "text", "bbox": {"l": 308.862, "t": 162.92399999999998, "r": 545.115, "b": 207.34199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9596064686775208, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 171.476, "r_x1": 426.929, "r_y1": 171.476, "r_x2": 426.929, "r_y2": 162.92399999999998, "r_x3": 308.862, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ances in regard to their size,", "orig": "ances in regard to their size,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.043, "r_y0": 171.476, "r_x1": 468.393, "r_y1": 171.476, "r_x2": 468.393, "r_y2": 162.92399999999998, "r_x3": 431.043, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "structure,", "orig": "structure,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.508, "r_y0": 171.476, "r_x1": 491.327, "r_y1": 171.476, "r_x2": 491.327, "r_y2": 162.92399999999998, "r_x3": 472.508, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "style", "orig": "style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 495.113, "r_y0": 171.476, "r_x1": 545.115, "r_y1": 171.476, "r_x2": 545.115, "r_y2": 162.92399999999998, "r_x3": 495.113, "r_y3": 162.92399999999998, "coord_origin": "TOPLEFT"}, "text": "and content.", "orig": "and content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 183.43100000000004, "r_x1": 545.115, "r_y1": 183.43100000000004, "r_x2": 545.115, "r_y2": 174.87900000000002, "r_x3": 308.862, "r_y3": 174.87900000000002, "coord_origin": "TOPLEFT"}, "text": "Every synthetic dataset contains 150k examples, summing", "orig": "Every synthetic dataset contains 150k examples, summing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 195.38599999999997, "r_x1": 545.115, "r_y1": 195.38599999999997, "r_x2": 545.115, "r_y2": 186.83399999999995, "r_x3": 308.862, "r_y3": 186.83399999999995, "coord_origin": "TOPLEFT"}, "text": "up to 600k synthetic examples. All datasets are divided into", "orig": "up to 600k synthetic examples. All datasets are divided into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 207.34199999999998, "r_x1": 484.074, "r_y1": 207.34199999999998, "r_x2": 484.074, "r_y2": 198.78999999999996, "r_x3": 308.862, "r_y3": 198.78999999999996, "coord_origin": "TOPLEFT"}, "text": "Train, Test and Val splits (80%, 10%, 10%).", "orig": "Train, Test and Val splits (80%, 10%, 10%).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%)."}, {"label": "text", "id": 15, "page_no": 10, "cluster": {"id": 15, "label": "text", "bbox": {"l": 308.862, "t": 211.50400000000002, "r": 545.115, "b": 232.01099999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9261796474456787, "cells": [{"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 220.05600000000004, "r_x1": 545.115, "r_y1": 220.05600000000004, "r_x2": 545.115, "r_y2": 211.50400000000002, "r_x3": 320.817, "r_y3": 211.50400000000002, "coord_origin": "TOPLEFT"}, "text": "The process of generating a synthetic dataset can be de-", "orig": "The process of generating a synthetic dataset can be de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 232.01099999999997, "r_x1": 448.089, "r_y1": 232.01099999999997, "r_x2": 448.089, "r_y2": 223.45899999999995, "r_x3": 308.862, "r_y3": 223.45899999999995, "coord_origin": "TOPLEFT"}, "text": "composed into the following steps:", "orig": "composed into the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The process of generating a synthetic dataset can be decomposed into the following steps:"}, {"label": "list_item", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 308.862, "t": 236.17399999999998, "r": 545.115, "b": 316.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9642952680587769, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 244.726, "r_x1": 328.289, "r_y1": 244.726, "r_x2": 328.289, "r_y2": 236.17399999999998, "r_x3": 320.817, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.382, "r_y0": 244.726, "r_x1": 492.911, "r_y1": 244.726, "r_x2": 492.911, "r_y2": 236.17399999999998, "r_x3": 335.382, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "Prepare styling and content templates:", "orig": "Prepare styling and content templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.669, "r_y0": 244.726, "r_x1": 545.115, "r_y1": 244.726, "r_x2": 545.115, "r_y2": 236.17399999999998, "r_x3": 498.669, "r_y3": 236.17399999999998, "coord_origin": "TOPLEFT"}, "text": "The styling", "orig": "The styling", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 256.68100000000004, "r_x1": 545.115, "r_y1": 256.68100000000004, "r_x2": 545.115, "r_y2": 248.12900000000002, "r_x3": 308.862, "r_y3": 248.12900000000002, "coord_origin": "TOPLEFT"}, "text": "templates have been manually designed and organized into", "orig": "templates have been manually designed and organized into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 268.63599999999997, "r_x1": 481.733, "r_y1": 268.63599999999997, "r_x2": 481.733, "r_y2": 260.08399999999995, "r_x3": 308.862, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "groups of scope specific appearances (e.g.", "orig": "groups of scope specific appearances (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.119, "r_y0": 268.63599999999997, "r_x1": 545.115, "r_y1": 268.63599999999997, "r_x2": 545.115, "r_y2": 260.08399999999995, "r_x3": 488.119, "r_y3": 260.08399999999995, "coord_origin": "TOPLEFT"}, "text": "financial data,", "orig": "financial data,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 280.591, "r_x1": 393.385, "r_y1": 280.591, "r_x2": 393.385, "r_y2": 272.039, "r_x3": 308.862, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "marketing data, etc.)", "orig": "marketing data, etc.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.119, "r_y0": 280.591, "r_x1": 545.115, "r_y1": 280.591, "r_x2": 545.115, "r_y2": 272.039, "r_x3": 400.119, "r_y3": 272.039, "coord_origin": "TOPLEFT"}, "text": "Additionally, we have prepared cu-", "orig": "Additionally, we have prepared cu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 292.546, "r_x1": 545.115, "r_y1": 292.546, "r_x2": 545.115, "r_y2": 283.994, "r_x3": 308.862, "r_y3": 283.994, "coord_origin": "TOPLEFT"}, "text": "rated collections of content templates by extracting the most", "orig": "rated collections of content templates by extracting the most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.502, "r_x1": 349.808, "r_y1": 304.502, "r_x2": 349.808, "r_y2": 295.95, "r_x3": 308.862, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "frequently", "orig": "frequently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.152, "r_y0": 304.502, "r_x1": 487.183, "r_y1": 304.502, "r_x2": 487.183, "r_y2": 295.95, "r_x3": 354.152, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "used terms out of non-synthetic", "orig": "used terms out of non-synthetic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 491.526, "r_y0": 304.502, "r_x1": 523.068, "r_y1": 304.502, "r_x2": 523.068, "r_y2": 295.95, "r_x3": 491.526, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "datasets", "orig": "datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 527.412, "r_y0": 304.502, "r_x1": 545.115, "r_y1": 304.502, "r_x2": 545.115, "r_y2": 295.95, "r_x3": 527.412, "r_y3": 295.95, "coord_origin": "TOPLEFT"}, "text": "(e.g.", "orig": "(e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.457, "r_x1": 425.693, "r_y1": 316.457, "r_x2": 425.693, "r_y2": 307.905, "r_x3": 308.862, "r_y3": 307.905, "coord_origin": "TOPLEFT"}, "text": "PubTabNet, FinTabNet, etc.).", "orig": "PubTabNet, FinTabNet, etc.).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.)."}, {"label": "list_item", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 320.619, "r": 545.115, "b": 448.723, "coord_origin": "TOPLEFT"}, "confidence": 0.9699994921684265, "cells": [{"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 329.171, "r_x1": 328.289, "r_y1": 329.171, "r_x2": 328.289, "r_y2": 320.619, "r_x3": 320.817, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.692, "r_y0": 329.171, "r_x1": 435.377, "r_y1": 329.171, "r_x2": 435.377, "r_y2": 320.619, "r_x3": 332.692, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "Generate table structures:", "orig": "Generate table structures:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.342, "r_y0": 329.171, "r_x1": 545.115, "r_y1": 329.171, "r_x2": 545.115, "r_y2": 320.619, "r_x3": 439.342, "r_y3": 320.619, "coord_origin": "TOPLEFT"}, "text": "The structure of each syn-", "orig": "The structure of each syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 341.126, "r_x1": 545.115, "r_y1": 341.126, "r_x2": 545.115, "r_y2": 332.574, "r_x3": 308.862, "r_y3": 332.574, "coord_origin": "TOPLEFT"}, "text": "thetic dataset assumes a horizontal table header which po-", "orig": "thetic dataset assumes a horizontal table header which po-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 353.081, "r_x1": 341.519, "r_y1": 353.081, "r_x2": 341.519, "r_y2": 344.529, "r_x3": 308.862, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "tentially", "orig": "tentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.813, "r_y0": 353.081, "r_x1": 427.168, "r_y1": 353.081, "r_x2": 427.168, "r_y2": 344.529, "r_x3": 345.813, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "spans over multiple", "orig": "spans over multiple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 431.462, "r_y0": 353.081, "r_x1": 545.115, "r_y1": 353.081, "r_x2": 545.115, "r_y2": 344.529, "r_x3": 431.462, "r_y3": 344.529, "coord_origin": "TOPLEFT"}, "text": "rows and a table body that", "orig": "rows and a table body that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 365.037, "r_x1": 545.115, "r_y1": 365.037, "r_x2": 545.115, "r_y2": 356.485, "r_x3": 308.862, "r_y3": 356.485, "coord_origin": "TOPLEFT"}, "text": "may contain a combination of row spans and column spans.", "orig": "may contain a combination of row spans and column spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 376.992, "r_x1": 545.115, "r_y1": 376.992, "r_x2": 545.115, "r_y2": 368.44, "r_x3": 308.862, "r_y3": 368.44, "coord_origin": "TOPLEFT"}, "text": "However, spans are not allowed to cross the header - body", "orig": "However, spans are not allowed to cross the header - body", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 388.947, "r_x1": 348.334, "r_y1": 388.947, "r_x2": 348.334, "r_y2": 380.395, "r_x3": 308.862, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "boundary.", "orig": "boundary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 353.504, "r_y0": 388.947, "r_x1": 545.115, "r_y1": 388.947, "r_x2": 545.115, "r_y2": 380.395, "r_x3": 353.504, "r_y3": 380.395, "coord_origin": "TOPLEFT"}, "text": "The table structure is described by the parame-", "orig": "The table structure is described by the parame-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 400.902, "r_x1": 326.018, "r_y1": 400.902, "r_x2": 326.018, "r_y2": 392.35, "r_x3": 308.862, "r_y3": 392.35, "coord_origin": "TOPLEFT"}, "text": "ters:", "orig": "ters:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.909, "r_y0": 400.902, "r_x1": 545.115, "r_y1": 400.902, "r_x2": 545.115, "r_y2": 392.35, "r_x3": 330.909, "r_y3": 392.35, "coord_origin": "TOPLEFT"}, "text": "Total number of table rows and columns, number of", "orig": "Total number of table rows and columns, number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.857, "r_x1": 545.115, "r_y1": 412.857, "r_x2": 545.115, "r_y2": 404.305, "r_x3": 308.862, "r_y3": 404.305, "coord_origin": "TOPLEFT"}, "text": "header rows, type of spans (header only spans, row only", "orig": "header rows, type of spans (header only spans, row only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.812, "r_x1": 333.49, "r_y1": 424.812, "r_x2": 333.49, "r_y2": 416.26, "r_x3": 308.862, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "spans,", "orig": "spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.082, "r_y0": 424.812, "r_x1": 418.66, "r_y1": 424.812, "r_x2": 418.66, "r_y2": 416.26, "r_x3": 338.082, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "column only spans,", "orig": "column only spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 423.253, "r_y0": 424.812, "r_x1": 545.115, "r_y1": 424.812, "r_x2": 545.115, "r_y2": 416.26, "r_x3": 423.253, "r_y3": 416.26, "coord_origin": "TOPLEFT"}, "text": "both row and column spans),", "orig": "both row and column spans),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 436.768, "r_x1": 545.115, "r_y1": 436.768, "r_x2": 545.115, "r_y2": 428.216, "r_x3": 308.862, "r_y3": 428.216, "coord_origin": "TOPLEFT"}, "text": "maximum span size and the ratio of the table area covered", "orig": "maximum span size and the ratio of the table area covered", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 448.723, "r_x1": 345.943, "r_y1": 448.723, "r_x2": 345.943, "r_y2": 440.171, "r_x3": 308.862, "r_y3": 440.171, "coord_origin": "TOPLEFT"}, "text": "by spans.", "orig": "by spans.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans."}, {"label": "list_item", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 452.706, "r": 545.115, "b": 497.303, "coord_origin": "TOPLEFT"}, "confidence": 0.9568928480148315, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 461.437, "r_x1": 485.758, "r_y1": 461.437, "r_x2": 485.758, "r_y2": 452.885, "r_x3": 320.817, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": "3. Generate content: Based on the dataset", "orig": "3. Generate content: Based on the dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.073, "r_y0": 461.294, "r_x1": 511.864, "r_y1": 461.294, "r_x2": 511.864, "r_y2": 452.706, "r_x3": 488.073, "r_y3": 452.706, "coord_origin": "TOPLEFT"}, "text": "theme", "orig": "theme", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 511.863, "r_y0": 461.437, "r_x1": 545.108, "r_y1": 461.437, "r_x2": 545.108, "r_y2": 452.885, "r_x3": 511.863, "r_y3": 452.885, "coord_origin": "TOPLEFT"}, "text": ", a set of", "orig": ", a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 473.392, "r_x1": 545.115, "r_y1": 473.392, "r_x2": 545.115, "r_y2": 464.84, "r_x3": 308.862, "r_y3": 464.84, "coord_origin": "TOPLEFT"}, "text": "suitable content templates is chosen first. Then, this content", "orig": "suitable content templates is chosen first. Then, this content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 485.348, "r_x1": 545.115, "r_y1": 485.348, "r_x2": 545.115, "r_y2": 476.796, "r_x3": 308.862, "r_y3": 476.796, "coord_origin": "TOPLEFT"}, "text": "can be combined with purely random text to produce the", "orig": "can be combined with purely random text to produce the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 497.303, "r_x1": 379.148, "r_y1": 497.303, "r_x2": 379.148, "r_y2": 488.751, "r_x3": 308.862, "r_y3": 488.751, "coord_origin": "TOPLEFT"}, "text": "synthetic content.", "orig": "synthetic content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content."}, {"label": "list_item", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 308.862, "t": 501.465, "r": 545.115, "b": 545.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9710659980773926, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 510.017, "r_x1": 328.289, "r_y1": 510.017, "r_x2": 328.289, "r_y2": 501.465, "r_x3": 320.817, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.217, "r_y0": 510.017, "r_x1": 434.082, "r_y1": 510.017, "r_x2": 434.082, "r_y2": 501.465, "r_x3": 334.217, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Apply styling templates:", "orig": "Apply styling templates:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.063, "r_y0": 510.017, "r_x1": 545.115, "r_y1": 510.017, "r_x2": 545.115, "r_y2": 501.465, "r_x3": 439.063, "r_y3": 501.465, "coord_origin": "TOPLEFT"}, "text": "Depending on the domain", "orig": "Depending on the domain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 521.972, "r_x1": 406.884, "r_y1": 521.972, "r_x2": 406.884, "r_y2": 513.4200000000001, "r_x3": 308.862, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "of the synthetic dataset,", "orig": "of the synthetic dataset,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.019, "r_y0": 521.972, "r_x1": 545.115, "r_y1": 521.972, "r_x2": 545.115, "r_y2": 513.4200000000001, "r_x3": 411.019, "r_y3": 513.4200000000001, "coord_origin": "TOPLEFT"}, "text": "a set of styling templates is first", "orig": "a set of styling templates is first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 533.928, "r_x1": 384.299, "r_y1": 533.928, "r_x2": 384.299, "r_y2": 525.376, "r_x3": 308.862, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "manually selected.", "orig": "manually selected.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.253, "r_y0": 533.928, "r_x1": 545.115, "r_y1": 533.928, "r_x2": 545.115, "r_y2": 525.376, "r_x3": 391.253, "r_y3": 525.376, "coord_origin": "TOPLEFT"}, "text": "Then, a style is randomly selected to", "orig": "Then, a style is randomly selected to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 545.883, "r_x1": 496.159, "r_y1": 545.883, "r_x2": 496.159, "r_y2": 537.331, "r_x3": 308.862, "r_y3": 537.331, "coord_origin": "TOPLEFT"}, "text": "format the appearance of the synthesized table.", "orig": "format the appearance of the synthesized table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table."}, {"label": "list_item", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 308.862, "t": 550.045, "r": 545.115, "b": 606.418, "coord_origin": "TOPLEFT"}, "confidence": 0.9778757095336914, "cells": [{"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 558.597, "r_x1": 328.289, "r_y1": 558.597, "r_x2": 328.289, "r_y2": 550.045, "r_x3": 320.817, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.402, "r_y0": 558.597, "r_x1": 450.371, "r_y1": 558.597, "r_x2": 450.371, "r_y2": 550.045, "r_x3": 335.402, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "Render the complete tables:", "orig": "Render the complete tables:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 456.139, "r_y0": 558.597, "r_x1": 545.115, "r_y1": 558.597, "r_x2": 545.115, "r_y2": 550.045, "r_x3": 456.139, "r_y3": 550.045, "coord_origin": "TOPLEFT"}, "text": "The synthetic table is", "orig": "The synthetic table is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 570.552, "r_x1": 334.326, "r_y1": 570.552, "r_x2": 334.326, "r_y2": 562.0, "r_x3": 308.862, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "finally", "orig": "finally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.983, "r_y0": 570.552, "r_x1": 545.115, "r_y1": 570.552, "r_x2": 545.115, "r_y2": 562.0, "r_x3": 337.983, "r_y3": 562.0, "coord_origin": "TOPLEFT"}, "text": "rendered by a web browser engine to generate the", "orig": "rendered by a web browser engine to generate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 582.5070000000001, "r_x1": 545.115, "r_y1": 582.5070000000001, "r_x2": 545.115, "r_y2": 573.955, "r_x3": 308.862, "r_y3": 573.955, "coord_origin": "TOPLEFT"}, "text": "bounding boxes for each table cell. A batching technique is", "orig": "bounding boxes for each table cell. A batching technique is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 594.463, "r_x1": 545.115, "r_y1": 594.463, "r_x2": 545.115, "r_y2": 585.9110000000001, "r_x3": 308.862, "r_y3": 585.9110000000001, "coord_origin": "TOPLEFT"}, "text": "utilized to optimize the runtime overhead of the rendering", "orig": "utilized to optimize the runtime overhead of the rendering", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 606.418, "r_x1": 341.23, "r_y1": 606.418, "r_x2": 341.23, "r_y2": 597.866, "r_x3": 308.862, "r_y3": 597.866, "coord_origin": "TOPLEFT"}, "text": "process.", "orig": "process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. 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"r_y1": 323.889, "r_x2": 286.365, "r_y2": 315.337, "r_x3": 128.348, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "PDF documents can be seen as a se-", "orig": "PDF documents can be seen as a se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.845, "r_x1": 286.365, "r_y1": 335.845, "r_x2": 286.365, "r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 516.431, "r_x1": 69.539, "r_y1": 516.431, "r_x2": 69.539, "r_y2": 507.879, "r_x3": 62.067, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.248, "r_y0": 516.431, "r_x1": 286.365, "r_y1": 516.431, "r_x2": 286.365, "r_y2": 507.879, "r_x3": 75.248, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "Use a carefully selected IOU threshold to designate", "orig": "Use a carefully selected IOU threshold to designate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 528.387, "r_x1": 226.071, "r_y1": 528.387, "r_x2": 226.071, "r_y2": 519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. The alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 600.7470000000001, "r_x1": 278.704, "r_y1": 600.7470000000001, "r_x2": 278.704, "r_y2": 592.1949999999999, "r_x3": 50.112, "r_y3": 592.1949999999999, "coord_origin": "TOPLEFT"}, "text": "of the column can be identified by the following formula:", "orig": "of the column can be identified by the following formula:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.028, "r_y0": 632.81, "r_x1": 157.952, "r_y1": 632.81, "r_x2": 157.952, "r_y2": 624.104, "r_x3": 112.028, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "alignment", "orig": "alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.715, 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metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.8530000000001, "r_x1": 442.221, "r_y1": 567.8530000000001, "r_x2": 442.221, "r_y2": 559.3009999999999, "r_x3": 308.862, "r_y3": 559.3009999999999, "coord_origin": "TOPLEFT"}, "text": "created to match with the orphan.", "orig": "created to match with the orphan.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 580.159, "r_x1": 332.712, "r_y1": 580.159, "r_x2": 332.712, "r_y2": 571.607, "r_x3": 320.817, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "9a.", "orig": "9a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.943, "r_y0": 580.159, "r_x1": 545.115, "r_y1": 580.159, "r_x2": 545.115, "r_y2": 571.607, "r_x3": 337.943, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "Compute the top and bottom boundary of the hori-", "orig": "Compute the top and bottom boundary of the hori-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 592.114, "r_x1": 471.641, "r_y1": 592.114, "r_x2": 471.641, "r_y2": 583.562, "r_x3": 308.862, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "zontal band for each grid row (min/max", "orig": "zontal band for each grid row (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.834, "r_y0": 591.95, "r_x1": 479.719, "r_y1": 591.95, "r_x2": 479.719, "r_y2": 583.244, "r_x3": 474.834, "r_y3": 583.244, "coord_origin": "TOPLEFT"}, "text": "y", "orig": "y", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.269, "r_y0": 592.114, "r_x1": 545.117, "r_y1": 592.114, "r_x2": 545.117, "r_y2": 583.562, "r_x3": 483.269, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "coordinates per", "orig": "coordinates per", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 604.069, "r_x1": 329.913, "r_y1": 604.069, "r_x2": 329.913, "r_y2": 595.517, "r_x3": 308.862, "r_y3": 595.517, "coord_origin": "TOPLEFT"}, "text": "row).", "orig": "row).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 616.374, "r_x1": 332.872, "r_y1": 616.374, "r_x2": 332.872, "r_y2": 607.822, "r_x3": 320.817, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "9b.", "orig": "9b.", 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"text": "However, it is possible to mitigate those limitations by", "orig": "However, it is possible to mitigate those limitations by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 299.979, "r_x1": 286.365, "r_y1": 299.979, "r_x2": 286.365, "r_y2": 291.427, "r_x3": 50.112, "r_y3": 291.427, "coord_origin": "TOPLEFT"}, "text": "combining the TableFormer predictions with the informa-", "orig": "combining the TableFormer predictions with the informa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.934, "r_x1": 286.365, "r_y1": 311.934, "r_x2": 286.365, "r_y2": 303.382, "r_x3": 50.112, "r_y3": 303.382, "coord_origin": "TOPLEFT"}, "text": "tion already present inside a programmatic PDF document.", "orig": "tion already present inside a programmatic PDF 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"r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.338, "r": 286.365, "b": 419.846, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.564, "r": 286.365, "b": 456.026, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.744, "r": 286.365, "b": 504.161, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.879, "r": 286.365, "b": 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519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 532.104, "r": 286.365, "b": 564.567, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.285, "r": 286.365, "b": 600.7470000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. The alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 600.7470000000001, "r_x1": 278.704, "r_y1": 600.7470000000001, "r_x2": 278.704, "r_y2": 592.1949999999999, "r_x3": 50.112, "r_y3": 592.1949999999999, "coord_origin": "TOPLEFT"}, "text": "of the column can be identified by the following formula:", "orig": "of the column can be identified by the following formula:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "formula", "bbox": {"l": 110.705, "t": 624.104, "r": 286.362, "b": 654.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.028, "r_y0": 632.81, "r_x1": 157.952, "r_y1": 632.81, "r_x2": 157.952, "r_y2": 624.104, "r_x3": 112.028, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "alignment", "orig": "alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.715, "r_y0": 632.81, "r_x1": 203.496, "r_y1": 632.81, "r_x2": 203.496, "r_y2": 624.104, "r_x3": 160.715, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "= arg min", "orig": "= arg min", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.585, "r_y0": 640.154, "r_x1": 189.145, "r_y1": 640.154, "r_x2": 189.145, "r_y2": 634.06, "r_x3": 185.585, "r_y3": 634.06, "coord_origin": "TOPLEFT"}, "text": "c", "orig": "c", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.499, "r_y0": 632.668, "r_x1": 208.48, "r_y1": 632.668, "r_x2": 208.48, "r_y2": 624.104, "r_x3": 203.499, "r_y3": 624.104, 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271.326, "coord_origin": "TOPLEFT"}, "text": "-coordinates and cell sizes.", "orig": "-coordinates and cell sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 283.632, "r": 545.115, "b": 387.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 292.184, "r_x1": 328.289, "r_y1": 292.184, "r_x2": 328.289, "r_y2": 283.632, "r_x3": 320.817, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "7.", "orig": "7.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.144, "r_y0": 292.184, "r_x1": 545.115, "r_y1": 292.184, "r_x2": 545.115, "r_y2": 283.632, "r_x3": 332.144, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "Generate a new set of pair-wise matches between the", "orig": "Generate a new set of pair-wise matches between the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.139, "r_x1": 476.562, "r_y1": 304.139, "r_x2": 476.562, "r_y2": 295.587, "r_x3": 308.862, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "corrected bounding boxes and PDF cells.", "orig": "corrected bounding boxes and PDF cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 482.082, "r_y0": 304.139, "r_x1": 545.115, "r_y1": 304.139, "r_x2": 545.115, "r_y2": 295.587, "r_x3": 482.082, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "This time use a", "orig": "This time use a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.094, "r_x1": 545.115, "r_y1": 316.094, "r_x2": 545.115, "r_y2": 307.542, "r_x3": 308.862, "r_y3": 307.542, "coord_origin": "TOPLEFT"}, "text": "modified version of the IOU metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "list_item", "bbox": {"l": 308.862, "t": 391.579, "r": 545.115, "b": 459.906, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "list_item", "bbox": {"l": 308.862, "t": 463.66, "r": 545.115, "b": 567.8530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.8530000000001, "r_x1": 442.221, "r_y1": 567.8530000000001, "r_x2": 442.221, "r_y2": 559.3009999999999, "r_x3": 308.862, "r_y3": 559.3009999999999, "coord_origin": "TOPLEFT"}, "text": "created to match with the orphan.", "orig": "created to match with the orphan.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 308.862, "t": 571.607, "r": 545.117, "b": 604.069, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 580.159, 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Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 211.20000000000005, "r": 286.365, "b": 231.707, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 219.75199999999995, "r_x1": 65.056, "r_y1": 219.75199999999995, "r_x2": 65.056, "r_y2": 211.20000000000005, "r_x3": 61.569, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 219.75199999999995, "r_x1": 286.365, "r_y1": 219.75199999999995, "r_x2": 286.365, "r_y2": 211.20000000000005, "r_x3": 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0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 252.89200000000005, "r_x1": 65.056, "r_y1": 252.89200000000005, "r_x2": 65.056, "r_y2": 244.34000000000003, "r_x3": 61.569, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 252.89200000000005, "r_x1": 286.365, "r_y1": 252.89200000000005, "r_x2": 286.365, "r_y2": 244.34000000000003, "r_x3": 70.037, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "There are occasional inaccuracies in the predictions of", "orig": "There are occasional inaccuracies in the predictions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 264.847, "r_x1": 150.415, "r_y1": 264.847, "r_x2": 150.415, "r_y2": 256.29499999999996, "r_x3": 70.037, "r_y3": 256.29499999999996, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes.", "orig": "the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.472, "r": 286.365, "b": 395.621, "coord_origin": "TOPLEFT"}, "confidence": 0.9767085909843445, "cells": [{"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 288.024, "r_x1": 286.365, "r_y1": 288.024, "r_x2": 286.365, "r_y2": 279.472, "r_x3": 62.067, "r_y3": 279.472, "coord_origin": "TOPLEFT"}, "text": "However, it is possible to mitigate those limitations by", "orig": "However, it is possible to mitigate those limitations by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 299.979, "r_x1": 286.365, "r_y1": 299.979, "r_x2": 286.365, "r_y2": 291.427, "r_x3": 50.112, "r_y3": 291.427, "coord_origin": "TOPLEFT"}, "text": "combining the TableFormer predictions with the informa-", "orig": "combining the TableFormer predictions with the informa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.934, "r_x1": 286.365, "r_y1": 311.934, "r_x2": 286.365, "r_y2": 303.382, "r_x3": 50.112, "r_y3": 303.382, "coord_origin": "TOPLEFT"}, "text": "tion already present inside a programmatic PDF document.", "orig": "tion already present inside a programmatic PDF document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.889, "r_x1": 123.387, "r_y1": 323.889, "r_x2": 123.387, "r_y2": 315.337, "r_x3": 50.112, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "More specifically,", "orig": "More specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.348, "r_y0": 323.889, "r_x1": 286.365, "r_y1": 323.889, "r_x2": 286.365, "r_y2": 315.337, "r_x3": 128.348, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "PDF documents can be seen as a se-", "orig": "PDF documents can be seen as a se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.845, "r_x1": 286.365, "r_y1": 335.845, "r_x2": 286.365, "r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.338, "r": 286.365, "b": 419.846, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.564, "r": 286.365, "b": 456.026, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.744, "r": 286.365, "b": 504.161, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.879, "r": 286.365, "b": 528.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 516.431, "r_x1": 69.539, "r_y1": 516.431, "r_x2": 69.539, "r_y2": 507.879, "r_x3": 62.067, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.248, "r_y0": 516.431, "r_x1": 286.365, "r_y1": 516.431, "r_x2": 286.365, "r_y2": 507.879, "r_x3": 75.248, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "Use a carefully selected IOU threshold to designate", "orig": "Use a carefully selected IOU threshold to designate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 528.387, "r_x1": 226.071, "r_y1": 528.387, "r_x2": 226.071, "r_y2": 519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 532.104, "r": 286.365, "b": 564.567, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.285, "r": 286.365, "b": 600.7470000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. The alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 600.7470000000001, "r_x1": 278.704, "r_y1": 600.7470000000001, "r_x2": 278.704, "r_y2": 592.1949999999999, "r_x3": 50.112, "r_y3": 592.1949999999999, "coord_origin": "TOPLEFT"}, "text": "of the column can be identified by the following formula:", "orig": "of the column can be identified by the following formula:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.705, "t": 624.104, "r": 286.362, "b": 654.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.028, "r_y0": 632.81, "r_x1": 157.952, "r_y1": 632.81, "r_x2": 157.952, "r_y2": 624.104, "r_x3": 112.028, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "alignment", "orig": "alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.715, "r_y0": 632.81, "r_x1": 203.496, "r_y1": 632.81, "r_x2": 203.496, "r_y2": 624.104, "r_x3": 160.715, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "= arg min", "orig": "= arg min", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.729, "r_y0": 633.734, "r_x1": 220.289, "r_y1": 633.734, "r_x2": 220.289, "r_y2": 627.64, "r_x3": 216.729, "r_y3": 627.64, "coord_origin": "TOPLEFT"}, "text": "c", "orig": "c", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.787, "r_y0": 632.668, "r_x1": 225.768, "r_y1": 632.668, "r_x2": 225.768, "r_y2": 624.104, "r_x3": 220.787, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "}", "orig": "}", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 110.705, "r_y0": 654.075, "r_x1": 118.953, "r_y1": 654.075, "r_x2": 118.953, "r_y2": 645.369, "r_x3": 110.705, "r_y3": 645.369, "coord_origin": "TOPLEFT"}, "text": "D", "orig": "D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, 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"r_x3": 243.529, "r_y3": 671.551, "coord_origin": "TOPLEFT"}, "text": "c", "orig": "c", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.473, "r_y0": 676.886, "r_x1": 258.118, "r_y1": 676.886, "r_x2": 258.118, "r_y2": 668.3340000000001, "r_x3": 251.473, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.003, "r_y0": 676.886, "r_x1": 286.362, "r_y1": 676.886, "r_x2": 286.362, "r_y2": 668.3340000000001, "r_x3": 262.003, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "the x-", "orig": "the x-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 688.841, "r_x1": 205.887, "r_y1": 688.841, "r_x2": 205.887, "r_y2": 680.289, "r_x3": 50.112, "r_y3": 680.289, "coord_origin": "TOPLEFT"}, "text": "coordinate for the corresponding point.", "orig": "coordinate for the corresponding point.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510206222534, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 701.111, "r_x1": 69.539, "r_y1": 701.111, "r_x2": 69.539, "r_y2": 692.559, "r_x3": 62.067, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.274, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 76.274, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Use the alignment computed in step 4, to compute", "orig": "Use the alignment computed in step 4, to compute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 94.605, "r_y1": 713.066, "r_x2": 94.605, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "the median", "orig": "the median", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.599, "r_y0": 712.902, "r_x1": 103.293, "r_y1": 712.902, "r_x2": 103.293, "r_y2": 704.196, "r_x3": 97.599, "r_y3": 704.196, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 103.292, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 103.292, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "-coordinate for all table columns and the me-", "orig": "-coordinate for all table columns and the me-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 211.20000000000005, "r": 545.115, "b": 255.61800000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8942298889160156, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 219.75199999999995, "r_x1": 435.915, "r_y1": 219.75199999999995, "r_x2": 435.915, "r_y2": 211.20000000000005, "r_x3": 308.862, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "dian cell size for all table cells.", "orig": "dian cell size for all table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.697, "r_y0": 219.75199999999995, "r_x1": 545.115, "r_y1": 219.75199999999995, "r_x2": 545.115, "r_y2": 211.20000000000005, "r_x3": 440.697, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "The usage of median dur-", "orig": "The usage of median dur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 231.70799999999997, "r_x1": 545.115, "r_y1": 231.70799999999997, "r_x2": 545.115, "r_y2": 223.15599999999995, "r_x3": 308.862, "r_y3": 223.15599999999995, "coord_origin": "TOPLEFT"}, "text": "ing the computations, helps to eliminate outliers caused by", "orig": "ing the computations, helps to eliminate outliers caused by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 243.663, "r_x1": 545.115, "r_y1": 243.663, "r_x2": 545.115, "r_y2": 235.111, "r_x3": 308.862, "r_y3": 235.111, "coord_origin": "TOPLEFT"}, "text": "occasional column spans which are usually wider than the", "orig": "occasional column spans which are usually wider than the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 255.61800000000005, "r_x1": 339.577, "r_y1": 255.61800000000005, "r_x2": 339.577, "r_y2": 247.06600000000003, "r_x3": 308.862, "r_y3": 247.06600000000003, "coord_origin": "TOPLEFT"}, "text": "normal.", "orig": "normal.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.371, "r": 545.115, "b": 279.87800000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9423392415046692, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 267.923, "r_x1": 328.289, "r_y1": 267.923, "r_x2": 328.289, "r_y2": 259.371, "r_x3": 320.817, "r_y3": 259.371, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.884, "r_y0": 267.923, "r_x1": 545.115, "r_y1": 267.923, "r_x2": 545.115, "r_y2": 259.371, "r_x3": 334.884, "r_y3": 259.371, "coord_origin": "TOPLEFT"}, "text": "Snap all cells with bad IOU to their corresponding", "orig": "Snap all cells with bad IOU to their corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 279.87800000000004, "r_x1": 338.192, "r_y1": 279.87800000000004, "r_x2": 338.192, "r_y2": 271.326, "r_x3": 308.862, "r_y3": 271.326, "coord_origin": "TOPLEFT"}, "text": "median", "orig": "median", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.682, "r_y0": 279.71400000000006, "r_x1": 346.376, "r_y1": 279.71400000000006, "r_x2": 346.376, "r_y2": 271.00800000000004, "r_x3": 340.682, "r_y3": 271.00800000000004, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.376, "r_y0": 279.87800000000004, "r_x1": 453.723, "r_y1": 279.87800000000004, "r_x2": 453.723, "r_y2": 271.326, "r_x3": 346.376, "r_y3": 271.326, "coord_origin": "TOPLEFT"}, "text": "-coordinates and cell sizes.", "orig": "-coordinates and cell sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 283.632, "r": 545.115, "b": 387.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 292.184, "r_x1": 328.289, "r_y1": 292.184, "r_x2": 328.289, "r_y2": 283.632, "r_x3": 320.817, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "7.", "orig": "7.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.144, "r_y0": 292.184, "r_x1": 545.115, "r_y1": 292.184, "r_x2": 545.115, "r_y2": 283.632, "r_x3": 332.144, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "Generate a new set of pair-wise matches between the", "orig": "Generate a new set of pair-wise matches between the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.139, "r_x1": 476.562, "r_y1": 304.139, "r_x2": 476.562, "r_y2": 295.587, "r_x3": 308.862, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "corrected bounding boxes and PDF cells.", "orig": "corrected bounding boxes and PDF cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 482.082, "r_y0": 304.139, "r_x1": 545.115, "r_y1": 304.139, "r_x2": 545.115, "r_y2": 295.587, "r_x3": 482.082, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "This time use a", "orig": "This time use a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.094, "r_x1": 545.115, "r_y1": 316.094, "r_x2": 545.115, "r_y2": 307.542, "r_x3": 308.862, "r_y3": 307.542, "coord_origin": "TOPLEFT"}, "text": "modified version of the IOU metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.862, "t": 391.579, "r": 545.115, "b": 459.906, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.862, "t": 463.66, "r": 545.115, "b": 567.8530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.8530000000001, "r_x1": 442.221, "r_y1": 567.8530000000001, "r_x2": 442.221, "r_y2": 559.3009999999999, "r_x3": 308.862, "r_y3": 559.3009999999999, "coord_origin": "TOPLEFT"}, "text": "created to match with the orphan.", "orig": "created to match with the orphan.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.862, "t": 571.607, "r": 545.117, "b": 604.069, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 580.159, "r_x1": 332.712, "r_y1": 580.159, "r_x2": 332.712, "r_y2": 571.607, "r_x3": 320.817, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "9a.", "orig": "9a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.943, "r_y0": 580.159, "r_x1": 545.115, "r_y1": 580.159, "r_x2": 545.115, "r_y2": 571.607, "r_x3": 337.943, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "Compute the top and bottom boundary of the hori-", "orig": "Compute the top and bottom boundary of the hori-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 592.114, "r_x1": 471.641, "r_y1": 592.114, "r_x2": 471.641, "r_y2": 583.562, "r_x3": 308.862, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "zontal band for each grid row (min/max", "orig": "zontal band for each grid row (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.834, "r_y0": 591.95, "r_x1": 479.719, "r_y1": 591.95, "r_x2": 479.719, "r_y2": 583.244, "r_x3": 474.834, "r_y3": 583.244, "coord_origin": "TOPLEFT"}, "text": "y", "orig": "y", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.269, "r_y0": 592.114, "r_x1": 545.117, "r_y1": 592.114, "r_x2": 545.117, "r_y2": 583.562, "r_x3": 483.269, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "coordinates per", "orig": "coordinates per", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 604.069, "r_x1": 329.913, "r_y1": 604.069, "r_x2": 329.913, "r_y2": 595.517, "r_x3": 308.862, "r_y3": 595.517, "coord_origin": "TOPLEFT"}, "text": "row).", "orig": "row).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.862, "t": 607.822, "r": 545.115, "b": 628.33, "coord_origin": "TOPLEFT"}, "confidence": 0.7716891169548035, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 616.374, "r_x1": 332.872, "r_y1": 616.374, "r_x2": 332.872, "r_y2": 607.822, "r_x3": 320.817, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "9b.", "orig": "9b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.925, "r_y0": 616.374, "r_x1": 374.226, "r_y1": 616.374, "r_x2": 374.226, "r_y2": 607.822, "r_x3": 339.925, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "Intersect", "orig": "Intersect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.042, "r_y0": 616.374, "r_x1": 545.115, "r_y1": 616.374, "r_x2": 545.115, "r_y2": 607.822, "r_x3": 378.042, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "the orphan's bounding box with the row", "orig": "the orphan's bounding box with the row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 628.33, "r_x1": 495.292, "r_y1": 628.33, "r_x2": 495.292, "r_y2": 619.778, "r_x3": 308.862, "r_y3": 619.778, "coord_origin": "TOPLEFT"}, "text": "bands, and map the cell to the closest grid row.", "orig": "bands, and map the cell to the closest grid row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.862, "t": 632.083, "r": 545.115, "b": 664.545, "coord_origin": "TOPLEFT"}, "confidence": 0.8584907054901123, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 640.635, "r_x1": 332.712, "r_y1": 640.635, "r_x2": 332.712, "r_y2": 632.083, "r_x3": 320.817, "r_y3": 632.083, "coord_origin": "TOPLEFT"}, "text": "9c.", "orig": "9c.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.534, "r_y0": 640.635, "r_x1": 545.115, "r_y1": 640.635, "r_x2": 545.115, "r_y2": 632.083, "r_x3": 337.534, "r_y3": 632.083, "coord_origin": "TOPLEFT"}, "text": "Compute the left and right boundary of the vertical", "orig": "Compute the left and right boundary of the vertical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.59, "r_x1": 455.282, "r_y1": 652.59, "r_x2": 455.282, "r_y2": 644.038, "r_x3": 308.862, "r_y3": 644.038, "coord_origin": "TOPLEFT"}, "text": "band for each grid column (min/max", "orig": "band for each grid column (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 457.777, "r_y0": 652.4259999999999, "r_x1": 463.471, "r_y1": 652.4259999999999, "r_x2": 463.471, "r_y2": 643.72, "r_x3": 457.777, "r_y3": 643.72, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.971, "r_y0": 652.59, "r_x1": 545.114, "r_y1": 652.59, "r_x2": 545.114, "r_y2": 644.038, "r_x3": 465.971, "r_y3": 644.038, "coord_origin": "TOPLEFT"}, "text": "coordinates per col-", "orig": "coordinates per col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.545, "r_x1": 332.384, "r_y1": 664.545, "r_x2": 332.384, "r_y2": 655.9929999999999, "r_x3": 308.862, "r_y3": 655.9929999999999, "coord_origin": "TOPLEFT"}, "text": "umn).", "orig": "umn).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.862, "t": 668.299, "r": 545.115, "b": 688.806, "coord_origin": "TOPLEFT"}, "confidence": 0.7590745091438293, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 676.851, "r_x1": 545.115, "r_y1": 676.851, "r_x2": 545.115, "r_y2": 668.299, "r_x3": 320.817, "r_y3": 668.299, "coord_origin": "TOPLEFT"}, "text": "9d. Intersect the orphan's bounding box with the column", "orig": "9d. Intersect the orphan's bounding box with the column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 688.806, "r_x1": 510.585, "r_y1": 688.806, "r_x2": 510.585, "r_y2": 680.254, "r_x3": 308.862, "r_y3": 680.254, "coord_origin": "TOPLEFT"}, "text": "bands, and map the cell to the closest grid column.", "orig": "bands, and map the cell to the closest grid column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.862, "t": 692.559, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 320.817, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "9e. If the table cell under the identified row and column", "orig": "9e. If the table cell under the identified row and column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "is not empty, extend its content with the content of the or-", "orig": "is not empty, extend its content with the content of the or-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"}, {"label": "page_footer", "id": 14, "page_no": 11, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 292.631, "t": 734.402, "r": 302.594, "b": 742.954, "coord_origin": "TOPLEFT"}, "confidence": 0.9126598238945007, "cells": [{"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.631, "r_y0": 742.954, "r_x1": 302.594, "r_y1": 742.954, "r_x2": 302.594, "r_y2": 734.402, "r_x3": 292.631, "r_y3": 734.402, "coord_origin": "TOPLEFT"}, "text": "12", "orig": "12", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "12"}], "body": [{"label": "picture", "id": 30, "page_no": 11, "cluster": {"id": 30, "label": "picture", "bbox": {"l": 53.54227066040039, "t": 74.74854278564453, "r": 544.938232421875, "b": 147.59091186523438, "coord_origin": "TOPLEFT"}, "confidence": 0.6033818125724792, "cells": 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"confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "caption", "bbox": {"l": 50.112, "t": 165.77099999999996, "r": 545.114, "b": 186.27800000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9661495685577393, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 174.32299999999998, "r_x1": 86.585, "r_y1": 174.32299999999998, "r_x2": 86.585, "r_y2": 165.77099999999996, "r_x3": 50.112, "r_y3": 165.77099999999996, "coord_origin": "TOPLEFT"}, "text": "Figure 7:", "orig": "Figure 7:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.832, "r_y0": 174.32299999999998, "r_x1": 545.114, "r_y1": 174.32299999999998, "r_x2": 545.114, "r_y2": 165.77099999999996, "r_x3": 92.832, "r_y3": 165.77099999999996, "coord_origin": "TOPLEFT"}, "text": "Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "orig": "Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 186.27800000000002, "r_x1": 513.522, "r_y1": 186.27800000000002, "r_x2": 513.522, "r_y2": 177.726, "r_x3": 50.112, "r_y3": 177.726, "coord_origin": "TOPLEFT"}, "text": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "orig": "strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 61.569, "t": 211.20000000000005, "r": 286.365, "b": 231.707, "coord_origin": "TOPLEFT"}, "confidence": 0.9621952772140503, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 219.75199999999995, "r_x1": 65.056, "r_y1": 219.75199999999995, "r_x2": 65.056, "r_y2": 211.20000000000005, "r_x3": 61.569, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 219.75199999999995, "r_x1": 286.365, "r_y1": 219.75199999999995, "r_x2": 286.365, "r_y2": 211.20000000000005, "r_x3": 70.037, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "TableFormer output does not include the table cell con-", "orig": "TableFormer output does not include the table cell con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 231.707, "r_x1": 87.472, "r_y1": 231.707, "r_x2": 87.472, "r_y2": 223.15499999999997, "r_x3": 70.037, "r_y3": 223.15499999999997, "coord_origin": "TOPLEFT"}, "text": "tent.", "orig": "tent.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 TableFormer output does not include the table cell content."}, {"label": "list_item", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 61.569, "t": 244.34000000000003, "r": 286.365, "b": 264.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9629555344581604, "cells": [{"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 61.569, "r_y0": 252.89200000000005, "r_x1": 65.056, "r_y1": 252.89200000000005, "r_x2": 65.056, "r_y2": 244.34000000000003, "r_x3": 61.569, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 252.89200000000005, "r_x1": 286.365, "r_y1": 252.89200000000005, "r_x2": 286.365, "r_y2": 244.34000000000003, "r_x3": 70.037, "r_y3": 244.34000000000003, "coord_origin": "TOPLEFT"}, "text": "There are occasional inaccuracies in the predictions of", "orig": "There are occasional inaccuracies in the predictions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.037, "r_y0": 264.847, "r_x1": 150.415, "r_y1": 264.847, "r_x2": 150.415, "r_y2": 256.29499999999996, "r_x3": 70.037, "r_y3": 256.29499999999996, "coord_origin": "TOPLEFT"}, "text": "the bounding boxes.", "orig": "the bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "\u00b7 There are occasional inaccuracies in the predictions of the bounding boxes."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 50.112, "t": 279.472, "r": 286.365, "b": 395.621, "coord_origin": "TOPLEFT"}, "confidence": 0.9767085909843445, "cells": [{"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 288.024, "r_x1": 286.365, "r_y1": 288.024, "r_x2": 286.365, "r_y2": 279.472, "r_x3": 62.067, "r_y3": 279.472, "coord_origin": "TOPLEFT"}, "text": "However, it is possible to mitigate those limitations by", "orig": "However, it is possible to mitigate those limitations by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 299.979, "r_x1": 286.365, "r_y1": 299.979, "r_x2": 286.365, "r_y2": 291.427, "r_x3": 50.112, "r_y3": 291.427, "coord_origin": "TOPLEFT"}, "text": "combining the TableFormer predictions with the informa-", "orig": "combining the TableFormer predictions with the informa-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 311.934, "r_x1": 286.365, "r_y1": 311.934, "r_x2": 286.365, "r_y2": 303.382, "r_x3": 50.112, "r_y3": 303.382, "coord_origin": "TOPLEFT"}, "text": "tion already present inside a programmatic PDF document.", "orig": "tion already present inside a programmatic PDF document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 323.889, "r_x1": 123.387, "r_y1": 323.889, "r_x2": 123.387, "r_y2": 315.337, "r_x3": 50.112, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "More specifically,", "orig": "More specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.348, "r_y0": 323.889, "r_x1": 286.365, "r_y1": 323.889, "r_x2": 286.365, "r_y2": 315.337, "r_x3": 128.348, "r_y3": 315.337, "coord_origin": "TOPLEFT"}, "text": "PDF documents can be seen as a se-", "orig": "PDF documents can be seen as a se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 335.845, "r_x1": 286.365, "r_y1": 335.845, "r_x2": 286.365, "r_y2": 327.293, "r_x3": 50.112, "r_y3": 327.293, "coord_origin": "TOPLEFT"}, "text": "quence of PDF cells where each cell is described by its con-", "orig": "quence of PDF cells where each cell is described by its con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 347.8, "r_x1": 286.365, "r_y1": 347.8, "r_x2": 286.365, "r_y2": 339.248, "r_x3": 50.112, "r_y3": 339.248, "coord_origin": "TOPLEFT"}, "text": "tent and bounding box. If we are able to associate the PDF", "orig": "tent and bounding box. If we are able to associate the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 359.755, "r_x1": 286.365, "r_y1": 359.755, "r_x2": 286.365, "r_y2": 351.203, "r_x3": 50.112, "r_y3": 351.203, "coord_origin": "TOPLEFT"}, "text": "cells with the predicted table cells, we can directly link the", "orig": "cells with the predicted table cells, we can directly link the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 371.71, "r_x1": 286.365, "r_y1": 371.71, "r_x2": 286.365, "r_y2": 363.158, "r_x3": 50.112, "r_y3": 363.158, "coord_origin": "TOPLEFT"}, "text": "PDF cell content to the table cell structure and use the PDF", "orig": "PDF cell content to the table cell structure and use the PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 383.665, "r_x1": 286.365, "r_y1": 383.665, "r_x2": 286.365, "r_y2": 375.113, "r_x3": 50.112, "r_y3": 375.113, "coord_origin": "TOPLEFT"}, "text": "bounding boxes to correct misalignments in the predicted", "orig": "bounding boxes to correct misalignments in the predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 395.621, "r_x1": 154.56, "r_y1": 395.621, "r_x2": 154.56, "r_y2": 387.069, "r_x3": 50.112, "r_y3": 387.069, "coord_origin": "TOPLEFT"}, "text": "table cell bounding boxes.", "orig": "table cell bounding boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes."}, {"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 50.112, "t": 399.338, "r": 286.365, "b": 419.846, "coord_origin": "TOPLEFT"}, "confidence": 0.934766411781311, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 407.89, "r_x1": 286.365, "r_y1": 407.89, "r_x2": 286.365, "r_y2": 399.338, "r_x3": 62.067, "r_y3": 399.338, "coord_origin": "TOPLEFT"}, "text": "Here is a step-by-step description of the prediction post-", "orig": "Here is a step-by-step description of the prediction post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 419.846, "r_x1": 95.492, "r_y1": 419.846, "r_x2": 95.492, "r_y2": 411.294, "r_x3": 50.112, "r_y3": 411.294, "coord_origin": "TOPLEFT"}, "text": "processing:", "orig": "processing:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is a step-by-step description of the prediction postprocessing:"}, {"label": "list_item", "id": 20, "page_no": 11, "cluster": {"id": 20, "label": "list_item", "bbox": {"l": 50.112, "t": 423.564, "r": 286.365, "b": 456.026, "coord_origin": "TOPLEFT"}, "confidence": 0.8280702233314514, "cells": [{"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 432.116, "r_x1": 286.365, "r_y1": 432.116, "r_x2": 286.365, "r_y2": 423.564, "r_x3": 62.067, "r_y3": 423.564, "coord_origin": "TOPLEFT"}, "text": "1. Get the minimal grid dimensions - number of rows and", "orig": "1. Get the minimal grid dimensions - number of rows and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 444.071, "r_x1": 218.779, "r_y1": 444.071, "r_x2": 218.779, "r_y2": 435.519, "r_x3": 50.112, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "columns for the predicted table structure.", "orig": "columns for the predicted table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.796, "r_y0": 444.071, "r_x1": 286.365, "r_y1": 444.071, "r_x2": 286.365, "r_y2": 435.519, "r_x3": 224.796, "r_y3": 435.519, "coord_origin": "TOPLEFT"}, "text": "This represents", "orig": "This represents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 456.026, "r_x1": 274.51, "r_y1": 456.026, "r_x2": 274.51, "r_y2": 447.474, "r_x3": 50.112, "r_y3": 447.474, "coord_origin": "TOPLEFT"}, "text": "the most granular grid for the underlying table structure.", "orig": "the most granular grid for the underlying table structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure."}, {"label": "list_item", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 50.112, "t": 459.744, "r": 286.365, "b": 504.161, "coord_origin": "TOPLEFT"}, "confidence": 0.9646760821342468, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 468.296, "r_x1": 69.539, "r_y1": 468.296, "r_x2": 69.539, "r_y2": 459.744, "r_x3": 62.067, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.429, "r_y0": 468.296, "r_x1": 286.365, "r_y1": 468.296, "r_x2": 286.365, "r_y2": 459.744, "r_x3": 77.429, "r_y3": 459.744, "coord_origin": "TOPLEFT"}, "text": "Generate pair-wise matches between the bounding", "orig": "Generate pair-wise matches between the bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 480.251, "r_x1": 286.365, "r_y1": 480.251, "r_x2": 286.365, "r_y2": 471.699, "r_x3": 50.112, "r_y3": 471.699, "coord_origin": "TOPLEFT"}, "text": "boxes of the PDF cells and the predicted cells. The Intersec-", "orig": "boxes of the PDF cells and the predicted cells. The Intersec-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 492.206, "r_x1": 286.365, "r_y1": 492.206, "r_x2": 286.365, "r_y2": 483.654, "r_x3": 50.112, "r_y3": 483.654, "coord_origin": "TOPLEFT"}, "text": "tion Over Union (IOU) metric is used to evaluate the quality", "orig": "tion Over Union (IOU) metric is used to evaluate the quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 504.161, "r_x1": 110.705, "r_y1": 504.161, "r_x2": 110.705, "r_y2": 495.609, "r_x3": 50.112, "r_y3": 495.609, "coord_origin": "TOPLEFT"}, "text": "of the matches.", "orig": "of the matches.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches."}, {"label": "list_item", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 50.112, "t": 507.879, "r": 286.365, "b": 528.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9391399025917053, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 516.431, "r_x1": 69.539, "r_y1": 516.431, "r_x2": 69.539, "r_y2": 507.879, "r_x3": 62.067, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 75.248, "r_y0": 516.431, "r_x1": 286.365, "r_y1": 516.431, "r_x2": 286.365, "r_y2": 507.879, "r_x3": 75.248, "r_y3": 507.879, "coord_origin": "TOPLEFT"}, "text": "Use a carefully selected IOU threshold to designate", "orig": "Use a carefully selected IOU threshold to designate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 528.387, "r_x1": 226.071, "r_y1": 528.387, "r_x2": 226.071, "r_y2": 519.835, "r_x3": 50.112, "r_y3": 519.835, "coord_origin": "TOPLEFT"}, "text": "the matches as 'good' ones and 'bad' ones.", "orig": "the matches as 'good' ones and 'bad' ones.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Use a carefully selected IOU threshold to designate the matches as 'good' ones and 'bad' ones."}, {"label": "list_item", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 50.112, "t": 532.104, "r": 286.365, "b": 564.567, "coord_origin": "TOPLEFT"}, "confidence": 0.9491708874702454, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 540.656, "r_x1": 76.453, "r_y1": 540.656, "r_x2": 76.453, "r_y2": 532.104, "r_x3": 62.067, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "3.a.", "orig": "3.a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 80.587, "r_y0": 540.656, "r_x1": 286.365, "r_y1": 540.656, "r_x2": 286.365, "r_y2": 532.104, "r_x3": 80.587, "r_y3": 532.104, "coord_origin": "TOPLEFT"}, "text": "If all IOU scores in a column are below the thresh-", "orig": "If all IOU scores in a column are below the thresh-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 552.612, "r_x1": 286.365, "r_y1": 552.612, "r_x2": 286.365, "r_y2": 544.06, "r_x3": 50.112, "r_y3": 544.06, "coord_origin": "TOPLEFT"}, "text": "old, discard all predictions (structure and bounding boxes)", "orig": "old, discard all predictions (structure and bounding boxes)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 564.567, "r_x1": 114.032, "r_y1": 564.567, "r_x2": 114.032, "r_y2": 556.015, "r_x3": 50.112, "r_y3": 556.015, "coord_origin": "TOPLEFT"}, "text": "for that column.", "orig": "for that column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 50.112, "t": 568.285, "r": 286.365, "b": 600.7470000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9587163329124451, "cells": [{"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 576.837, "r_x1": 69.539, "r_y1": 576.837, "r_x2": 69.539, "r_y2": 568.285, "r_x3": 62.067, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.732, "r_y0": 576.837, "r_x1": 286.365, "r_y1": 576.837, "r_x2": 286.365, "r_y2": 568.285, "r_x3": 76.732, "r_y3": 568.285, "coord_origin": "TOPLEFT"}, "text": "Find the best-fitting content alignment for the pre-", "orig": "Find the best-fitting content alignment for the pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 588.792, "r_x1": 286.365, "r_y1": 588.792, "r_x2": 286.365, "r_y2": 580.24, "r_x3": 50.112, "r_y3": 580.24, "coord_origin": "TOPLEFT"}, "text": "dicted cells with good IOU per each column. The alignment", "orig": "dicted cells with good IOU per each column. The alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 600.7470000000001, "r_x1": 278.704, "r_y1": 600.7470000000001, "r_x2": 278.704, "r_y2": 592.1949999999999, "r_x3": 50.112, "r_y3": 592.1949999999999, "coord_origin": "TOPLEFT"}, "text": "of the column can be identified by the following formula:", "orig": "of the column can be identified by the following formula:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:"}, {"label": "formula", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "formula", "bbox": {"l": 110.705, "t": 624.104, "r": 286.362, "b": 654.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9298412799835205, "cells": [{"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 112.028, "r_y0": 632.81, "r_x1": 157.952, "r_y1": 632.81, "r_x2": 157.952, "r_y2": 624.104, "r_x3": 112.028, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "alignment", "orig": "alignment", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.715, "r_y0": 632.81, "r_x1": 203.496, "r_y1": 632.81, "r_x2": 203.496, "r_y2": 624.104, "r_x3": 160.715, "r_y3": 624.104, "coord_origin": "TOPLEFT"}, "text": "= arg min", "orig": "= arg min", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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668.016, "coord_origin": "TOPLEFT"}, "text": "}", "orig": "}", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.563, "r_y0": 676.886, "r_x1": 233.949, "r_y1": 676.886, "r_x2": 233.949, "r_y2": 668.3340000000001, "r_x3": 219.563, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.835, "r_y0": 676.722, "r_x1": 243.529, "r_y1": 676.722, "r_x2": 243.529, "r_y2": 668.016, "r_x3": 237.835, "r_y3": 668.016, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 243.529, "r_y0": 677.645, "r_x1": 247.089, "r_y1": 677.645, "r_x2": 247.089, "r_y2": 671.551, "r_x3": 243.529, "r_y3": 671.551, "coord_origin": "TOPLEFT"}, "text": "c", "orig": "c", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.473, "r_y0": 676.886, "r_x1": 258.118, "r_y1": 676.886, "r_x2": 258.118, "r_y2": 668.3340000000001, "r_x3": 251.473, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.003, "r_y0": 676.886, "r_x1": 286.362, "r_y1": 676.886, "r_x2": 286.362, "r_y2": 668.3340000000001, "r_x3": 262.003, "r_y3": 668.3340000000001, "coord_origin": "TOPLEFT"}, "text": "the x-", "orig": "the x-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 688.841, "r_x1": 205.887, "r_y1": 688.841, "r_x2": 205.887, "r_y2": 680.289, "r_x3": 50.112, "r_y3": 680.289, "coord_origin": "TOPLEFT"}, "text": "coordinate for the corresponding point.", "orig": "coordinate for the corresponding point.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point."}, {"label": "list_item", "id": 13, "page_no": 11, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 50.112, "t": 692.559, "r": 286.365, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9260510206222534, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 62.067, "r_y0": 701.111, "r_x1": 69.539, "r_y1": 701.111, "r_x2": 69.539, "r_y2": 692.559, "r_x3": 62.067, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 76.274, "r_y0": 701.111, "r_x1": 286.365, "r_y1": 701.111, "r_x2": 286.365, "r_y2": 692.559, "r_x3": 76.274, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "Use the alignment computed in step 4, to compute", "orig": "Use the alignment computed in step 4, to compute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 50.112, "r_y0": 713.066, "r_x1": 94.605, "r_y1": 713.066, "r_x2": 94.605, "r_y2": 704.514, "r_x3": 50.112, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "the median", "orig": "the median", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.599, "r_y0": 712.902, "r_x1": 103.293, "r_y1": 712.902, "r_x2": 103.293, "r_y2": 704.196, "r_x3": 97.599, "r_y3": 704.196, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 103.292, "r_y0": 713.066, "r_x1": 286.365, "r_y1": 713.066, "r_x2": 286.365, "r_y2": 704.514, "r_x3": 103.292, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "-coordinate for all table columns and the me-", "orig": "-coordinate for all table columns and the me-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-"}, {"label": "text", "id": 16, "page_no": 11, "cluster": {"id": 16, "label": "text", "bbox": {"l": 308.862, "t": 211.20000000000005, "r": 545.115, "b": 255.61800000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8942298889160156, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 219.75199999999995, "r_x1": 435.915, "r_y1": 219.75199999999995, "r_x2": 435.915, "r_y2": 211.20000000000005, "r_x3": 308.862, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "dian cell size for all table cells.", "orig": "dian cell size for all table cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.697, "r_y0": 219.75199999999995, "r_x1": 545.115, "r_y1": 219.75199999999995, "r_x2": 545.115, "r_y2": 211.20000000000005, "r_x3": 440.697, "r_y3": 211.20000000000005, "coord_origin": "TOPLEFT"}, "text": "The usage of median dur-", "orig": "The usage of median dur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 231.70799999999997, "r_x1": 545.115, "r_y1": 231.70799999999997, "r_x2": 545.115, "r_y2": 223.15599999999995, "r_x3": 308.862, "r_y3": 223.15599999999995, "coord_origin": "TOPLEFT"}, "text": "ing the computations, helps to eliminate outliers caused by", "orig": "ing the computations, helps to eliminate outliers caused by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 243.663, "r_x1": 545.115, "r_y1": 243.663, "r_x2": 545.115, "r_y2": 235.111, "r_x3": 308.862, "r_y3": 235.111, "coord_origin": "TOPLEFT"}, "text": "occasional column spans which are usually wider than the", "orig": "occasional column spans which are usually wider than the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 255.61800000000005, "r_x1": 339.577, "r_y1": 255.61800000000005, "r_x2": 339.577, "r_y2": 247.06600000000003, "r_x3": 308.862, "r_y3": 247.06600000000003, "coord_origin": "TOPLEFT"}, "text": "normal.", "orig": "normal.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal."}, {"label": "list_item", "id": 9, "page_no": 11, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 308.862, "t": 259.371, "r": 545.115, "b": 279.87800000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9423392415046692, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 267.923, "r_x1": 328.289, "r_y1": 267.923, "r_x2": 328.289, "r_y2": 259.371, "r_x3": 320.817, "r_y3": 259.371, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.884, "r_y0": 267.923, "r_x1": 545.115, "r_y1": 267.923, "r_x2": 545.115, "r_y2": 259.371, "r_x3": 334.884, "r_y3": 259.371, "coord_origin": "TOPLEFT"}, "text": "Snap all cells with bad IOU to their corresponding", "orig": "Snap all cells with bad IOU to their corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 279.87800000000004, "r_x1": 338.192, "r_y1": 279.87800000000004, "r_x2": 338.192, "r_y2": 271.326, "r_x3": 308.862, "r_y3": 271.326, "coord_origin": "TOPLEFT"}, "text": "median", "orig": "median", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.682, "r_y0": 279.71400000000006, "r_x1": 346.376, "r_y1": 279.71400000000006, "r_x2": 346.376, "r_y2": 271.00800000000004, "r_x3": 340.682, "r_y3": 271.00800000000004, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.376, "r_y0": 279.87800000000004, "r_x1": 453.723, "r_y1": 279.87800000000004, "r_x2": 453.723, "r_y2": 271.326, "r_x3": 346.376, "r_y3": 271.326, "coord_origin": "TOPLEFT"}, "text": "-coordinates and cell sizes.", "orig": "-coordinates and cell sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes."}, {"label": "list_item", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 308.862, "t": 283.632, "r": 545.115, "b": 387.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9562004804611206, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 292.184, "r_x1": 328.289, "r_y1": 292.184, "r_x2": 328.289, "r_y2": 283.632, "r_x3": 320.817, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "7.", "orig": "7.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.144, "r_y0": 292.184, "r_x1": 545.115, "r_y1": 292.184, "r_x2": 545.115, "r_y2": 283.632, "r_x3": 332.144, "r_y3": 283.632, "coord_origin": "TOPLEFT"}, "text": "Generate a new set of pair-wise matches between the", "orig": "Generate a new set of pair-wise matches between the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 304.139, "r_x1": 476.562, "r_y1": 304.139, "r_x2": 476.562, "r_y2": 295.587, "r_x3": 308.862, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "corrected bounding boxes and PDF cells.", "orig": "corrected bounding boxes and PDF cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 482.082, "r_y0": 304.139, "r_x1": 545.115, "r_y1": 304.139, "r_x2": 545.115, "r_y2": 295.587, "r_x3": 482.082, "r_y3": 295.587, "coord_origin": "TOPLEFT"}, "text": "This time use a", "orig": "This time use a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 316.094, "r_x1": 545.115, "r_y1": 316.094, "r_x2": 545.115, "r_y2": 307.542, "r_x3": 308.862, "r_y3": 307.542, "coord_origin": "TOPLEFT"}, "text": "modified version of the IOU metric, where the area of the", "orig": "modified version of the IOU metric, where the area of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 328.049, "r_x1": 545.115, "r_y1": 328.049, "r_x2": 545.115, "r_y2": 319.497, "r_x3": 308.862, "r_y3": 319.497, "coord_origin": "TOPLEFT"}, "text": "intersection between the predicted and PDF cells is divided", "orig": "intersection between the predicted and PDF cells is divided", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 340.005, "r_x1": 397.19, "r_y1": 340.005, "r_x2": 397.19, "r_y2": 331.453, "r_x3": 308.862, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "by the PDF cell area.", "orig": "by the PDF cell area.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 403.656, "r_y0": 340.005, "r_x1": 545.115, "r_y1": 340.005, "r_x2": 545.115, "r_y2": 331.453, "r_x3": 403.656, "r_y3": 331.453, "coord_origin": "TOPLEFT"}, "text": "In case there are multiple matches", "orig": "In case there are multiple matches", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 351.96, "r_x1": 545.115, "r_y1": 351.96, "r_x2": 545.115, "r_y2": 343.408, "r_x3": 308.862, "r_y3": 343.408, "coord_origin": "TOPLEFT"}, "text": "for the same PDF cell, the prediction with the higher score", "orig": "for the same PDF cell, the prediction with the higher score", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 363.915, "r_x1": 357.26, "r_y1": 363.915, "r_x2": 357.26, "r_y2": 355.363, "r_x3": 308.862, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "is preferred.", "orig": "is preferred.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.176, "r_y0": 363.915, "r_x1": 545.115, "r_y1": 363.915, "r_x2": 545.115, "r_y2": 355.363, "r_x3": 361.176, "r_y3": 355.363, "coord_origin": "TOPLEFT"}, "text": "This covers the cases where the PDF cells are", "orig": "This covers the cases where the PDF cells are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 375.87, "r_x1": 545.115, "r_y1": 375.87, "r_x2": 545.115, "r_y2": 367.318, "r_x3": 308.862, "r_y3": 367.318, "coord_origin": "TOPLEFT"}, "text": "smaller than the area of predicted or corrected prediction", "orig": "smaller than the area of predicted or corrected prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 387.825, "r_x1": 329.614, "r_y1": 387.825, "r_x2": 329.614, "r_y2": 379.273, "r_x3": 308.862, "r_y3": 379.273, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells."}, {"label": "list_item", "id": 17, "page_no": 11, "cluster": {"id": 17, "label": "list_item", "bbox": {"l": 308.862, "t": 391.579, "r": 545.115, "b": 459.906, "coord_origin": "TOPLEFT"}, "confidence": 0.8719939589500427, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 400.131, "r_x1": 328.289, "r_y1": 400.131, "r_x2": 328.289, "r_y2": 391.579, "r_x3": 320.817, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "8.", "orig": "8.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.569, "r_y0": 400.131, "r_x1": 545.115, "r_y1": 400.131, "r_x2": 545.115, "r_y2": 391.579, "r_x3": 333.569, "r_y3": 391.579, "coord_origin": "TOPLEFT"}, "text": "In some rare occasions, we have noticed that Table-", "orig": "In some rare occasions, we have noticed that Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 412.086, "r_x1": 545.115, "r_y1": 412.086, "r_x2": 545.115, "r_y2": 403.534, "r_x3": 308.862, "r_y3": 403.534, "coord_origin": "TOPLEFT"}, "text": "Former can confuse a single column as two. When the post-", "orig": "Former can confuse a single column as two. When the post-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 424.041, "r_x1": 545.115, "r_y1": 424.041, "r_x2": 545.115, "r_y2": 415.489, "r_x3": 308.862, "r_y3": 415.489, "coord_origin": "TOPLEFT"}, "text": "processing steps are applied, this results with two predicted", "orig": "processing steps are applied, this results with two predicted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 435.996, "r_x1": 488.109, "r_y1": 435.996, "r_x2": 488.109, "r_y2": 427.444, "r_x3": 308.862, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "columns pointing to the same PDF column.", "orig": "columns pointing to the same PDF column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 494.336, "r_y0": 435.996, "r_x1": 545.115, "r_y1": 435.996, "r_x2": 545.115, "r_y2": 427.444, "r_x3": 494.336, "r_y3": 427.444, "coord_origin": "TOPLEFT"}, "text": "In such case", "orig": "In such case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 447.951, "r_x1": 545.115, "r_y1": 447.951, "r_x2": 545.115, "r_y2": 439.399, "r_x3": 308.862, "r_y3": 439.399, "coord_origin": "TOPLEFT"}, "text": "we must de-duplicate the columns according to highest to-", "orig": "we must de-duplicate the columns according to highest to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 459.906, "r_x1": 426.182, "r_y1": 459.906, "r_x2": 426.182, "r_y2": 451.354, "r_x3": 308.862, "r_y3": 451.354, "coord_origin": "TOPLEFT"}, "text": "tal column intersection score.", "orig": "tal column intersection score.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."}, {"label": "list_item", "id": 15, "page_no": 11, "cluster": {"id": 15, "label": "list_item", "bbox": {"l": 308.862, "t": 463.66, "r": 545.115, "b": 567.8530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9126082062721252, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 472.212, "r_x1": 328.289, "r_y1": 472.212, "r_x2": 328.289, "r_y2": 463.66, "r_x3": 320.817, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "9.", "orig": "9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.629, "r_y0": 472.212, "r_x1": 478.505, "r_y1": 472.212, "r_x2": 478.505, "r_y2": 463.66, "r_x3": 333.629, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "Pick up the remaining orphan cells.", "orig": "Pick up the remaining orphan cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.855, "r_y0": 472.212, "r_x1": 545.115, "r_y1": 472.212, "r_x2": 545.115, "r_y2": 463.66, "r_x3": 483.855, "r_y3": 463.66, "coord_origin": "TOPLEFT"}, "text": "There could be", "orig": "There could be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 484.167, "r_x1": 545.115, "r_y1": 484.167, "r_x2": 545.115, "r_y2": 475.615, "r_x3": 308.862, "r_y3": 475.615, "coord_origin": "TOPLEFT"}, "text": "cases, when after applying all the previous post-processing", "orig": "cases, when after applying all the previous post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 496.122, "r_x1": 545.115, "r_y1": 496.122, "r_x2": 545.115, "r_y2": 487.57, "r_x3": 308.862, "r_y3": 487.57, "coord_origin": "TOPLEFT"}, "text": "steps, some PDF cells could still remain without any match", "orig": "steps, some PDF cells could still remain without any match", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 508.077, "r_x1": 381.898, "r_y1": 508.077, "r_x2": 381.898, "r_y2": 499.525, "r_x3": 308.862, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "to predicted cells.", "orig": "to predicted cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 388.702, "r_y0": 508.077, "r_x1": 545.115, "r_y1": 508.077, "r_x2": 545.115, "r_y2": 499.525, "r_x3": 388.702, "r_y3": 499.525, "coord_origin": "TOPLEFT"}, "text": "However, it is still possible to deduce", "orig": "However, it is still possible to deduce", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 520.033, "r_x1": 545.115, "r_y1": 520.033, "r_x2": 545.115, "r_y2": 511.481, "r_x3": 308.862, "r_y3": 511.481, "coord_origin": "TOPLEFT"}, "text": "the correct matching for an orphan PDF cell by mapping its", "orig": "the correct matching for an orphan PDF cell by mapping its", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 531.988, "r_x1": 483.576, "r_y1": 531.988, "r_x2": 483.576, "r_y2": 523.4359999999999, "r_x3": 308.862, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "bounding box on the geometry of the grid.", "orig": "bounding box on the geometry of the grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 489.195, "r_y0": 531.988, "r_x1": 545.115, "r_y1": 531.988, "r_x2": 545.115, "r_y2": 523.4359999999999, "r_x3": 489.195, "r_y3": 523.4359999999999, "coord_origin": "TOPLEFT"}, "text": "This mapping", "orig": "This mapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 543.943, "r_x1": 545.115, "r_y1": 543.943, "r_x2": 545.115, "r_y2": 535.3910000000001, "r_x3": 308.862, "r_y3": 535.3910000000001, "coord_origin": "TOPLEFT"}, "text": "decides if the content of the orphan cell will be appended to", "orig": "decides if the content of the orphan cell will be appended to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 555.898, "r_x1": 545.115, "r_y1": 555.898, "r_x2": 545.115, "r_y2": 547.346, "r_x3": 308.862, "r_y3": 547.346, "coord_origin": "TOPLEFT"}, "text": "an already matched table cell, or a new table cell should be", "orig": "an already matched table cell, or a new table cell should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 567.8530000000001, "r_x1": 442.221, "r_y1": 567.8530000000001, "r_x2": 442.221, "r_y2": 559.3009999999999, "r_x3": 308.862, "r_y3": 559.3009999999999, "coord_origin": "TOPLEFT"}, "text": "created to match with the orphan.", "orig": "created to match with the orphan.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan."}, {"label": "text", "id": 19, "page_no": 11, "cluster": {"id": 19, "label": "text", "bbox": {"l": 308.862, "t": 571.607, "r": 545.117, "b": 604.069, "coord_origin": "TOPLEFT"}, "confidence": 0.8459473848342896, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 580.159, "r_x1": 332.712, "r_y1": 580.159, "r_x2": 332.712, "r_y2": 571.607, "r_x3": 320.817, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "9a.", "orig": "9a.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.943, "r_y0": 580.159, "r_x1": 545.115, "r_y1": 580.159, "r_x2": 545.115, "r_y2": 571.607, "r_x3": 337.943, "r_y3": 571.607, "coord_origin": "TOPLEFT"}, "text": "Compute the top and bottom boundary of the hori-", "orig": "Compute the top and bottom boundary of the hori-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 592.114, "r_x1": 471.641, "r_y1": 592.114, "r_x2": 471.641, "r_y2": 583.562, "r_x3": 308.862, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "zontal band for each grid row (min/max", "orig": "zontal band for each grid row (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.834, "r_y0": 591.95, "r_x1": 479.719, "r_y1": 591.95, "r_x2": 479.719, "r_y2": 583.244, "r_x3": 474.834, "r_y3": 583.244, "coord_origin": "TOPLEFT"}, "text": "y", "orig": "y", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 483.269, "r_y0": 592.114, "r_x1": 545.117, "r_y1": 592.114, "r_x2": 545.117, "r_y2": 583.562, "r_x3": 483.269, "r_y3": 583.562, "coord_origin": "TOPLEFT"}, "text": "coordinates per", "orig": "coordinates per", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 604.069, "r_x1": 329.913, "r_y1": 604.069, "r_x2": 329.913, "r_y2": 595.517, "r_x3": 308.862, "r_y3": 595.517, "coord_origin": "TOPLEFT"}, "text": "row).", "orig": "row).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."}, {"label": "list_item", "id": 21, "page_no": 11, "cluster": {"id": 21, "label": "list_item", "bbox": {"l": 308.862, "t": 607.822, "r": 545.115, "b": 628.33, "coord_origin": "TOPLEFT"}, "confidence": 0.7716891169548035, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 616.374, "r_x1": 332.872, "r_y1": 616.374, "r_x2": 332.872, "r_y2": 607.822, "r_x3": 320.817, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "9b.", "orig": "9b.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.925, "r_y0": 616.374, "r_x1": 374.226, "r_y1": 616.374, "r_x2": 374.226, "r_y2": 607.822, "r_x3": 339.925, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "Intersect", "orig": "Intersect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.042, "r_y0": 616.374, "r_x1": 545.115, "r_y1": 616.374, "r_x2": 545.115, "r_y2": 607.822, "r_x3": 378.042, "r_y3": 607.822, "coord_origin": "TOPLEFT"}, "text": "the orphan's bounding box with the row", "orig": "the orphan's bounding box with the row", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 628.33, "r_x1": 495.292, "r_y1": 628.33, "r_x2": 495.292, "r_y2": 619.778, "r_x3": 308.862, "r_y3": 619.778, "coord_origin": "TOPLEFT"}, "text": "bands, and map the cell to the closest grid row.", "orig": "bands, and map the cell to the closest grid row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row."}, {"label": "list_item", "id": 18, "page_no": 11, "cluster": {"id": 18, "label": "list_item", "bbox": {"l": 308.862, "t": 632.083, "r": 545.115, "b": 664.545, "coord_origin": "TOPLEFT"}, "confidence": 0.8584907054901123, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 640.635, "r_x1": 332.712, "r_y1": 640.635, "r_x2": 332.712, "r_y2": 632.083, "r_x3": 320.817, "r_y3": 632.083, "coord_origin": "TOPLEFT"}, "text": "9c.", "orig": "9c.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.534, "r_y0": 640.635, "r_x1": 545.115, "r_y1": 640.635, "r_x2": 545.115, "r_y2": 632.083, "r_x3": 337.534, "r_y3": 632.083, "coord_origin": "TOPLEFT"}, "text": "Compute the left and right boundary of the vertical", "orig": "Compute the left and right boundary of the vertical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 652.59, "r_x1": 455.282, "r_y1": 652.59, "r_x2": 455.282, "r_y2": 644.038, "r_x3": 308.862, "r_y3": 644.038, "coord_origin": "TOPLEFT"}, "text": "band for each grid column (min/max", "orig": "band for each grid column (min/max", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 457.777, "r_y0": 652.4259999999999, "r_x1": 463.471, "r_y1": 652.4259999999999, "r_x2": 463.471, "r_y2": 643.72, "r_x3": 457.777, "r_y3": 643.72, "coord_origin": "TOPLEFT"}, "text": "x", "orig": "x", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 465.971, "r_y0": 652.59, "r_x1": 545.114, "r_y1": 652.59, "r_x2": 545.114, "r_y2": 644.038, "r_x3": 465.971, "r_y3": 644.038, "coord_origin": "TOPLEFT"}, "text": "coordinates per col-", "orig": "coordinates per col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 664.545, "r_x1": 332.384, "r_y1": 664.545, "r_x2": 332.384, "r_y2": 655.9929999999999, "r_x3": 308.862, "r_y3": 655.9929999999999, "coord_origin": "TOPLEFT"}, "text": "umn).", "orig": "umn).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."}, {"label": "list_item", "id": 22, "page_no": 11, "cluster": {"id": 22, "label": "list_item", "bbox": {"l": 308.862, "t": 668.299, "r": 545.115, "b": 688.806, "coord_origin": "TOPLEFT"}, "confidence": 0.7590745091438293, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 676.851, "r_x1": 545.115, "r_y1": 676.851, "r_x2": 545.115, "r_y2": 668.299, "r_x3": 320.817, "r_y3": 668.299, "coord_origin": "TOPLEFT"}, "text": "9d. Intersect the orphan's bounding box with the column", "orig": "9d. Intersect the orphan's bounding box with the column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 688.806, "r_x1": 510.585, "r_y1": 688.806, "r_x2": 510.585, "r_y2": 680.254, "r_x3": 308.862, "r_y3": 680.254, "coord_origin": "TOPLEFT"}, "text": "bands, and map the cell to the closest grid column.", "orig": "bands, and map the cell to the closest grid column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column."}, {"label": "list_item", "id": 25, "page_no": 11, "cluster": {"id": 25, "label": "list_item", "bbox": {"l": 308.862, "t": 692.559, "r": 545.115, "b": 713.066, "coord_origin": "TOPLEFT"}, "confidence": 0.6971189975738525, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.817, "r_y0": 701.111, "r_x1": 545.115, "r_y1": 701.111, "r_x2": 545.115, "r_y2": 692.559, "r_x3": 320.817, "r_y3": 692.559, "coord_origin": "TOPLEFT"}, "text": "9e. If the table cell under the identified row and column", "orig": "9e. If the table cell under the identified row and column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.862, "r_y0": 713.066, "r_x1": 545.115, "r_y1": 713.066, "r_x2": 545.115, "r_y2": 704.514, "r_x3": 308.862, "r_y3": 704.514, "coord_origin": "TOPLEFT"}, "text": "is not empty, extend its content with the content of the or-", "orig": "is not empty, extend its content with the content of the or-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9e. 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Nassar +DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis +Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com +Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com +Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com +Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com +Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com ABSTRACT -Accurate document layout analysis is a key requirement for high- +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. CCS CONCEPTS -• -Permission to make digital or hard copies of part or all of this work for personal or https://doi.org/10.1145/3534678.3539043 -Peter Staar -Figure 1: +· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning Computer vision ; ; Object detection ; +Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). +KDD '22, August 14-18, 2022, Washington, DC, USA +© 2022 Copyright held by the owner/author(s). +ACM ISBN 978-1-4503-9385-0/22/08. +https://doi.org/10.1145/3534678.3539043 +Figure 1: Four examples of complex page layouts across different document categories KEYWORDS -PDF document conversion, layout segmentation, object-detection, +PDF document conversion, layout segmentation, object-detection, data set, Machine Learning ACMReference Format: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 -KDD '22, August 14-18, 2022, Washington, DC, USA -1 -Despite the substantial improvements achieved with machine-learning -Akeyproblem in the process of document conversion is to under- -In this paper, we present the DocLayNet dataset. It provides page- -(1) -(2) -(3) -(4) +KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +1 INTRODUCTION +Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. +Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. +In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: +(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. +(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources. +(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. +(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation. -1 -This enables experimentation with annotation uncertainty -(5) +1 https://developer.ibm.com/exchanges/data/all/doclaynet +This enables experimentation with annotation uncertainty and quality control analysis. +(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. -All aspects outlined above are detailed in Section 3. In Section 4, -In Section 5, we will present baseline accuracy numbers for a -2 -While early approaches in document-layout analysis used rule- -Lately, new types of ML models for document-layout analysis -3 -DocLayNet contains 80863 PDF pages. Among these, 7059 carry two -In addition to open intellectual property constraints for the +All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns. +In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery. +2 RELATED WORK +While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]. +Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish. +3 THE DOCLAYNET DATASET +DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4. +In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis KDD '22, August 14-18, 2022, Washington, DC, USA -2 -Figure 2: Distribution of DocLayNet pages across document -to a minimum, since they introduce difficulties in annotation (see -The pages in DocLayNet can be grouped into six distinct cate- -We did not control the document selection with regard to lan- -To ensure that future benchmarks in the document-layout analy- -Table 1 shows the overall frequency and distribution of the labels -In order to accommodate the different types of models currently -Despite being cost-intense and far less scalable than automation, -4 -The annotation campaign was carried out in four phases. In phase +Figure 2: Distribution of DocLayNet pages across document categories. +to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild". +The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. +We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. +To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. +2 e.g. AAPL from https://www.annualreports.com/ +Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5. +In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 × 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. +Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4. +4 ANNOTATION CAMPAIGN +The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four, -KDD '22, August 14-18, 2022, Washington, DC, USA -Figure 3: Corpus Conversion Service annotation user inter- -Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % -we distributed the annotation workload and performed continuous -Phase 1: Data selection and preparation. -include publication repositories such as arXiv -Preparation work included uploading and parsing the sourced -Phase 2: Label selection and guideline. -3 +KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +% of Totaltriple inter-annotator mAP @ 0.5-0.95 (%)class labelCountTrainTestValAllFinManSciLawPatTenCaption225242.041.772.3284-8940-6186-9294-9995-9969-78n/aFootnote63180.600.310.5883-91n/a10062-8885-94n/a82-97Formula250272.251.902.9683-85n/an/a84-8786-96n/an/aList-item18566017.1913.3415.8287-8874-8390-9297-9781-8575-8893-95Page-footer708786.515.586.0093-9488-9095-9610092-9710096-98Page-header580225.106.705.0685-8966-7690-9498-10091-9297-9981-86Picture459764.212.785.3169-7156-5982-8669-8280-9566-7159-76Section-header14288412.6015.7712.8583-8476-8190-9294-9587-9469-7378-86Table347333.202.273.6077-8175-8083-8698-9958-8079-8470-85Text51037745.8249.2845.0084-8681-8688-9389-9387-9271-7987-95Title50710.470.300.5060-7224-6350-6394-10082-9668-7924-56Total1107470941123998166653182-8371-7479-8189-9486-9171-7668-85Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. +Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. +we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. +Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. +Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. +Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on +3 https://arxiv.org/ DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis KDD '22, August 14-18, 2022, Washington, DC, USA -the textual content of an element, which goes beyond visual layout -At first sight, the task of visual document-layout interpretation -Obviously, this inconsistency in annotations is not desirable for -(1) -(2) -(3) -(4) -(5) -(6) +the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category. +At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. +Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: +(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object. +(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement. +(3) For every Caption , there must be exactly one corresponding Picture or Table . +(4) Connected sub-pictures are grouped together in one Picture object. +(5) Formula numbers are included in a Formula object. +(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line. -The complete annotation guideline is over 100 pages long and a -Phase 3: Training. -Figure 4: Examples of plausible annotation alternatives for -were carried out over a timeframe of 12 weeks, after which 8 of the -Phase 4: Production annotation. +The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference. +Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations + +05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0 +Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous. +were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar. +Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted -KDD '22, August 14-18, 2022, Washington, DC, USA -Table 2: Prediction performance (mAP@0.5-0.95) of object - -to avoid this at any cost in order to have clear, unbiased baseline -5 -The primary goal of DocLayNet is to obtain high-quality ML models -Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask -paper and leave the detailed evaluation of more recent methods -In this section, we will present several aspects related to the +KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. +humanMRCNNFRCNNYOLOR50R101R101v5x6Caption84-8968.471.570.177.7Footnote83-9170.971.873.777.2Formula83-8560.163.463.566.2List-item87-8881.280.881.086.2Page-footer93-9461.659.358.961.1Page-header85-8971.970.072.067.9Picture69-7171.772.772.077.1Section-header83-8467.669.368.474.6Table77-8182.282.982.286.3Text84-8684.685.885.488.1Title60-7276.780.479.982.7All82-8372.473.573.476.8 +to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. +5 EXPERIMENTS +The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this +Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. +paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work. +In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]. Baselines for Object Detection -In Table 2, we present baseline experiments (given in mAP) on Mask +In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document. DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis KDD '22, August 14-18, 2022, Washington, DC, USA -Table 3: Performance of a Mask R-CNN R50 network in - +Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. +Class-count11654Caption68TextTextTextFootnote71TextTextTextFormula60TextTextTextList-item81Text82TextPage-footer6262--Page-header7268--Picture72727272Section-header68676968Table82838282Text85848484Title77Sec.-h.Sec.-h.Sec.-h.Overall72737877 Learning Curve -One of the fundamental questions related to any dataset is if it is +One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles. Impact of Class Labels -The choice and number of labels can have a significant effect on -Table 4: Performance of a Mask R-CNN R50 network with - -lists in PubLayNet (grouped list-items) versus DocLayNet (separate +The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of +Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in /tildelow 10% point improvement. +Class-count115SplitDocPageDocPageCaption6883Footnote7184Formula6066List-item81888288Page-footer6289Page-header7290Picture72827282Section-header68836983Table82898290Text85918490Title7781All72847887 +lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded. Impact of Document Split in Train and Test Set -Many documents in DocLayNet have a unique styling. In order +Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains ˜ 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided. Dataset Comparison -Throughout this paper, we claim that DocLayNet's wider variety of +Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , -KDD '22, August 14-18, 2022, Washington, DC, USA -Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask - -Section-header -For comparison of DocBank with DocLayNet, we trained only +KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. +Testing onTraining onlabelsPLNDBDLNPubLayNet (PLN)Figure964323Sec-header87-32Table952449Text96-42total933430DocBank (DB)Figure777131Table196522total486827DocLayNet (DLN)Figure675172Sec-header53-68Table874382Text77-84total594778 +Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text . +For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. Example Predictions -To conclude this section, we illustrate the quality of layout predic- -6 -In this paper, we presented the DocLayNet dataset. It provides the -From the dataset, we have derived on the one hand reference -To date, there is still a significant gap between human and ML +To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence. +6 CONCLUSION +In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. +From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust. +To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap. REFERENCES -[1] -[2] -[3] -[4] -[5] -[6] -[7] -[8] -[9] -[10] -[11] -[12] -[13] +[1] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013. +[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017. +[3] Hervé Déjean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. 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Nassar, and Peter", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter"}, {"self_ref": "#/texts/18", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.798, "t": 731.44, "r": 246.244, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/19", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.798, "t": 705.202, "r": 59.405, "b": 696.135, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "level": 1}, {"self_ref": "#/texts/20", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.798, "t": 680.586, "r": 303.017, "b": 672.785, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Despite the substantial improvements achieved with machine-learning", "text": "Despite the substantial improvements achieved with machine-learning"}, {"self_ref": "#/texts/21", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 63.761, "t": 560.038, "r": 295.564, "b": 552.237, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Akeyproblem in the process of document conversion is to under-", "text": "Akeyproblem in the process of document conversion is to under-"}, {"self_ref": "#/texts/22", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 63.761, "t": 286.066, "r": 295.564, "b": 278.265, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text": "In this paper, we present the DocLayNet dataset. It provides page-"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 206.98800000000006, "r": 74.221, "b": 199.187, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(1)", "text": "(1)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/24", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 174.111, "r": 74.221, "b": 166.30999999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(2)", "text": "(2)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/25", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 152.19399999999996, "r": 74.221, "b": 144.39300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(3)", "text": "(3)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/26", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 119.31700000000001, "r": 74.221, "b": 111.51599999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(4)", "text": "(4)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/27", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672, "t": 91.10299999999995, "r": 56.22, "b": 86.33600000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/28", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.095, "t": 704.206, "r": 558.432, "b": 696.405, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "This enables experimentation with annotation uncertainty", "text": "This enables experimentation with annotation uncertainty"}, {"self_ref": "#/texts/29", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.865, "t": 682.288, "r": 338.378, "b": 674.487, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(5)", "text": "(5)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/30", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 327.918, "t": 623.594, "r": 559.19, "b": 615.793, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text": "All aspects outlined above are detailed in Section 3. In Section 4,"}, {"self_ref": "#/texts/31", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 327.918, "t": 568.799, "r": 558.198, "b": 560.998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "In Section 5, we will present baseline accuracy numbers for a", "text": "In Section 5, we will present baseline accuracy numbers for a"}, {"self_ref": "#/texts/32", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 469.853, "r": 323.562, "b": 460.786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2", "level": 1}, {"self_ref": "#/texts/33", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.525, "t": 445.237, "r": 559.713, "b": 437.436, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "While early approaches in document-layout analysis used rule-", "text": "While early approaches in document-layout analysis used rule-"}, {"self_ref": "#/texts/34", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 327.918, "t": 324.689, "r": 558.198, "b": 316.888, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Lately, new types of ML models for document-layout analysis", "text": "Lately, new types of ML models for document-layout analysis"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 225.74199999999996, "r": 323.562, "b": 216.67499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3", "level": 1}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 201.12599999999998, "r": 558.202, "b": 193.32500000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 327.918, "t": 113.45500000000004, "r": 558.198, "b": 105.654, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "In addition to open intellectual property constraints for the", "text": "In addition to open intellectual property constraints for the"}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 91.673, "r": 56.346, "b": 86.90599999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 555.114, "r": 294.044, "b": 547.661, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Figure 2: Distribution of DocLayNet pages across document", "text": "Figure 2: Distribution of DocLayNet pages across document"}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 509.766, "r": 294.046, "b": 501.965, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "to a minimum, since they introduce difficulties in annotation (see", "text": "to a minimum, since they introduce difficulties in annotation (see"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 63.761, "t": 422.095, "r": 295.559, "b": 414.294, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text": "The pages in DocLayNet can be grouped into six distinct cate-"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 63.761, "t": 279.629, "r": 295.559, "b": 271.828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "We did not control the document selection with regard to lan-", "text": "We did not control the document selection with regard to lan-"}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 63.761, "t": 180.99900000000002, "r": 295.564, "b": 173.19799999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "To ensure that future benchmarks in the document-layout analy-", "text": "To ensure that future benchmarks in the document-layout analy-"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 327.918, "t": 704.206, "r": 558.201, "b": 696.405, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "Table 1 shows the overall frequency and distribution of the labels", "text": "Table 1 shows the overall frequency and distribution of the labels"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 327.918, "t": 627.494, "r": 558.438, "b": 619.693, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "In order to accommodate the different types of models currently", "text": "In order to accommodate the different types of models currently"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 327.918, "t": 517.905, "r": 559.185, "b": 510.104, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Despite being cost-intense and far less scalable than automation,", "text": "Despite being cost-intense and far less scalable than automation,"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.955, "t": 184.212, "r": 323.562, "b": 175.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4", "level": 1}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.686, "t": 159.596, "r": 558.201, "b": 151.79499999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "The annotation campaign was carried out in four phases. In phase", "text": "The annotation campaign was carried out in four phases. In phase"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.798, "t": 731.44, "r": 246.244, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.798, "t": 237.21900000000005, "r": 295.649, "b": 229.76599999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text": "Figure 3: Corpus Conversion Service annotation user inter-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.502, "t": 706.274, "r": 558.49, "b": 698.821, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.467, "t": 157.27800000000002, "r": 294.047, "b": 149.47699999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "we distributed the annotation workload and performed continuous", "text": "we distributed the annotation workload and performed continuous"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 63.761, "t": 113.21799999999996, "r": 226.725, "b": 105.76499999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Phase 1: Data selection and preparation.", "text": "Phase 1: Data selection and preparation."}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.955, "t": 479.49, "r": 481.034, "b": 471.689, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "include publication repositories such as arXiv", "text": "include publication repositories such as arXiv"}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 327.918, "t": 413.737, "r": 558.206, "b": 405.936, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Preparation work included uploading and parsing the sourced", "text": "Preparation work included uploading and parsing the sourced"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 327.918, "t": 282.006, "r": 482.418, "b": 274.553, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Phase 2: Label selection and guideline.", "text": "Phase 2: Label selection and guideline."}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.955, "t": 90.976, "r": 320.503, "b": 86.20900000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 704.206, "r": 294.045, "b": 696.405, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "the textual content of an element, which goes beyond visual layout", "text": "the textual content of an element, which goes beyond visual layout"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 63.761, "t": 682.288, "r": 294.043, "b": 674.487, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "At first sight, the task of visual document-layout interpretation", "text": "At first sight, the task of visual document-layout interpretation"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 63.761, "t": 539.823, "r": 294.219, "b": 532.022, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Obviously, this inconsistency in annotations is not desirable for", "text": "Obviously, this inconsistency in annotations is not desirable for"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 443.057, "r": 74.221, "b": 435.256, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(1)", "text": "(1)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 399.221, "r": 74.221, "b": 391.42, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(2)", "text": "(2)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 355.385, "r": 74.221, "b": 347.584, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(3)", "text": "(3)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/68", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 333.468, "r": 74.221, "b": 325.667, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(4)", "text": "(4)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/69", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 311.55, "r": 74.221, "b": 303.749, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(5)", "text": "(5)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 300.591, "r": 74.221, "b": 292.79, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "(6)", "text": "(6)", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.529, "t": 258.619, "r": 294.043, "b": 250.81799999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "The complete annotation guideline is over 100 pages long and a", "text": "The complete annotation guideline is over 100 pages long and a"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 63.761, "t": 214.55999999999995, "r": 136.774, "b": 207.10699999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Phase 3: Training.", "text": "Phase 3: Training."}, {"self_ref": "#/texts/73", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.955, "t": 317.735, "r": 558.389, "b": 310.282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Figure 4: Examples of plausible annotation alternatives for", "text": "Figure 4: Examples of plausible annotation alternatives for"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.623, "t": 266.072, "r": 558.204, "b": 258.27099999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 327.918, "t": 243.93000000000006, "r": 456.801, "b": 236.47699999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Phase 4: Production annotation.", "text": "Phase 4: Production annotation."}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.798, "t": 731.44, "r": 246.244, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.502, "t": 704.356, "r": 294.044, "b": 696.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.798, "t": 420.642, "r": 294.047, "b": 412.841, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text": "to avoid this at any cost in order to have clear, unbiased baseline"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.798, "t": 202.93200000000002, "r": 59.405, "b": 193.865, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.529, "t": 178.31600000000003, "r": 294.049, "b": 170.515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text": "The primary goal of DocLayNet is to obtain high-quality ML models"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 512.213, "r": 558.479, "b": 504.76, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 407.558, "r": 558.204, "b": 399.757, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "paper and leave the detailed evaluation of more recent methods", "text": "paper and leave the detailed evaluation of more recent methods"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 327.918, "t": 385.64, "r": 558.198, "b": 377.839, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "In this section, we will present several aspects related to the", "text": "In this section, we will present several aspects related to the"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 294.24, "r": 466.853, "b": 285.173, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "level": 1}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 279.5450000000001, "r": 558.431, "b": 271.744, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.502, "t": 704.356, "r": 294.044, "b": 696.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text": "Table 3: Performance of a Mask R-CNN R50 network in"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 471.492, "r": 131.056, "b": 462.425, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "level": 1}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 456.798, "r": 294.042, "b": 448.997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "One of the fundamental questions related to any dataset is if it is", "text": "One of the fundamental questions related to any dataset is if it is"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 248.55200000000002, "r": 164.329, "b": 239.485, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "level": 1}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.529, "t": 233.85799999999995, "r": 294.043, "b": 226.05700000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "The choice and number of labels can have a significant effect on", "text": "The choice and number of labels can have a significant effect on"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.659, "t": 704.356, "r": 558.201, "b": 696.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text": "Table 4: Performance of a Mask R-CNN R50 network with"}, {"self_ref": "#/texts/94", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 460.166, "r": 558.202, "b": 452.365, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 361.667, "r": 549.861, "b": 352.6, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Impact of Document Split in Train and Test Set", "text": "Impact of Document Split in Train and Test Set", "level": 1}, {"self_ref": "#/texts/96", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 346.973, "r": 558.369, "b": 339.172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Many documents in DocLayNet have a unique styling. In order", "text": "Many documents in DocLayNet have a unique styling. In order"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 182.72000000000003, "r": 418.548, "b": 173.65300000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "level": 1}, {"self_ref": "#/texts/98", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.686, "t": 168.02600000000007, "r": 558.206, "b": 160.22500000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text": "Throughout this paper, we claim that DocLayNet's wider variety of"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 731.44, "r": 246.244, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.502, "t": 704.356, "r": 294.322, "b": 696.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 400.712, "r": 106.239, "b": 392.804, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Section-header", "text": "Section-header"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 63.761, "t": 345.855, "r": 294.276, "b": 338.054, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "For comparison of DocBank with DocLayNet, we trained only", "text": "For comparison of DocBank with DocLayNet, we trained only"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 186.00099999999998, "r": 156.005, "b": 176.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "level": 1}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.529, "t": 171.30600000000004, "r": 295.557, "b": 163.505, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "To conclude this section, we illustrate the quality of layout predic-", "text": "To conclude this section, we illustrate the quality of layout predic-"}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.955, "t": 705.202, "r": 323.562, "b": 696.135, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6", "level": 1}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.955, "t": 690.508, "r": 558.204, "b": 682.707, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 66]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text": "In this paper, we presented the DocLayNet dataset. It provides the"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 327.918, "t": 602.836, "r": 558.198, "b": 595.035, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "From the dataset, we have derived on the one hand reference", "text": "From the dataset, we have derived on the one hand reference"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 327.918, "t": 504.206, "r": 558.435, "b": 496.405, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "To date, there is still a significant gap between human and ML", "text": "To date, there is still a significant gap between human and ML"}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 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[{"page_no": 1, "bbox": {"l": 172.543, "t": 599.369, "r": 275.307, "b": 553.534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "text": "Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 336.693, "t": 599.369, "r": 439.457, "b": 553.534, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 68]}], "orig": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.798, "t": 543.359, "r": 111.944, "b": 534.292, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "ABSTRACT", "text": "ABSTRACT", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.467, "t": 528.665, "r": 295.56, "b": 257.85, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1595]}], "orig": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 53.798, "t": 240.06500000000005, "r": 134.82, "b": 230.99800000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CCS CONCEPTS", "text": "CCS CONCEPTS", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.798, "t": 227.91700000000003, "r": 297.853, "b": 194.71000000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "\u00b7 Informationsystems \u2192 Documentstructure ; \u00b7 Appliedcomputing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning Computer vision ; ; Object detection ;", "text": "\u00b7 Informationsystems \u2192 Documentstructure ; \u00b7 Appliedcomputing \u2192 Document analysis ; \u00b7 Computing methodologies \u2192 Machine learning Computer vision ; ; Object detection ;", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.798, "t": 157.26700000000005, "r": 295.118, "b": 119.31899999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "text": "Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.798, "t": 116.66899999999998, "r": 197.863, "b": 110.519, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.317, "t": 107.85299999999995, "r": 186.746, "b": 101.78499999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text": "\u00a9 2022 Copyright held by the owner/author(s).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.554, "t": 99.88200000000006, "r": 157.031, "b": 93.81399999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 53.798, "t": 91.91200000000003, "r": 166.941, "b": 85.84400000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "https://doi.org/10.1145/3534678.3539043", "text": "https://doi.org/10.1145/3534678.3539043", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/16", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 317.955, "t": 251.14599999999996, "r": 559.806, "b": 232.73400000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "Figure 1: Four examples of complex page layouts across different document categories", "text": "Figure 1: Four examples of complex page layouts across different document categories", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/17", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.87, "t": 439.734, "r": 330.412, "b": 438.147, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "13", "text": "13", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/18", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83, "t": 460.039, "r": 351.161, "b": 458.769, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "USING THE VERTICAL TUBE -", "text": "USING THE VERTICAL TUBE -", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/19", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83, "t": 458.429, "r": 348.305, "b": 457.159, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "MODELS AY11230/11234", "text": "MODELS AY11230/11234", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/20", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 327.83, "t": 455.256, "r": 350.743, "b": 454.145, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "1. 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Make su", "text": "3. 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There are two objectives. The lower", "text": "1. There are two objectives. The lower", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/34", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 523.287, "r": 356.201, "b": 522.016, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "magnification objective has a greater", "text": "magnification objective has a greater", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/35", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 521.676, "r": 345.801, "b": 520.406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "depth of field and view.", "text": "depth of field and view.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/36", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 520.066, "r": 353.377, "b": 518.796, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "2. In order to observe the specimen", "text": "2. In order to observe the specimen", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/37", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 518.456, "r": 353.892, "b": 517.186, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "easily use the lower magnification", "text": "easily use the lower magnification", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/38", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 516.846, "r": 356.017, "b": 515.576, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "objective first. Then, by rotating the", "text": "objective first. Then, by rotating the", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/39", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 515.235, "r": 351.888, "b": 513.965, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "case, the magnification can be", "text": "case, the magnification can be", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/40", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 513.625, "r": 335.467, "b": 512.355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "changed.", "text": "changed.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/41", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 510.405, "r": 354.578, "b": 509.135, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "CHANGING THE INTERPUPILLARY", "text": "CHANGING THE INTERPUPILLARY", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/42", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 508.795, "r": 335.175, "b": 507.524, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "DISTANCE", "text": "DISTANCE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/43", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 507.184, "r": 355.748, "b": 505.914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "1. The distance between the observer's", "text": "1. The distance between the observer's", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/44", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 505.574, "r": 355.576, "b": 504.304, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "pupils is the interpupil ary distance.", "text": "pupils is the interpupil ary distance.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/45", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.88, "t": 503.964, "r": 355.17, "b": 502.694, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "2. To adjust the interpupil ary distance", "text": "2. To adjust the interpupil ary distance", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/46", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.882, "t": 502.354, "r": 356.021, "b": 501.084, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "rotate the prism caps until both eyes", "text": "rotate the prism caps until both eyes", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/47", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.882, "t": 500.744, "r": 351.575, "b": 499.473, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "coincide with the image in the", "text": "coincide with the image in the", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/48", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.882, "t": 499.133, "r": 336.207, "b": 497.863, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "eyepiece.", "text": "eyepiece.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/49", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.882, "t": 495.913, "r": 335.394, "b": 494.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/50", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.882, "t": 494.303, "r": 353.186, "b": 493.033, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "1. Remove the lens protective cover.", "text": "1. Remove the lens protective cover.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/51", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 492.692, "r": 354.432, "b": 491.422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "2. Place the specimen on the working", "text": "2. Place the specimen on the working", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/52", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 491.082, "r": 333.328, "b": 489.812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "stage.", "text": "stage.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/53", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 489.472, "r": 355.709, "b": 488.202, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "3. Focus the specimen with the left eye", "text": "3. Focus the specimen with the left eye", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/54", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 487.862, "r": 356.887, "b": 486.592, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "first while turning the focus knob until", "text": "first while turning the focus knob until", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/55", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 486.252, "r": 354.466, "b": 484.981, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "the image appears clear and sharp.", "text": "the image appears clear and sharp.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/56", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 484.641, "r": 357.261, "b": 483.371, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "4. Rotate the right eyepiece ring until the", "text": "4. Rotate the right eyepiece ring until the", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/57", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 483.031, "r": 356.449, "b": 481.761, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "images in each eyepiece coincide and", "text": "images in each eyepiece coincide and", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/58", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 481.421, "r": 343.173, "b": 480.151, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "are sharp and clear.", "text": "are sharp and clear.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/59", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 478.201, "r": 344.134, "b": 476.93, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CHANGING THE BULB", "text": "CHANGING THE BULB", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/60", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 476.59, "r": 348.502, "b": 475.32, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "1. Disconnect the power cord.", "text": "1. Disconnect the power cord.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/61", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.883, "t": 474.98, "r": 354.081, "b": 473.71, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "2. When the bulb is cool, remove the", "text": "2. When the bulb is cool, remove the", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/62", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.885, "t": 473.37, "r": 354.842, "b": 472.1, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique il uminator cap and remove", "text": "oblique il uminator cap and remove", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/63", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.885, "t": 471.76, "r": 348.021, "b": 470.49, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/64", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.885, "t": 470.149, "r": 352.968, "b": 468.879, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "3. Replace with a new halogen bulb.", "text": "3. Replace with a new halogen bulb.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/65", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.886, "t": 468.539, "r": 357.529, "b": 467.269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "4. Open the window in the base plate and", "text": "4. Open the window in the base plate and", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/66", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.886, "t": 466.929, "r": 350.138, "b": 465.659, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "replace the halogen lamp or", "text": "replace the halogen lamp or", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/67", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.886, "t": 465.319, "r": 353.141, "b": 464.049, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "fluorescent lamp of transmitted", "text": "fluorescent lamp of transmitted", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/68", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 326.886, "t": 463.709, "r": 336.892, "b": 462.439, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "il uminator.", "text": "il uminator.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/69", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42, "t": 528.117, "r": 366.933, "b": 526.847, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "FOCUSING", "text": "FOCUSING", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/70", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42, "t": 526.507, "r": 388.997, "b": 525.237, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "1. Turn the focusing knob away or toward", "text": "1. Turn the focusing knob away or toward", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/71", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.42, "t": 524.897, "r": 384.59, "b": 523.627, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "you until a clear image is viewed.", "text": "you until a clear image is viewed.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/72", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.422, "t": 523.287, "r": 385.698, "b": 522.016, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "2. If the image is unclear, adjust the", "text": "2. If the image is unclear, adjust the", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/73", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 521.676, "r": 385.612, "b": 520.406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "height of the elevator up or down,", "text": "height of the elevator up or down,", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/74", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 520.066, "r": 385.389, "b": 518.796, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "then turn the focusing knob again.", "text": "then turn the focusing knob again.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/75", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 516.846, "r": 377.35, "b": 515.576, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "ZOOM MAGNIFICATION", "text": "ZOOM MAGNIFICATION", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/76", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 515.235, "r": 387.359, "b": 513.965, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "1. Turn the zoom magnification knob to", "text": "1. Turn the zoom magnification knob to", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/77", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 513.625, "r": 387.748, "b": 512.355, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "the desired magnification and field of", "text": "the desired magnification and field of", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/78", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 512.015, "r": 364.169, "b": 510.745, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "view.", "text": "view.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/79", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 510.405, "r": 388.266, "b": 509.135, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "2. In most situations, it is recommended", "text": "2. In most situations, it is recommended", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/80", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 508.795, "r": 381.567, "b": 507.524, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "that you focus at the lowest", "text": "that you focus at the lowest", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/81", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.423, "t": 507.184, "r": 387.673, "b": 505.914, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "magnification, then move to a higher", "text": "magnification, then move to a higher", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/82", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 505.574, "r": 382.771, "b": 504.304, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "magnification and re-focus as", "text": "magnification and re-focus as", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/83", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 503.964, "r": 367.987, "b": 502.694, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "necessary.", "text": "necessary.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/84", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 502.354, "r": 387.601, "b": 501.084, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "3. If the image is not clear to both eyes", "text": "3. If the image is not clear to both eyes", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/85", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 500.744, "r": 389.075, "b": 499.473, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "at the same time, the diopter ring may", "text": "at the same time, the diopter ring may", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/86", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 499.133, "r": 373.137, "b": 497.863, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "need adjustment.", "text": "need adjustment.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/87", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 495.913, "r": 381.745, "b": 494.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "DIOPTER RING ADJUSTMENT", "text": "DIOPTER RING ADJUSTMENT", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/88", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 494.303, "r": 389.01, "b": 493.033, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 42]}], "orig": "1. To adjust the eyepiece for viewing with", "text": "1. To adjust the eyepiece for viewing with", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/89", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 492.692, "r": 382.733, "b": 491.422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 29]}], "orig": "or without eyeglasses and for", "text": "or without eyeglasses and for", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 491.082, "r": 388.701, "b": 489.812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "differences in acuity between the right", "text": "differences in acuity between the right", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/91", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 489.472, "r": 385.147, "b": 488.202, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "and left eyes, fol ow the fol owing", "text": "and left eyes, fol ow the fol owing", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/92", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 487.862, "r": 364.887, "b": 486.592, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "steps:", "text": "steps:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 486.252, "r": 387.708, "b": 484.981, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "a. Observe an image through the left", "text": "a. Observe an image through the left", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/94", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 484.641, "r": 387.857, "b": 483.371, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "eyepiece and bring a specific point", "text": "eyepiece and bring a specific point", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/95", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 483.031, "r": 385.414, "b": 481.761, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "into focus using the focus knob.", "text": "into focus using the focus knob.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/96", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.425, "t": 481.421, "r": 382.561, "b": 480.151, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "b. By turning the diopter ring", "text": "b. 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"parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 471.76, "r": 385.949, "b": 470.49, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "by turning the right diopter ring.", "text": "by turning the right diopter ring.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/103", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 470.149, "r": 387.137, "b": 468.879, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "d.With more than one viewer, each", "text": "d.With more than one viewer, each", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/104", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 468.539, "r": 384.369, "b": 467.269, "coord_origin": 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"#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 463.709, "r": 383.28, "b": 462.439, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "viewing set the diopter ring", "text": "viewing set the diopter ring", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/108", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 462.098, "r": 382.634, "b": 460.828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 28]}], "orig": "adjustments to that setting.", "text": "adjustments to that setting.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/109", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 458.878, "r": 375.677, "b": 457.608, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": 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Disconnect the power cord from the", "text": "1. Disconnect the power cord from the", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/111", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 455.658, "r": 372.014, "b": 454.387, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "electrical outlet.", "text": "electrical outlet.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/112", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 454.047, "r": 385.634, "b": 452.777, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "2. When the bulb is cool, remove the", "text": "2. When the bulb is cool, remove the", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/113", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 452.437, "r": 386.395, "b": 451.167, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "oblique il uminator cap and remove", "text": "oblique il uminator cap and remove", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/114", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.426, "t": 450.827, "r": 379.572, "b": 449.557, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "the halogen bulb with cap.", "text": "the halogen bulb with cap.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/115", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.427, "t": 449.217, "r": 384.511, "b": 447.947, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "3. Replace with a new halogen bulb.", "text": "3. Replace with a new halogen bulb.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 358.429, "t": 447.606, "r": 386.415, "b": 446.336, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "4. Open the window in the base plate", "text": "4. 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{"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.39, "t": 512.554, "r": 445.872, "b": 511.507, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Coarse", "text": "Coarse", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.39, "t": 511.374, "r": 448.223, "b": 510.327, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Adjustment", "text": "Adjustment", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 441.39, "t": 510.194, "r": 444.404, "b": 509.147, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Knob", "text": "Knob", "formatting": null, "hyperlink": null}, {"self_ref": 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a powerful fixed power compound", "text": "BARSKA Model AY11236 is a powerful fixed power compound", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/131", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.086, "t": 479.009, "r": 453.099, "b": 477.422, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "microscope designed for biological studies such as specimen", "text": "microscope designed for biological studies such as specimen", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/132", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.086, "t": 476.862, "r": 458.638, "b": 475.275, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "examination. It can also be used for examining bacteria and", "text": "examination. It can also be used for examining bacteria and", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/133", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.086, "t": 474.715, "r": 456.739, "b": 473.128, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "for general clinical and medical studies and other scientific uses.", "text": "for general clinical and medical studies and other scientific uses.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/134", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.624, "t": 470.698, "r": 427.775, "b": 467.84, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "CONSTRUCTION", "text": "CONSTRUCTION", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/135", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.086, "t": 465.054, "r": 458.024, "b": 463.467, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "BARSKA Model AY11236 is a fixed power compound microscope.", "text": "BARSKA Model AY11236 is a fixed power compound microscope.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/136", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.084, "t": 462.907, "r": 455.422, "b": 461.32, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "It is constructed with two optical paths at the same angle. It is", "text": "It is constructed with two optical paths at the same angle. It is", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/137", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 398.084, "t": 460.761, "r": 457.398, "b": 459.173, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "equipped with transmitted il umination. By using this instrument,", "text": "equipped with transmitted il umination. 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Indicative dates only, subject to change/Board confirmation", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/220", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.405, "t": 387.462, "r": 391.771, "b": 385.457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 87]}], "orig": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney", "text": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/221", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 329.405, "t": 385.428, "r": 369.653, "b": 383.423, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 49]}], "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text": "commencing at 10.30am on Tuesday 23 October 2012.", 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This chapter is a handy", "text": "some of the controls in your vehicle. This chapter is a handy", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/245", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.153, "t": 523.851, "r": 544.013, "b": 522.041, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "reference section that gives examples of the most common", "text": "reference section that gives examples of the most common", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/246", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.153, "t": 521.808, "r": 544.12, "b": 519.998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "signs, signals and road markings that keep traffic organized", "text": "signs, signals and road markings that keep traffic organized", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/247", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.153, "t": 519.765, "r": 515.411, "b": 517.955, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "and flowing smoothly.", "text": "and flowing smoothly.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/248", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.153, "t": 514.337, "r": 505.646, "b": 511.149, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Signs", "text": "Signs", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/249", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.153, "t": 510.004, "r": 543.93, "b": 508.195, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "There are three ways to read signs: by their shape, colour and", "text": "There are three ways to read signs: by their shape, colour and", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/250", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 498.153, "t": 507.961, "r": 545.678, "b": 506.152, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "the messages printed on them. 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"children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 478.152, "t": 526.674, "r": 493.781, "b": 524.009, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "in this chapter", "text": "in this chapter", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/323", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.955, "t": 198.596, "r": 379.821, "b": 189.529, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "KEYWORDS", "text": "KEYWORDS", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/324", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.955, "t": 183.90200000000004, "r": 559.186, "b": 165.14200000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "text": "PDF document conversion, layout segmentation, object-detection, data set, Machine Learning", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/325", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 317.66, "t": 151.26, "r": 404.654, "b": 144.63599999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "ACMReference Format:", "text": "ACMReference Format:", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/326", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 317.955, "t": 141.49700000000007, "r": 559.549, "b": 84.75, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 374]}], "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/327", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 53.798, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/328", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 53.798, "t": 705.202, "r": 156.529, "b": 696.135, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 INTRODUCTION", "text": "1 INTRODUCTION", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/329", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.529, "t": 680.586, "r": 303.017, "b": 563.196, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 702]}], "orig": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/330", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.529, "t": 560.038, "r": 295.564, "b": 289.224, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1578]}], "orig": "Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "text": "Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/331", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 53.592, "t": 286.066, "r": 295.564, "b": 212.51099999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 462]}], "orig": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/332", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 207.05099999999993, "r": 295.562, "b": 177.269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 149]}], "orig": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/333", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 174.17399999999998, "r": 294.263, "b": 155.35199999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 109]}], "orig": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/334", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 152.25700000000006, "r": 294.684, "b": 122.47500000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 180]}], "orig": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/335", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 64.708, "t": 119.38, "r": 295.564, "b": 100.55700000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 115]}], "orig": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/336", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 53.672, "t": 91.10299999999995, "r": 216.027, "b": 83.37099999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 56]}], "orig": "1 https://developer.ibm.com/exchanges/data/all/doclaynet", "text": "1 https://developer.ibm.com/exchanges/data/all/doclaynet", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/337", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 342.095, "t": 704.206, "r": 558.432, "b": 685.446, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "This enables experimentation with annotation uncertainty and quality control analysis.", "text": "This enables experimentation with annotation uncertainty and quality control analysis.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/338", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 2, "bbox": {"l": 328.865, "t": 682.351, "r": 559.721, "b": 630.652, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 280]}], "orig": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/339", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.623, "t": 623.594, "r": 559.19, "b": 571.957, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/340", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.731, "t": 568.799, "r": 559.582, "b": 484.286, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 506]}], "orig": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/341", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 469.853, "r": 421.744, "b": 460.786, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 RELATED WORK", "text": "2 RELATED WORK", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/342", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.525, "t": 445.237, "r": 559.716, "b": 327.847, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 655]}], "orig": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16].", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/343", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 324.689, "r": 559.186, "b": 240.17499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/344", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 225.74199999999996, "r": 477.457, "b": 216.67499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "3 THE DOCLAYNET DATASET", "text": "3 THE DOCLAYNET DATASET", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/345", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 201.12599999999998, "r": 559.713, "b": 116.61300000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 522]}], "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/346", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 317.955, "t": 113.45500000000004, "r": 558.204, "b": 83.73599999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 186]}], "orig": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/347", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/348", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/349", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 555.114, "r": 294.044, "b": 536.702, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "Figure 2: Distribution of DocLayNet pages across document categories.", "text": "Figure 2: Distribution of DocLayNet pages across document categories.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/350", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 237.113, "t": 657.464, "r": 262.976, "b": 650.725, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Patents", "text": "Patents", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/351", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 202.879, "t": 650.089, "r": 213.9, "b": 643.35, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "8%", "text": "8%", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/352", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 207.133, "t": 697.393, "r": 237.649, "b": 690.654, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Scientific", "text": "Scientific", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/353", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 184.403, "t": 671.869, "r": 199.665, "b": 665.13, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "17%", "text": "17%", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/354", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 88.288, "t": 676.196, "r": 118.804, "b": 669.457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "Financial", "text": "Financial", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/355", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 136.244, "t": 660.307, "r": 151.506, "b": 653.568, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "32%", "text": "32%", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/356", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 93.973, "t": 602.893, "r": 121.115, "b": 596.154, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Tenders", "text": "Tenders", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/357", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.624, "t": 620.323, "r": 150.645, "b": 613.585, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6%", "text": "6%", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/358", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 139.883, "t": 578.05, "r": 157.685, "b": 571.312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Laws", "text": "Laws", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/359", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 157.44, "t": 606.773, "r": 172.702, "b": 600.034, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "16%", "text": "16%", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/360", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 225.473, "t": 601.254, "r": 254.295, "b": 594.515, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Manuals", "text": "Manuals", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/361", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 194.407, "t": 619.429, "r": 209.669, "b": 612.691, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "21%", "text": "21%", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/362", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 509.766, "r": 294.274, "b": 425.253, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 513]}], "orig": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild\".", "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild\".", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/363", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.574, "t": 422.095, "r": 295.56, "b": 282.787, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 810]}], "orig": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/364", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.467, "t": 279.629, "r": 295.562, "b": 184.15700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 535]}], "orig": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/365", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 180.99900000000002, "r": 295.564, "b": 107.44499999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 413]}], "orig": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/366", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 53.798, "t": 91.673, "r": 195.79, "b": 83.94100000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "2 e.g. AAPL from https://www.annualreports.com/", "text": "2 e.g. AAPL from https://www.annualreports.com/", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/367", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.623, "t": 704.206, "r": 559.192, "b": 630.652, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 435]}], "orig": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/368", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.955, "t": 627.494, "r": 558.438, "b": 521.063, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 645]}], "orig": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/369", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 316.942, "t": 517.905, "r": 559.722, "b": 203.25400000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1854]}], "orig": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/370", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 317.955, "t": 184.212, "r": 470.213, "b": 175.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "4 ANNOTATION CAMPAIGN", "text": "4 ANNOTATION CAMPAIGN", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/371", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 317.686, "t": 159.596, "r": 559.714, "b": 86.04099999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 457]}], "orig": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/372", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 53.798, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/373", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.502, "t": 706.274, "r": 558.49, "b": 676.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 348]}], "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/374", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 53.798, "t": 237.21900000000005, "r": 295.649, "b": 185.93000000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 281]}], "orig": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/375", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.467, "t": 157.27800000000002, "r": 294.047, "b": 116.60000000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/376", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 53.798, "t": 113.44200000000001, "r": 295.558, "b": 83.72299999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 193]}, {"page_no": 4, "bbox": {"l": 53.798, "t": 113.44200000000001, "r": 295.558, "b": 83.72299999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [194, 567]}], "orig": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/377", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.955, "t": 413.737, "r": 559.713, "b": 285.388, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 746]}], "orig": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/378", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 317.623, "t": 282.23, "r": 559.718, "b": 99.08699999999999, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1159]}], "orig": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/379", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 317.955, "t": 90.976, "r": 369.246, "b": 83.24400000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "3 https://arxiv.org/", "text": "3 https://arxiv.org/", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/380", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/381", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/382", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 704.206, "r": 294.045, "b": 685.402, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/383", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 682.288, "r": 295.559, "b": 542.981, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 812]}], "orig": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/384", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 539.823, "r": 295.56, "b": 455.309, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 465]}], "orig": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/385", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 443.057, "r": 294.046, "b": 402.335, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 202]}], "orig": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/386", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 399.284, "r": 295.564, "b": 358.543, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 208]}], "orig": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/387", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 355.448, "r": 294.047, "b": 336.581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table .", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/388", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 333.531, "r": 294.046, "b": 314.708, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "(4) Connected sub-pictures are grouped together in one Picture object.", "text": "(4) Connected sub-pictures are grouped together in one Picture object.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/389", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 311.613, "r": 264.506, "b": 303.705, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "(5) Formula numbers are included in a Formula object.", "text": "(5) Formula numbers are included in a Formula object.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/390", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 64.708, "t": 300.591, "r": 294.046, "b": 270.87200000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 160]}], "orig": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/391", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.529, "t": 258.619, "r": 295.563, "b": 217.942, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 221]}], "orig": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/392", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 53.798, "t": 214.784, "r": 295.562, "b": 86.43499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 792]}], "orig": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/393", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.002, "t": 611.985, "r": 416.179, "b": 610.167, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/394", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 339.383, "t": 705.609, "r": 417.395, "b": 699.997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "Compliant with guidelines", "text": "Compliant with guidelines", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/395", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 451.428, "t": 705.609, "r": 545.834, "b": 699.997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "Plausible but invalid alternative", "text": "Plausible but invalid alternative", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/396", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 350.337, "t": 425.943, "r": 513.216, "b": 420.331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Borderline case: Two guideline-compliant alternatives", "text": "Borderline case: Two guideline-compliant alternatives", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/397", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.002, "t": 546.704, "r": 416.179, "b": 544.886, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "text": "03c31a2ee1ed1b583c28957f475ee545d144e1b5a264dc4dd068c8d2f6a64860", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/398", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 340.002, "t": 432.653, "r": 416.179, "b": 430.835, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "text": "1a5cd524f1844c1260c8e8c073e1f442423c264583212b0d0b6626fc780e6ed4", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/399", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.194, "t": 692.659, "r": 326.015, "b": 687.982, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/400", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.194, "t": 604.009, "r": 326.015, "b": 599.332, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/401", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.194, "t": 537.458, "r": 326.015, "b": 532.781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/402", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 322.194, "t": 423.915, "r": 326.015, "b": 419.238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "D", "text": "D", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/403", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 400.128, "t": 333.335, "r": 476.305, "b": 331.517, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 64]}], "orig": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "text": "05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/404", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 317.955, "t": 317.735, "r": 559.806, "b": 288.364, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 173]}], "orig": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/405", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.623, "t": 266.072, "r": 558.204, "b": 247.312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 123]}], "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/406", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 317.623, "t": 244.154, "r": 559.715, "b": 82.928, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 987]}], "orig": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/407", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 53.798, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/408", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.502, "t": 704.356, "r": 295.649, "b": 609.232, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 489]}], "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/409", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.529, "t": 420.642, "r": 295.556, "b": 215.58000000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1252]}], "orig": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/410", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 53.798, "t": 202.93200000000002, "r": 147.485, "b": 193.865, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 EXPERIMENTS", "text": "5 EXPERIMENTS", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/411", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 53.484, "t": 178.31600000000003, "r": 295.428, "b": 82.84400000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/412", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 512.213, "r": 559.806, "b": 449.965, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 322]}], "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/413", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 349.166, "t": 545.32, "r": 352.482, "b": 539.246, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "0", "text": "0", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/414", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 385.937, "t": 545.32, "r": 392.569, "b": 539.246, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/415", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", 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"content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.935, "t": 599.913, "r": 337.567, "b": 593.839, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "55", "text": "55", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/422", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.935, "t": 626.211, "r": 337.567, "b": 620.137, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/423", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.935, "t": 652.509, "r": 337.567, "b": 646.435, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/424", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 330.935, "t": 678.807, "r": 337.567, "b": 672.733, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/425", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 322.923, "t": 643.623, "r": 328.997, "b": 605.208, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "mAP 0.50:0.95", "text": "mAP 0.50:0.95", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/426", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 470.972, "t": 557.176, "r": 479.978, "b": 550.559, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "10 1", "text": "10 1", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/427", "parent": {"cref": 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"#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.911, "t": 607.558, "r": 411.543, "b": 601.483, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "60", "text": "60", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/431", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.911, "t": 623.336, "r": 411.543, "b": 617.262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "65", "text": "65", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/432", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 404.911, "t": 639.115, "r": 411.543, "b": 633.041, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "70", "text": "70", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/433", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 407.558, "r": 558.204, "b": 388.798, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 102]}], "orig": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/434", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.641, "t": 385.64, "r": 558.437, "b": 312.086, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 397]}], "orig": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16].", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/435", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 317.955, "t": 294.24, "r": 466.853, "b": 285.173, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Baselines for Object Detection", "text": "Baselines for Object Detection", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/436", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 317.749, "t": 279.5450000000001, "r": 558.431, "b": 85.44299999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1146]}], "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/437", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 731.44, "r": 347.017, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/438", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 365.757, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/439", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.502, "t": 704.356, "r": 295.649, "b": 664.026, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 205]}], "orig": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/440", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 471.492, "r": 131.056, "b": 462.425, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Learning Curve", "text": "Learning Curve", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/441", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 52.785, "t": 456.798, "r": 295.558, "b": 262.69499999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1157]}], "orig": "One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "text": "One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/442", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 53.798, "t": 248.55200000000002, "r": 164.329, "b": 239.485, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Impact of Class Labels", "text": "Impact of Class Labels", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/443", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 53.467, "t": 233.85799999999995, "r": 295.557, "b": 83.59100000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 910]}], "orig": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/444", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.659, "t": 704.356, "r": 559.807, "b": 664.026, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 183]}], "orig": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in /tildelow 10% point improvement.", "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in /tildelow 10% point improvement.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/445", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.686, "t": 460.166, "r": 559.585, "b": 375.653, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/446", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 361.667, "r": 549.861, "b": 352.6, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Impact of Document Split in Train and Test Set", "text": "Impact of Document Split in Train and Test Set", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/447", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.623, "t": 346.973, "r": 559.714, "b": 196.70600000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 853]}], "orig": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/448", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 317.955, "t": 182.72000000000003, "r": 418.548, "b": 173.65300000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Dataset Comparison", "text": "Dataset Comparison", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/449", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 317.686, "t": 168.02600000000007, "r": 559.188, "b": 83.46799999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 521]}], "orig": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/450", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 731.44, "r": 558.203, "b": 723.511, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/451", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.502, "t": 704.356, "r": 295.649, "b": 642.108, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 298]}], "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/452", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 400.712, "r": 294.047, "b": 348.968, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 295]}], "orig": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text .", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/453", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.467, "t": 345.855, "r": 295.559, "b": 206.54700000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 793]}], "orig": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/454", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 53.798, "t": 186.00099999999998, "r": 156.005, "b": 176.93399999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "Example Predictions", "text": "Example Predictions", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/455", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 53.529, "t": 171.30600000000004, "r": 295.558, "b": 86.793, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 481]}], "orig": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/456", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.955, "t": 705.202, "r": 405.73, "b": 696.135, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 CONCLUSION", "text": "6 CONCLUSION", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/457", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.955, "t": 690.508, "r": 559.714, "b": 605.994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 507]}], "orig": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/458", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.686, "t": 602.836, "r": 559.717, "b": 507.364, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 573]}], "orig": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/459", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 317.623, "t": 504.206, "r": 558.435, "b": 474.487, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 188]}], "orig": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/460", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 317.955, "t": 455.97, "r": 387.37, "b": 446.903, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "REFERENCES", "text": "REFERENCES", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/461", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 321.198, "t": 442.963, "r": 558.201, "b": 420.922, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 191]}], "orig": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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[], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 398.261, "t": 523.801, "r": 402.916, "b": 517.069, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "F", "text": "F", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/489", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 53.798, "t": 326.747, "r": 559.808, "b": 286.418, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 386]}], "orig": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "text": "Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/490", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 69.234, "t": 264.59899999999993, "r": 295.224, "b": 242.591, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 195]}], "orig": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.", "text": "Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/491", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 9, "bbox": {"l": 317.955, "t": 264.59899999999993, "r": 559.026, "b": 250.56100000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "[20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. 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{"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 8}, "9": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 9}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2206.01062.md b/tests/data/groundtruth/docling_v2/2206.01062.md index ba40b73a..c87289fb 100644 --- a/tests/data/groundtruth/docling_v2/2206.01062.md +++ b/tests/data/groundtruth/docling_v2/2206.01062.md @@ -1,224 +1,310 @@ -## DocLayNet: A Large Human-Annotated Dataset for +## DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis -Birgit Pfitzmann +Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com -Christoph Auer +Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com -Michele Dolfi +Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com -Ahmed S. Nassar +Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com + +Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com ## ABSTRACT -Accurate document layout analysis is a key requirement for high- +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. ## CCS CONCEPTS -• +· Informationsystems → Documentstructure ; · Appliedcomputing → Document analysis ; · Computing methodologies → Machine learning Computer vision ; ; Object detection ; -Permission to make digital or hard copies of part or all of this work for personal or https://doi.org/10.1145/3534678.3539043 +Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). -Peter Staar +KDD '22, August 14-18, 2022, Washington, DC, USA -Figure 1: +© 2022 Copyright held by the owner/author(s). + +ACM ISBN 978-1-4503-9385-0/22/08. + +https://doi.org/10.1145/3534678.3539043 + +Figure 1: Four examples of complex page layouts across different document categories ## KEYWORDS -PDF document conversion, layout segmentation, object-detection, +PDF document conversion, layout segmentation, object-detection, data set, Machine Learning ## ACMReference Format: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 -## 1 +## 1 INTRODUCTION -Despite the substantial improvements achieved with machine-learning +Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. -Akeyproblem in the process of document conversion is to under- +Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. -In this paper, we present the DocLayNet dataset. It provides page- +In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: -- (1) -- (2) -- (3) -- (4) +- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. +- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources. +- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. +- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation. -This enables experimentation with annotation uncertainty +This enables experimentation with annotation uncertainty and quality control analysis. -- (5) +- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. -All aspects outlined above are detailed in Section 3. In Section 4, +All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns. -In Section 5, we will present baseline accuracy numbers for a +In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery. -## 2 +## 2 RELATED WORK -While early approaches in document-layout analysis used rule- +While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]. -Lately, new types of ML models for document-layout analysis +Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish. -## 3 +## 3 THE DOCLAYNET DATASET -DocLayNet contains 80863 PDF pages. Among these, 7059 carry two +DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4. -In addition to open intellectual property constraints for the +In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents -Figure 2: Distribution of DocLayNet pages across document +Figure 2: Distribution of DocLayNet pages across document categories. -to a minimum, since they introduce difficulties in annotation (see +to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild". -The pages in DocLayNet can be grouped into six distinct cate- +The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. -We did not control the document selection with regard to lan- +We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. -To ensure that future benchmarks in the document-layout analy- +To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. -Table 1 shows the overall frequency and distribution of the labels +Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5. -In order to accommodate the different types of models currently +In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 × 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. -Despite being cost-intense and far less scalable than automation, +Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4. -## 4 +## 4 ANNOTATION CAMPAIGN -The annotation campaign was carried out in four phases. In phase +The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four, -Figure 3: Corpus Conversion Service annotation user inter- +Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. -Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % +| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | +|----------------|---------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------| +| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten | +| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a | +| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 | +| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a | +| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 | +| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 | +| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 | +| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 | +| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 | +| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 | +| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 | +| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 | +| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 | + +Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. -we distributed the annotation workload and performed continuous +we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. -Phase 1: Data selection and preparation. +Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. -include publication repositories such as arXiv +Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. -Preparation work included uploading and parsing the sourced +Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on -Phase 2: Label selection and guideline. +the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category. -the textual content of an element, which goes beyond visual layout +At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. -At first sight, the task of visual document-layout interpretation +Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: -Obviously, this inconsistency in annotations is not desirable for +- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object. +- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement. +- (3) For every Caption , there must be exactly one corresponding Picture or Table . +- (4) Connected sub-pictures are grouped together in one Picture object. +- (5) Formula numbers are included in a Formula object. +- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line. -- (1) -- (2) -- (3) -- (4) -- (5) -- (6) +The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference. -The complete annotation guideline is over 100 pages long and a - -Phase 3: Training. - -Figure 4: Examples of plausible annotation alternatives for +Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations -were carried out over a timeframe of 12 weeks, after which 8 of the +05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0 -Phase 4: Production annotation. +were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar. -Table 2: Prediction performance (mAP@0.5-0.95) of object +Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted -to avoid this at any cost in order to have clear, unbiased baseline +Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. -## 5 +| | human | MRCNN | MRCNN | FRCNN | YOLO | +|----------------|---------|---------|---------|---------|--------| +| | | R50 | R101 | R101 | v5x6 | +| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 | +| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 | +| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 | +| List-item | 87-88 | 81.2 | 80.8 | 81.0 | 86.2 | +| Page-footer | 93-94 | 61.6 | 59.3 | 58.9 | 61.1 | +| Page-header | 85-89 | 71.9 | 70.0 | 72.0 | 67.9 | +| Picture | 69-71 | 71.7 | 72.7 | 72.0 | 77.1 | +| Section-header | 83-84 | 67.6 | 69.3 | 68.4 | 74.6 | +| Table | 77-81 | 82.2 | 82.9 | 82.2 | 86.3 | +| Text | 84-86 | 84.6 | 85.8 | 85.4 | 88.1 | +| Title | 60-72 | 76.7 | 80.4 | 79.9 | 82.7 | +| All | 82-83 | 72.4 | 73.5 | 73.4 | 76.8 | -The primary goal of DocLayNet is to obtain high-quality ML models +to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. -Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask +## 5 EXPERIMENTS + +The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this + +Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. -paper and leave the detailed evaluation of more recent methods +paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work. -In this section, we will present several aspects related to the +In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]. ## Baselines for Object Detection -In Table 2, we present baseline experiments (given in mAP) on Mask +In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document. -Table 3: Performance of a Mask R-CNN R50 network in +Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. + +| Class-count | 11 | 6 | 5 | 4 | +|----------------|------|---------|---------|---------| +| Caption | 68 | Text | Text | Text | +| Footnote | 71 | Text | Text | Text | +| Formula | 60 | Text | Text | Text | +| List-item | 81 | Text | 82 | Text | +| Page-footer | 62 | 62 | - | - | +| Page-header | 72 | 68 | - | - | +| Picture | 72 | 72 | 72 | 72 | +| Section-header | 68 | 67 | 69 | 68 | +| Table | 82 | 83 | 82 | 82 | +| Text | 85 | 84 | 84 | 84 | +| Title | 77 | Sec.-h. | Sec.-h. | Sec.-h. | +| Overall | 72 | 73 | 78 | 77 | ## Learning Curve -One of the fundamental questions related to any dataset is if it is +One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles. ## Impact of Class Labels -The choice and number of labels can have a significant effect on +The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of -Table 4: Performance of a Mask R-CNN R50 network with +Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in /tildelow 10% point improvement. -lists in PubLayNet (grouped list-items) versus DocLayNet (separate +| Class-count | 11 | 11 | 5 | 5 | +|----------------|------|------|-----|------| +| Split | Doc | Page | Doc | Page | +| Caption | 68 | 83 | | | +| Footnote | 71 | 84 | | | +| Formula | 60 | 66 | | | +| List-item | 81 | 88 | 82 | 88 | +| Page-footer | 62 | 89 | | | +| Page-header | 72 | 90 | | | +| Picture | 72 | 82 | 72 | 82 | +| Section-header | 68 | 83 | 69 | 83 | +| Table | 82 | 89 | 82 | 90 | +| Text | 85 | 91 | 84 | 90 | +| Title | 77 | 81 | | | +| All | 72 | 84 | 78 | 87 | + +lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded. ## Impact of Document Split in Train and Test Set -Many documents in DocLayNet have a unique styling. In order +Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains ˜ 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided. ## Dataset Comparison -Throughout this paper, we claim that DocLayNet's wider variety of +Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , -Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask +Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. -Section-header +| | | Testing on | Testing on | Testing on | +|-----------------|------------|--------------|--------------|--------------| +| Training on | labels | PLN | DB | DLN | +| PubLayNet (PLN) | Figure | 96 | 43 | 23 | +| PubLayNet (PLN) | Sec-header | 87 | - | 32 | +| | Table | 95 | 24 | 49 | +| | Text | 96 | - | 42 | +| | total | 93 | 34 | 30 | +| DocBank (DB) | Figure | 77 | 71 | 31 | +| DocBank (DB) | Table | 19 | 65 | 22 | +| DocBank (DB) | total | 48 | 68 | 27 | +| DocLayNet (DLN) | Figure | 67 | 51 | 72 | +| DocLayNet (DLN) | Sec-header | 53 | - | 68 | +| | Table | 87 | 43 | 82 | +| | Text | 77 | - | 84 | +| | total | 59 | 47 | 78 | -For comparison of DocBank with DocLayNet, we trained only +Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text . + +For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. ## Example Predictions -To conclude this section, we illustrate the quality of layout predic- +To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence. -## 6 +## 6 CONCLUSION -In this paper, we presented the DocLayNet dataset. It provides the +In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. -From the dataset, we have derived on the one hand reference +From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. 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With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 293.054, "r_x1": 295.346, "r_y1": 293.054, "r_x2": 295.346, "r_y2": 285.253, "r_x3": 53.798, "r_y3": 285.253, "coord_origin": "TOPLEFT"}, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 304.013, "r_x1": 294.047, "r_y1": 304.013, "r_x2": 294.047, "r_y2": 296.212, "r_x3": 53.798, "r_y3": 296.212, "coord_origin": "TOPLEFT"}, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.972, "r_x1": 294.046, "r_y1": 314.972, "r_x2": 294.046, "r_y2": 307.171, "r_x3": 53.798, "r_y3": 307.171, "coord_origin": "TOPLEFT"}, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, 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not made or distributed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 656.741, "r_x1": 294.045, "r_y1": 656.741, "r_x2": 294.045, "r_y2": 650.673, "r_x3": 53.798, "r_y3": 650.673, "coord_origin": "TOPLEFT"}, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "orig": "for profit or commercial advantage and that copies bear this notice and the full citation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.711, "r_x1": 295.118, "r_y1": 664.711, "r_x2": 295.118, "r_y2": 658.643, "r_x3": 53.798, "r_y3": 658.643, "coord_origin": "TOPLEFT"}, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.681, "r_x1": 187.723, "r_y1": 672.681, "r_x2": 187.723, "r_y2": 666.613, "r_x3": 53.798, "r_y3": 666.613, "coord_origin": "TOPLEFT"}, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 681.481, "r_x1": 197.863, "r_y1": 681.481, "r_x2": 197.863, "r_y2": 675.331, "r_x3": 53.798, "r_y3": 675.331, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.317, "r_y0": 690.215, "r_x1": 186.746, "r_y1": 690.215, "r_x2": 186.746, "r_y2": 684.147, "r_x3": 53.317, "r_y3": 684.147, "coord_origin": "TOPLEFT"}, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.554, "r_y0": 698.186, "r_x1": 157.031, "r_y1": 698.186, "r_x2": 157.031, "r_y2": 692.1179999999999, "r_x3": 53.554, "r_y3": 692.1179999999999, "coord_origin": "TOPLEFT"}, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 706.156, "r_x1": 166.941, "r_y1": 706.156, "r_x2": 166.941, "r_y2": 700.088, "r_x3": 53.798, "r_y3": 700.088, "coord_origin": "TOPLEFT"}, "text": "https://doi.org/10.1145/3534678.3539043", "orig": "https://doi.org/10.1145/3534678.3539043", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.87, "r_y0": 353.853, "r_x1": 330.412, "r_y1": 353.853, "r_x2": 330.412, "r_y2": 352.266, "r_x3": 327.87, "r_y3": 352.266, "coord_origin": "TOPLEFT"}, "text": "13", "orig": "13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 333.231, "r_x1": 351.161, "r_y1": 333.231, "r_x2": 351.161, "r_y2": 331.961, "r_x3": 327.83, "r_y3": 331.961, "coord_origin": "TOPLEFT"}, "text": "USING THE VERTICAL TUBE -", "orig": "USING THE VERTICAL TUBE -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 334.841, "r_x1": 348.305, "r_y1": 334.841, "r_x2": 348.305, "r_y2": 333.571, "r_x3": 327.83, "r_y3": 333.571, "coord_origin": "TOPLEFT"}, "text": "MODELS AY11230/11234", "orig": "MODELS AY11230/11234", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 337.855, "r_x1": 350.743, "r_y1": 337.855, "r_x2": 350.743, "r_y2": 336.744, "r_x3": 327.83, "r_y3": 336.744, "coord_origin": "TOPLEFT"}, "text": "1. 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Make su", "orig": "3. 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The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.632, "r_y0": 278.03499999999997, "r_x1": 353.135, "r_y1": 278.03499999999997, "r_x2": 353.135, "r_y2": 276.765, "r_x3": 352.632, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.633, "r_y0": 292.527, "r_x1": 353.137, "r_y1": 292.527, "r_x2": 353.137, "r_y2": 291.256, "r_x3": 352.633, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 295.747, "r_x1": 327.385, "r_y1": 295.747, "r_x2": 327.385, "r_y2": 294.477, "r_x3": 326.882, "r_y3": 294.477, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.638, "r_y0": 326.341, "r_x1": 353.141, "r_y1": 326.341, "r_x2": 353.141, "r_y2": 325.071, "r_x3": 352.638, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.175, "r_y0": 284.476, "r_x1": 384.678, "r_y1": 284.476, "r_x2": 384.678, "r_y2": 283.205, "r_x3": 384.175, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.176, "r_y0": 287.696, "r_x1": 384.68, "r_y1": 287.696, "r_x2": 384.68, "r_y2": 286.426, "r_x3": 384.176, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.176, "r_y0": 300.578, "r_x1": 384.68, "r_y1": 300.578, "r_x2": 384.68, "r_y2": 299.308, "r_x3": 384.176, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.177, "r_y0": 311.849, "r_x1": 384.681, "r_y1": 311.849, "r_x2": 384.681, "r_y2": 310.579, "r_x3": 384.177, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.177, "r_y0": 329.561, "r_x1": 384.681, "r_y1": 329.561, "r_x2": 384.681, "r_y2": 328.291, "r_x3": 384.177, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, "r_x3": 358.426, "r_y3": 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. 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404.538, "coord_origin": "TOPLEFT"}, "text": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney ", "orig": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 408.577, "r_x1": 369.653, "r_y1": 408.577, "r_x2": 369.653, "r_y2": 406.572, "r_x3": 329.405, "r_y3": 406.572, "coord_origin": "TOPLEFT"}, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 465.415, "r_x1": 379.529, "r_y1": 465.415, "r_x2": 379.529, "r_y2": 460.96, "r_x3": 363.545, "r_y3": 460.96, "coord_origin": "TOPLEFT"}, "text": "Yesterday", "orig": "Yesterday", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 468.914, "r_x1": 391.382, "r_y1": 468.914, "r_x2": 391.382, "r_y2": 466.909, "r_x3": 363.545, "r_y3": 466.909, "coord_origin": "TOPLEFT"}, "text": "Established in Sydney in 1837, and then ", "orig": "Established in Sydney in 1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 470.947, "r_x1": 395.018, "r_y1": 470.947, "r_x2": 395.018, "r_y2": 468.942, "r_x3": 363.545, "r_y3": 468.942, "coord_origin": "TOPLEFT"}, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, 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Australian households. ", "orig": "electricity needs of Australian households. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 479.082, "r_x1": 394.115, "r_y1": 479.082, "r_x2": 394.115, "r_y2": 477.076, "r_x3": 363.545, "r_y3": 477.076, "coord_origin": "TOPLEFT"}, "text": "In 1841, when AGL supplied the gas to light ", "orig": "In 1841, when AGL supplied the gas to light ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 481.115, "r_x1": 393.759, "r_y1": 481.115, "r_x2": 393.759, "r_y2": 479.11, "r_x3": 363.545, "r_y3": 479.11, "coord_origin": "TOPLEFT"}, "text": "the first public street lamp, it was reported ", "orig": "the first public street lamp, it was reported ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 483.149, "r_x1": 390.498, "r_y1": 483.149, "r_x2": 390.498, "r_y2": 481.144, "r_x3": 363.545, "r_y3": 481.144, "coord_origin": "TOPLEFT"}, "text": "in the Sydney Gazette as a 'wonderful ", "orig": "in the Sydney Gazette as a 'wonderful ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 485.182, "r_x1": 395.71, "r_y1": 485.182, "r_x2": 395.71, "r_y2": 483.177, "r_x3": 363.545, "r_y3": 483.177, "coord_origin": "TOPLEFT"}, "text": "achievement of scientific knowledge, assisted ", "orig": "achievement of scientific knowledge, assisted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 487.216, "r_x1": 394.273, "r_y1": 487.216, "r_x2": 394.273, "r_y2": 485.211, "r_x3": 363.545, "r_y3": 485.211, "coord_origin": "TOPLEFT"}, "text": "by mechanical ingenuity.' Within two years, ", "orig": "by mechanical ingenuity.' 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"r_y1": 441.009, "r_x2": 372.166, "r_y2": 431.094, "r_x3": 329.405, "r_y3": 431.094, "coord_origin": "TOPLEFT"}, "text": "175years of ", "orig": "175years of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.994, "r_y0": 441.009, "r_x1": 342.695, "r_y1": 441.009, "r_x2": 342.695, "r_y2": 431.094, "r_x3": 340.994, "r_y3": 431.094, "coord_origin": "TOPLEFT"}, "text": "\u00a0", "orig": "\u00a0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 451.181, "r_x1": 385.754, "r_y1": 451.181, "r_x2": 385.754, "r_y2": 441.267, "r_x3": 329.405, "r_y3": 441.267, "coord_origin": "TOPLEFT"}, "text": "lookingforward.", "orig": "lookingforward.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 497.777, "r_y0": 254.86599999999999, "r_x1": 542.884, "r_y1": 254.86599999999999, "r_x2": 542.884, "r_y2": 251.74699999999996, "r_x3": 497.777, "r_y3": 251.74699999999996, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings", "orig": "signs, signals and road markings", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.307, "r_y0": 254.90700000000004, "r_x1": 492.1, "r_y1": 254.90700000000004, "r_x2": 492.1, "r_y2": 251.788, "r_x3": 490.307, "r_y3": 251.788, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 265.87300000000005, "r_x1": 500.04, "r_y1": 265.87300000000005, "r_x2": 500.04, "r_y2": 264.063, "r_x3": 498.153, "r_y3": 264.063, "coord_origin": "TOPLEFT"}, "text": "In ", "orig": "In ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.056, "r_y0": 265.81600000000003, "r_x1": 524.387, "r_y1": 265.81600000000003, "r_x2": 524.387, "r_y2": 264.034, "r_x3": 500.056, "r_y3": 264.034, "coord_origin": "TOPLEFT"}, "text": "chapter 2, you and your vehicle", "orig": "chapter 2, you and your vehicle", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 524.37, "r_y0": 265.87300000000005, "r_x1": 539.891, "r_y1": 265.87300000000005, "r_x2": 539.891, "r_y2": 264.063, "r_x3": 524.37, "r_y3": 264.063, "coord_origin": "TOPLEFT"}, "text": ", you learned about ", "orig": ", you learned about ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 267.91600000000005, "r_x1": 544.504, "r_y1": 267.91600000000005, "r_x2": 544.504, "r_y2": 266.106, "r_x3": 498.153, "r_y3": 266.106, "coord_origin": "TOPLEFT"}, "text": "some of the controls in your vehicle. This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 95.86500000000001, "r_x1": 156.529, "r_y1": 95.86500000000001, "r_x2": 156.529, "r_y2": 86.798, "r_x3": 70.314, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 130.17399999999998, "r_x1": 295.557, "r_y1": 130.17399999999998, "r_x2": 295.557, "r_y2": 122.37300000000005, "r_x3": 53.529, "r_y3": 122.37300000000005, "coord_origin": "TOPLEFT"}, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 141.13300000000004, "r_x1": 294.046, "r_y1": 141.13300000000004, "r_x2": 294.046, "r_y2": 133.332, "r_x3": 53.798, "r_y3": 133.332, "coord_origin": "TOPLEFT"}, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 152.091, "r_x1": 294.047, "r_y1": 152.091, "r_x2": 294.047, "r_y2": 144.28999999999996, "r_x3": 53.798, "r_y3": 144.28999999999996, "coord_origin": "TOPLEFT"}, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 625.734, "r_x1": 167.917, "r_y1": 625.734, "r_x2": 167.917, "r_y2": 617.826, "r_x3": 78.207, "r_y3": 617.826, "coord_origin": "TOPLEFT"}, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.335, "r_y0": 625.69, "r_x1": 170.493, "r_y1": 625.69, "r_x2": 170.493, "r_y2": 617.889, "r_x3": 168.335, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.981, "r_y0": 625.69, "r_x1": 294.263, "r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 647.6510000000001, "r_x1": 143.517, "r_y1": 647.6510000000001, "r_x2": 143.517, "r_y2": 639.7429999999999, "r_x3": 78.207, "r_y3": 639.7429999999999, "coord_origin": "TOPLEFT"}, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.02, "r_y0": 647.607, "r_x1": 146.178, "r_y1": 647.607, "r_x2": 146.178, "r_y2": 639.806, "r_x3": 144.02, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 117.55700000000002, "r_x1": 487.253, "r_y1": 117.55700000000002, "r_x2": 487.253, "r_y2": 109.649, "r_x3": 342.364, "r_y3": 109.649, "coord_origin": "TOPLEFT"}, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.759, "r_y0": 117.51300000000003, "r_x1": 489.867, "r_y1": 117.51300000000003, "r_x2": 489.867, "r_y2": 109.71199999999999, "r_x3": 487.759, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 542.158, "r_y0": 642.554, "r_x1": 559.099, "r_y1": 642.554, "r_x2": 559.099, "r_y2": 634.646, "r_x3": 542.158, "r_y3": 634.646, "coord_origin": "TOPLEFT"}, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 653.513, "r_x1": 331.863, "r_y1": 653.513, "r_x2": 331.863, "r_y2": 645.605, "r_x3": 317.955, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.863, "r_y0": 653.469, "r_x1": 333.84, "r_y1": 653.469, "r_x2": 333.84, "r_y2": 645.668, "r_x3": 331.863, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.064, "r_y0": 653.513, "r_x1": 366.054, "r_y1": 653.513, "r_x2": 366.054, "r_y2": 645.605, "r_x3": 336.064, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "Footnote", "orig": "Footnote", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 366.053, "r_y0": 653.469, "r_x1": 368.03, "r_y1": 653.469, "r_x2": 368.03, "r_y2": 645.668, "r_x3": 366.053, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.254, "r_y0": 653.513, "r_x1": 436.195, "r_y1": 653.513, "r_x2": 436.195, "r_y2": 645.605, "r_x3": 370.254, 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property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.3050000000001, "r_x1": 558.199, "r_y1": 697.3050000000001, "r_x2": 558.199, "r_y2": 689.504, "r_x3": 317.955, "r_y3": 689.504, "coord_origin": "TOPLEFT"}, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.264, "r_x1": 558.204, "r_y1": 708.264, "r_x2": 558.204, "r_y2": 700.463, "r_x3": 317.955, "r_y3": 700.463, "coord_origin": "TOPLEFT"}, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 246.244, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 59.405, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.71199999999999, "r": 338.378, "b": 117.51300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 111.41399999999999, "r": 303.017, "b": 119.21500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 327.918, "t": 168.40599999999995, "r": 559.19, "b": 176.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 223.20100000000002, "r": 558.198, "b": 231.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 63.761, "t": 231.962, "r": 295.564, "b": 239.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 323.562, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.713, "b": 354.564, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 467.311, "r": 558.198, "b": 475.112, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 505.934, "r": 295.564, "b": 513.735, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 323.562, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 585.012, "r": 74.221, "b": 592.813, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 558.202, "b": 598.675, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.889, "r": 74.221, "b": 625.69, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.806, "r": 74.221, "b": 647.607, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.683, "r": 74.221, "b": 680.484, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 327.918, "t": 678.545, "r": 558.198, "b": 686.346, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 56.22, "b": 705.664, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 246.244, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 59.405, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This enables experimentation with annotation uncertainty"}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.71199999999999, "r": 338.378, "b": 117.51300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5)"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 111.41399999999999, "r": 303.017, "b": 119.21500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning"}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 327.918, "t": 168.40599999999995, "r": 559.19, "b": 176.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,"}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 223.20100000000002, "r": 558.198, "b": 231.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a"}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 63.761, "t": 231.962, "r": 295.564, "b": 239.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Akeyproblem in the process of document conversion is to under-"}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 323.562, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.713, "b": 354.564, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While early approaches in document-layout analysis used rule-"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 467.311, "r": 558.198, "b": 475.112, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis"}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 505.934, "r": 295.564, "b": 513.735, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides page-"}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 323.562, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 585.012, "r": 74.221, "b": 592.813, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1)"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 558.202, "b": 598.675, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two"}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.889, "r": 74.221, "b": 625.69, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2)"}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.806, "r": 74.221, "b": 647.607, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, 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0.9749757051467896, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In addition to open intellectual property constraints for the"}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 56.22, "b": 705.664, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 59.405, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This enables experimentation with annotation uncertainty"}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.71199999999999, "r": 338.378, "b": 117.51300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5)"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 111.41399999999999, "r": 303.017, "b": 119.21500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning"}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 327.918, "t": 168.40599999999995, "r": 559.19, "b": 176.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,"}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 223.20100000000002, "r": 558.198, "b": 231.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a"}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 63.761, "t": 231.962, "r": 295.564, "b": 239.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Akeyproblem in the process of document conversion is to under-"}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 323.562, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.713, "b": 354.564, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While early approaches in document-layout analysis used rule-"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 467.311, "r": 558.198, "b": 475.112, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis"}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 505.934, "r": 295.564, "b": 513.735, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. 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Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two"}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.889, "r": 74.221, "b": 625.69, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2)"}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.806, "r": 74.221, "b": 647.607, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, 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As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 311.953, "r_x1": 98.881, "r_y1": 311.953, "r_x2": 98.881, "r_y2": 304.152, "r_x3": 53.798, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.071, "r_y0": 310.088, "r_x1": 104.774, "r_y1": 310.088, "r_x2": 104.774, "r_y2": 306.08, "r_x3": 99.071, "r_y3": 306.08, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.464, "r_y0": 311.953, "r_x1": 294.262, "r_y1": 311.953, "r_x2": 294.262, "r_y2": 304.152, "r_x3": 107.464, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "10 pages) with technical content, dense in complex", "orig": "10 pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 322.912, "r_x1": 294.047, "r_y1": 322.912, "r_x2": 294.047, "r_y2": 315.111, "r_x3": 53.798, "r_y3": 315.111, "coord_origin": "TOPLEFT"}, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 388.665, "r_x1": 105.905, "r_y1": 388.665, "r_x2": 105.905, "r_y2": 380.864, "r_x3": 53.798, "r_y3": 380.864, "coord_origin": "TOPLEFT"}, "text": "gories, namely", "orig": "gories, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.754, "r_y0": 388.709, "r_x1": 167.497, "r_y1": 388.709, "r_x2": 167.497, "r_y2": 380.801, "r_x3": 107.754, "r_y3": 380.801, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.496, "r_y0": 388.665, "r_x1": 169.429, "r_y1": 388.665, "r_x2": 169.429, "r_y2": 380.864, "r_x3": 167.496, "r_y3": 380.864, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.281, "r_y0": 388.709, "r_x1": 264.553, "r_y1": 388.709, "r_x2": 264.553, "r_y2": 380.801, "r_x3": 171.281, "r_y3": 380.801, "coord_origin": "TOPLEFT"}, "text": "Manuals Scientific Articles", "orig": "Manuals Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 201.455, "r_y0": 388.665, "r_x1": 203.388, 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"r_y1": 399.668, "r_x2": 94.9, "r_y2": 391.76, "r_x3": 53.798, "r_y3": 391.76, "coord_origin": "TOPLEFT"}, "text": "Regulations", "orig": "Regulations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 94.9, "r_y0": 399.624, "r_x1": 96.863, "r_y1": 399.624, "r_x2": 96.863, "r_y2": 391.823, "r_x3": 94.9, "r_y3": 391.823, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.109, "r_y0": 399.668, "r_x1": 124.723, "r_y1": 399.668, "r_x2": 124.723, "r_y2": 391.76, "r_x3": 99.109, "r_y3": 391.76, "coord_origin": "TOPLEFT"}, "text": "Patents", "orig": "Patents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 127.179, "r_y0": 399.624, "r_x1": 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For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. In phase four,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 87.79399999999998, "r": 558.201, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 327.918, "t": 164.50599999999997, "r": 558.438, "b": 172.30700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 236.88599999999997, "r": 294.044, "b": 244.33900000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 244.33900000000006, "r_x1": 294.044, "r_y1": 244.33900000000006, "r_x2": 294.044, "r_y2": 236.88599999999997, "r_x3": 53.798, "r_y3": 236.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 327.918, "t": 274.095, "r": 559.185, "b": 281.896, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 282.234, "r": 294.046, "b": 290.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 63.761, "t": 369.905, "r": 295.559, "b": 377.706, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 63.761, "t": 512.371, "r": 295.559, "b": 520.172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 323.562, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 63.761, "t": 611.001, "r": 295.564, "b": 618.802, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 558.201, "b": 640.205, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 56.346, "b": 705.094, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 87.79399999999998, "r": 558.201, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels"}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 327.918, "t": 164.50599999999997, "r": 558.438, "b": 172.30700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In order to accommodate the different types of models currently"}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 236.88599999999997, "r": 294.044, "b": 244.33900000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 244.33900000000006, "r_x1": 294.044, "r_y1": 244.33900000000006, "r_x2": 294.044, "r_y2": 236.88599999999997, "r_x3": 53.798, "r_y3": 236.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document"}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 327.918, "t": 274.095, "r": 559.185, "b": 281.896, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation,"}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 282.234, "r": 294.046, "b": 290.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 63.761, "t": 369.905, "r": 295.559, "b": 377.706, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct cate-"}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 63.761, "t": 512.371, "r": 295.559, "b": 520.172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We did not control the document selection with regard to lan-"}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 323.562, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 63.761, "t": 611.001, "r": 295.564, "b": 618.802, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analy-"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 558.201, "b": 640.205, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 56.346, "b": 705.094, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 327.918, "t": 87.79399999999998, "r": 558.201, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels"}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 327.918, "t": 164.50599999999997, "r": 558.438, "b": 172.30700000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In order to accommodate the different types of models currently"}, {"label": "caption", "id": 9, "page_no": 2, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 236.88599999999997, "r": 294.044, "b": 244.33900000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9425410628318787, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 244.33900000000006, "r_x1": 294.044, "r_y1": 244.33900000000006, "r_x2": 294.044, "r_y2": 236.88599999999997, "r_x3": 53.798, "r_y3": 236.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document"}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 327.918, "t": 274.095, "r": 559.185, "b": 281.896, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation,"}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 282.234, "r": 294.046, "b": 290.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see"}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 63.761, "t": 369.905, "r": 295.559, "b": 377.706, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct cate-"}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 63.761, "t": 512.371, "r": 295.559, "b": 520.172, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We did not control the document selection with regard to lan-"}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 323.562, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 63.761, "t": 611.001, "r": 295.564, "b": 618.802, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analy-"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 558.201, "b": 640.205, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase"}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 56.346, "b": 705.094, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. 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"coord_origin": "TOPLEFT"}, "text": "71-76", "orig": "71-76", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.47, "r_y0": 291.718, "r_x1": 507.178, "r_y1": 291.718, "r_x2": 507.178, "r_y2": 283.917, "r_x3": 487.47, "r_y3": 283.917, "coord_origin": "TOPLEFT"}, "text": "68-85", "orig": "68-85", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 573.193, "r_x1": 295.649, "r_y1": 573.193, "r_x2": 295.649, "r_y2": 565.74, "r_x3": 53.798, "r_y3": 565.74, "coord_origin": "TOPLEFT"}, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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317.955, "r_y3": 575.523, "coord_origin": "TOPLEFT"}, "text": "Critical factors that were considered for the choice of these class", "orig": "Critical factors that were considered for the choice of these class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 594.283, "r_x1": 558.431, "r_y1": 594.283, "r_x2": 558.431, "r_y2": 586.482, "r_x3": 317.955, "r_y3": 586.482, "coord_origin": "TOPLEFT"}, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 605.242, "r_x1": 558.369, "r_y1": 605.242, "r_x2": 558.369, "r_y2": 597.441, "r_x3": 317.955, "r_y3": 597.441, "coord_origin": "TOPLEFT"}, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 660.037, "r_x1": 436.906, "r_y1": 660.037, "r_x2": 436.906, "r_y2": 652.236, "r_x3": 317.955, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.138, "r_y0": 660.081, "r_x1": 469.691, "r_y1": 660.081, "r_x2": 469.691, "r_y2": 652.173, "r_x3": 439.138, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.429, "r_y0": 660.037, "r_x1": 493.973, "r_y1": 660.037, "r_x2": 493.973, "r_y2": 652.236, "r_x3": 472.429, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 496.207, "r_y0": 660.081, "r_x1": 558.2, "r_y1": 660.081, "r_x2": 558.2, "r_y2": 652.173, "r_x3": 496.207, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 670.996, "r_x1": 558.206, "r_y1": 670.996, "r_x2": 558.206, "r_y2": 663.1949999999999, "r_x3": 317.955, "r_y3": 663.1949999999999, "coord_origin": "TOPLEFT"}, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.954, "r_x1": 447.652, "r_y1": 681.954, "r_x2": 447.652, "r_y2": 674.153, "r_x3": 317.955, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.86, "r_y0": 681.998, "r_x1": 474.314, "r_y1": 681.998, "r_x2": 474.314, "r_y2": 674.09, "r_x3": 449.86, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.172, "r_y0": 681.954, "r_x1": 490.397, "r_y1": 681.954, "r_x2": 490.397, "r_y2": 674.153, "r_x3": 477.172, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.606, "r_y0": 681.998, "r_x1": 528.299, "r_y1": 681.998, "r_x2": 528.299, "r_y2": 674.09, "r_x3": 492.606, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.299, "r_y0": 681.954, "r_x1": 530.232, "r_y1": 681.954, "r_x2": 530.232, "r_y2": 674.153, "r_x3": 528.299, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.438, "r_y0": 681.954, "r_x1": 558.201, "r_y1": 681.954, "r_x2": 558.201, "r_y2": 674.153, "r_x3": 532.438, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "as seen", "orig": "as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 692.913, "r_x1": 558.204, "r_y1": 692.913, "r_x2": 558.204, "r_y2": 685.112, "r_x3": 317.955, "r_y3": 685.112, "coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 246.244, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.502, "t": 85.726, "r": 558.49, "b": 93.17899999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 312.51, "r": 481.034, "b": 320.311, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 378.263, "r": 558.206, "b": 386.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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Along with the frequency of each class label, we present the relative occurrence (as %"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 312.51, "r": 481.034, "b": 320.311, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "include publication repositories such as arXiv"}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 327.918, "t": 378.263, "r": 558.206, "b": 386.064, "coord_origin": "TOPLEFT"}, "confidence": 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"coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 2: Label selection and guideline."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 554.781, "r": 295.649, "b": 562.234, "coord_origin": "TOPLEFT"}, "confidence": 0.810490071773529, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user inter-"}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 642.523, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "we distributed the annotation workload and performed continuous"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 678.782, "r": 226.725, "b": 686.235, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": 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"coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 2: Label selection and guideline."}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 554.781, "r": 295.649, "b": 562.234, "coord_origin": "TOPLEFT"}, "confidence": 0.810490071773529, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user inter-"}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 642.523, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "we distributed the annotation workload and performed continuous"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 63.761, "t": 678.782, "r": 226.725, "b": 686.235, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": 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USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. 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list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 389.621, "r_x1": 91.594, "r_y1": 389.621, "r_x2": 91.594, "r_y2": 381.82, "r_x3": 78.207, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.835, "r_y0": 389.665, "r_x1": 106.881, "r_y1": 389.665, "r_x2": 106.881, "r_y2": 381.757, "r_x3": 93.835, "r_y3": 381.757, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 109.629, "r_y0": 389.621, "r_x1": 133.722, "r_y1": 389.621, "r_x2": 133.722, "r_y2": 381.82, "r_x3": 109.629, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 400.58, "r_x1": 74.221, "r_y1": 400.58, "r_x2": 74.221, "r_y2": 392.779, "r_x3": 64.708, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 400.58, "r_x1": 84.351, "r_y1": 400.58, "r_x2": 84.351, "r_y2": 392.779, "r_x3": 78.207, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 86.584, "r_y0": 400.624, "r_x1": 118.013, "r_y1": 400.624, "r_x2": 118.013, "r_y2": 392.716, "r_x3": 86.584, "r_y3": 392.716, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.269, "r_y0": 400.58, "r_x1": 295.557, "r_y1": 400.58, "r_x2": 295.557, "r_y2": 392.779, "r_x3": 120.269, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 433.457, "r_x1": 192.009, "r_y1": 433.457, "r_x2": 192.009, "r_y2": 425.656, "r_x3": 78.207, "r_y3": 425.656, "coord_origin": "TOPLEFT"}, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 444.416, "r_x1": 74.221, "r_y1": 444.416, "r_x2": 74.221, "r_y2": 436.615, "r_x3": 64.708, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 444.416, "r_x1": 112.616, "r_y1": 444.416, "r_x2": 112.616, "r_y2": 436.615, "r_x3": 78.207, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 114.861, "r_y0": 444.46, "r_x1": 142.612, "r_y1": 444.46, "r_x2": 142.612, "r_y2": 436.552, "r_x3": 114.861, "r_y3": 436.552, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.612, "r_y0": 444.416, "r_x1": 144.557, "r_y1": 444.416, "r_x2": 144.557, "r_y2": 436.615, "r_x3": 142.612, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.794, "r_y0": 444.416, "r_x1": 294.047, "r_y1": 444.416, "r_x2": 294.047, "r_y2": 436.615, "r_x3": 146.794, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "there must be exactly one corresponding", "orig": "there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 455.419, "r_x1": 102.793, "r_y1": 455.419, "r_x2": 102.793, "r_y2": 447.511, "r_x3": 78.207, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 105.245, "r_y0": 455.375, "r_x1": 113.1, "r_y1": 455.375, "r_x2": 113.1, "r_y2": 447.574, "r_x3": 105.245, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.341, 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1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 488.251, "r_x1": 74.221, "r_y1": 488.251, "r_x2": 74.221, "r_y2": 480.45, "r_x3": 64.708, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.202, "r_y0": 488.251, "r_x1": 206.135, "r_y1": 488.251, "r_x2": 206.135, "r_y2": 480.45, "r_x3": 78.202, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.38, "r_y0": 488.295, "r_x1": 238.122, "r_y1": 488.295, "r_x2": 238.122, "r_y2": 480.387, "r_x3": 208.38, "r_y3": 480.387, "coord_origin": "TOPLEFT"}, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.413, "r_y0": 488.251, "r_x1": 264.506, "r_y1": 488.251, "r_x2": 264.506, "r_y2": 480.45, "r_x3": 240.413, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 499.21, "r_x1": 294.046, "r_y1": 499.21, "r_x2": 294.046, "r_y2": 491.409, "r_x3": 78.207, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 510.169, "r_x1": 200.348, "r_y1": 510.169, "r_x2": 200.348, "r_y2": 502.368, "r_x3": 78.207, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.667, "r_y0": 510.213, "r_x1": 256.575, "r_y1": 510.213, "r_x2": 256.575, "r_y2": 502.305, "r_x3": 203.667, "r_y3": 502.305, "coord_origin": "TOPLEFT"}, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 256.574, "r_y0": 510.169, "r_x1": 258.586, "r_y1": 510.169, "r_x2": 258.586, "r_y2": 502.368, "r_x3": 256.574, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.906, "r_y0": 510.169, "r_x1": 294.044, "r_y1": 510.169, "r_x2": 294.044, "r_y2": 502.368, "r_x3": 261.906, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "unless it", "orig": "unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 521.1279999999999, "r_x1": 208.13, "r_y1": 521.1279999999999, "r_x2": 208.13, "r_y2": 513.327, "r_x3": 78.207, "r_y3": 513.327, "coord_origin": "TOPLEFT"}, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.14, "r_x1": 295.563, "r_y1": 552.14, "r_x2": 295.563, "r_y2": 544.3389999999999, "r_x3": 53.798, "r_y3": 544.3389999999999, "coord_origin": "TOPLEFT"}, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 181.83299999999997, "r_x1": 416.179, "r_y1": 181.83299999999997, "r_x2": 416.179, "r_y2": 180.015, "r_x3": 340.002, "r_y3": 180.015, "coord_origin": "TOPLEFT"}, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.383, "r_y0": 92.00300000000004, "r_x1": 417.395, "r_y1": 92.00300000000004, "r_x2": 417.395, "r_y2": 86.39099999999996, "r_x3": 339.383, "r_y3": 86.39099999999996, "coord_origin": "TOPLEFT"}, "text": "Compliant with guidelines", "orig": "Compliant with guidelines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.428, "r_y0": 92.00300000000004, "r_x1": 545.834, "r_y1": 92.00300000000004, "r_x2": 545.834, "r_y2": 86.39099999999996, "r_x3": 451.428, "r_y3": 86.39099999999996, "coord_origin": "TOPLEFT"}, "text": "Plausible but invalid alternative", "orig": "Plausible but invalid alternative", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.337, "r_y0": 371.669, "r_x1": 513.216, "r_y1": 371.669, "r_x2": 513.216, "r_y2": 366.057, "r_x3": 350.337, "r_y3": 366.057, "coord_origin": "TOPLEFT"}, "text": "Borderline case: Two guideline-compliant alternatives", "orig": "Borderline case: Two guideline-compliant alternatives", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 247.11400000000003, "r_x1": 416.179, 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"from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 322.194, "r_y0": 259.21900000000005, "r_x1": 326.015, "r_y1": 259.21900000000005, "r_x2": 326.015, "r_y2": 254.54200000000003, "r_x3": 322.194, "r_y3": 254.54200000000003, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 322.194, "r_y0": 372.762, "r_x1": 326.015, "r_y1": 372.762, "r_x2": 326.015, "r_y2": 368.085, "r_x3": 322.194, "r_y3": 368.085, "coord_origin": "TOPLEFT"}, "text": "D", "orig": "D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 481.718, "r_x1": 558.389, "r_y1": 481.718, "r_x2": 558.389, "r_y2": 474.265, "r_x3": 317.955, "r_y3": 474.265, "coord_origin": "TOPLEFT"}, "text": "Figure 4: Examples of plausible annotation alternatives for", "orig": "Figure 4: Examples of plausible annotation alternatives for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 492.677, "r_x1": 559.806, "r_y1": 492.677, "r_x2": 559.806, "r_y2": 485.224, "r_x3": 317.955, "r_y3": 485.224, "coord_origin": "TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 53.798, "t": 87.79399999999998, "r": 294.045, "b": 95.59500000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 63.761, "t": 109.71199999999999, "r": 294.043, "b": 117.51300000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": 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"confidence": 0.9544308185577393, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.623, "t": 525.928, "r": 558.204, "b": 533.729, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 53.529, "t": 533.381, "r": 294.043, "b": 541.182, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 327.918, "t": 548.0699999999999, "r": 456.801, "b": 555.523, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 63.761, "t": 577.44, "r": 136.774, "b": 584.893, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 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"DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.798, "t": 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Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 401.077, "r_x1": 140.362, "r_y1": 401.077, "r_x2": 140.362, "r_y2": 393.276, "r_x3": 53.798, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.99, "r_y0": 401.121, "r_x1": 175.969, "r_y1": 401.121, "r_x2": 175.969, "r_y2": 393.213, "r_x3": 142.99, "r_y3": 393.213, "coord_origin": "TOPLEFT"}, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.951, "r_y0": 401.077, "r_x1": 294.041, "r_y1": 401.077, "r_x2": 294.041, "r_y2": 393.276, "r_x3": 178.951, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 412.036, "r_x1": 295.425, "r_y1": 412.036, "r_x2": 295.425, "r_y2": 404.235, "r_x3": 53.798, "r_y3": 404.235, "coord_origin": "TOPLEFT"}, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 422.995, "r_x1": 294.043, "r_y1": 422.995, "r_x2": 294.043, "r_y2": 415.194, "r_x3": 53.529, "r_y3": 415.194, "coord_origin": "TOPLEFT"}, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 433.954, "r_x1": 294.048, "r_y1": 433.954, "r_x2": 294.048, "r_y2": 426.153, "r_x3": 53.798, "r_y3": 426.153, "coord_origin": "TOPLEFT"}, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 444.913, "r_x1": 256.806, "r_y1": 444.913, "r_x2": 256.806, "r_y2": 437.112, "r_x3": 53.798, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": 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"from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.877, "r_y0": 455.872, "r_x1": 80.889, "r_y1": 455.872, "r_x2": 80.889, "r_y2": 448.071, "r_x3": 78.877, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.139, "r_y0": 455.872, "r_x1": 294.049, "r_y1": 455.872, "r_x2": 294.049, "r_y2": 448.071, "r_x3": 83.139, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": "For the latter, we instructed annotation staff to minimise", "orig": "For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 466.831, "r_x1": 294.046, "r_y1": 466.831, "r_x2": 294.046, "r_y2": 459.03, "r_x3": 53.798, "r_y3": 459.03, "coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 385.937, "r_y0": 252.75400000000002, "r_x1": 392.569, "r_y1": 252.75400000000002, "r_x2": 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"TOPLEFT"}, "text": "ing fractions of the DocLayNet dataset. The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, 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"r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", 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PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 372.512, "r_x1": 558.2, "r_y1": 372.512, "r_x2": 558.2, "r_y2": 364.711, "r_x3": 317.686, "r_y3": 364.711, "coord_origin": "TOPLEFT"}, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 383.47, "r_x1": 558.369, "r_y1": 383.47, "r_x2": 558.369, "r_y2": 375.669, "r_x3": 317.955, "r_y3": 375.669, "coord_origin": "TOPLEFT"}, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 394.429, "r_x1": 558.2, "r_y1": 394.429, "r_x2": 558.2, "r_y2": 386.628, "r_x3": 317.955, "r_y3": 386.628, "coord_origin": "TOPLEFT"}, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.488, "r_x1": 531.077, "r_y1": 708.488, "r_x2": 531.077, "r_y2": 700.687, "r_x3": 317.955, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 533.162, "r_y0": 708.532, "r_x1": 557.256, "r_y1": 708.532, "r_x2": 557.256, "r_y2": 700.624, "r_x3": 533.162, "r_y3": 700.624, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 557.255, "r_y0": 708.488, "r_x1": 559.188, "r_y1": 708.488, "r_x2": 559.188, "r_y2": 700.687, "r_x3": 557.255, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": 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"Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 331.834, "r": 558.202, "b": 339.635, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 335.202, "r": 294.042, "b": 343.003, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, 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[]}, {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 445.027, "r": 558.369, "b": 452.828, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.529, "t": 558.142, "r": 294.043, "b": 565.943, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 558.206, "b": 631.775, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 352.97747802734375, "t": 150.791259765625, "r": 522.9158935546875, "b": 306.2658386230469, "coord_origin": "TOPLEFT"}, "confidence": 0.9879696369171143, "cells": [], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 80.35525512695312, "t": 150.93629455566406, "r": 267.0082092285156, "b": 295.4454650878906, "coord_origin": "TOPLEFT"}, "confidence": 0.9869412779808044, "cells": [], "children": []}]}, 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"figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}, {"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 294.044, "b": 95.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 558.201, "b": 95.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with"}, {"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 320.508, "r": 131.056, "b": 329.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9555686116218567, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 329.575, "r_x1": 131.056, "r_y1": 329.575, "r_x2": 131.056, "r_y2": 320.508, "r_x3": 53.798, "r_y3": 320.508, "coord_origin": "TOPLEFT"}, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 331.834, "r": 558.202, "b": 339.635, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate"}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 335.202, "r": 294.042, "b": 343.003, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 445.027, "r": 558.369, "b": 452.828, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order"}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.529, "t": 558.142, "r": 294.043, "b": 565.943, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The choice and number of labels can have a significant effect on"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 558.206, "b": 631.775, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of"}, {"label": "table", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "table", "bbox": {"l": 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"b": 95.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 558.201, "b": 95.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 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"children": []}, "text": "Learning Curve"}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 331.834, "r": 558.202, "b": 339.635, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate"}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.798, "t": 335.202, "r": 294.042, "b": 343.003, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is"}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 430.333, "r": 549.861, "b": 439.4, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 445.027, "r": 558.369, "b": 452.828, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order"}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.529, "t": 558.142, "r": 294.043, "b": 565.943, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The choice and number of labels can have a significant effect on"}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", 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60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": 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DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.322, "r_y1": 95.09699999999998, "r_x2": 294.322, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 295.649, "r_y1": 106.05600000000004, "r_x2": 295.649, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "orig": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "cLayNet data-sets. By evaluating on common label classes of", "orig": "cLayNet data-sets. By evaluating on common label classes of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "each dataset, we observe that the DocLayNet-trained model", "orig": "each dataset, we observe that the DocLayNet-trained model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 294.044, "r_y1": 138.933, "r_x2": 294.044, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "has much less pronounced variations in performance across", "orig": "has much less pronounced variations in performance across", 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"orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.448, "r_y0": 443.032, "r_x1": 123.305, "r_y1": 443.032, "r_x2": 123.305, "r_y2": 435.124, "r_x3": 108.448, "r_y3": 435.124, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.305, "r_y0": 442.988, "r_x1": 125.278, "r_y1": 442.988, "r_x2": 125.278, "r_y2": 435.187, "r_x3": 123.305, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 453.946, "r_x1": 294.276, "r_y1": 453.946, "r_x2": 294.276, "r_y2": 446.145, "r_x3": 63.761, "r_y3": 446.145, "coord_origin": "TOPLEFT"}, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 464.905, "r_x1": 62.989, "r_y1": 464.905, "r_x2": 62.989, "r_y2": 457.104, "r_x3": 53.798, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.853, "r_y0": 464.949, "r_x1": 88.947, "r_y1": 464.949, "r_x2": 88.947, "r_y2": 457.041, "r_x3": 64.853, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.02, "r_y0": 464.905, "r_x1": 104.245, "r_y1": 464.905, "r_x2": 104.245, "r_y2": 457.104, "r_x3": 91.02, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.108, "r_y0": 464.949, "r_x1": 124.781, "r_y1": 464.949, "r_x2": 124.781, "r_y2": 457.041, "r_x3": 106.108, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.855, "r_y0": 464.905, "r_x1": 277.632, "r_y1": 464.905, "r_x2": 277.632, "r_y2": 457.104, "r_x3": 126.855, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 295.595, "r_x1": 558.435, "r_y1": 295.595, "r_x2": 558.435, "r_y2": 287.794, "r_x3": 327.918, "r_y3": 287.794, "coord_origin": "TOPLEFT"}, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 306.554, "r_x1": 558.2, "r_y1": 306.554, "r_x2": 558.2, "r_y2": 298.753, "r_x3": 317.955, "r_y3": 298.753, "coord_origin": "TOPLEFT"}, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 371.078, "r_x1": 365.596, "r_y1": 371.078, "r_x2": 365.596, "r_y2": 364.928, "r_x3": 333.391, "r_y3": 364.928, "coord_origin": "TOPLEFT"}, "text": "Recognition", "orig": "Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.596, "r_y0": 371.045, "r_x1": 367.13, "r_y1": 371.045, "r_x2": 367.13, "r_y2": 364.977, "r_x3": 365.596, "r_y3": 364.977, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.874, "r_y0": 371.045, "r_x1": 434.295, "r_y1": 371.045, "r_x2": 434.295, "r_y2": 364.977, "r_x3": 368.874, "r_y3": 364.977, "coord_origin": "TOPLEFT"}, "text": "pages 1449-1453, 2013.", "orig": "pages 1449-1453, 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"Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 558.204, "b": 109.293, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. 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"coord_origin": "TOPLEFT"}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 558.204, "b": 109.293, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. 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While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 512.232, "r_x1": 295.556, "r_y1": 512.232, "r_x2": 295.556, "r_y2": 504.431, "r_x3": 53.798, "r_y3": 504.431, "coord_origin": "TOPLEFT"}, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 523.191, "r_x1": 294.216, "r_y1": 523.191, "r_x2": 294.216, "r_y2": 515.39, "r_x3": 53.798, "r_y3": 515.39, "coord_origin": "TOPLEFT"}, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 534.15, "r_x1": 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not made or distributed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 656.741, "r_x1": 294.045, "r_y1": 656.741, "r_x2": 294.045, "r_y2": 650.673, "r_x3": 53.798, "r_y3": 650.673, "coord_origin": "TOPLEFT"}, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "orig": "for profit or commercial advantage and that copies bear this notice and the full citation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.711, "r_x1": 295.118, "r_y1": 664.711, "r_x2": 295.118, "r_y2": 658.643, "r_x3": 53.798, "r_y3": 658.643, "coord_origin": "TOPLEFT"}, "text": "on the first page. Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.681, "r_x1": 187.723, "r_y1": 672.681, "r_x2": 187.723, "r_y2": 666.613, "r_x3": 53.798, "r_y3": 666.613, "coord_origin": "TOPLEFT"}, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 681.481, "r_x1": 197.863, "r_y1": 681.481, "r_x2": 197.863, "r_y2": 675.331, "r_x3": 53.798, "r_y3": 675.331, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.317, "r_y0": 690.215, "r_x1": 186.746, "r_y1": 690.215, "r_x2": 186.746, "r_y2": 684.147, "r_x3": 53.317, "r_y3": 684.147, "coord_origin": "TOPLEFT"}, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.554, "r_y0": 698.186, "r_x1": 157.031, "r_y1": 698.186, "r_x2": 157.031, "r_y2": 692.1179999999999, "r_x3": 53.554, "r_y3": 692.1179999999999, "coord_origin": "TOPLEFT"}, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 706.156, "r_x1": 166.941, "r_y1": 706.156, "r_x2": 166.941, "r_y2": 700.088, "r_x3": 53.798, "r_y3": 700.088, "coord_origin": "TOPLEFT"}, "text": "https://doi.org/10.1145/3534678.3539043", "orig": "https://doi.org/10.1145/3534678.3539043", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.87, "r_y0": 353.853, "r_x1": 330.412, "r_y1": 353.853, "r_x2": 330.412, "r_y2": 352.266, "r_x3": 327.87, "r_y3": 352.266, "coord_origin": "TOPLEFT"}, "text": "13", "orig": "13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 333.231, "r_x1": 351.161, "r_y1": 333.231, "r_x2": 351.161, "r_y2": 331.961, "r_x3": 327.83, "r_y3": 331.961, "coord_origin": "TOPLEFT"}, "text": "USING THE VERTICAL TUBE -", "orig": "USING THE VERTICAL TUBE -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 334.841, "r_x1": 348.305, "r_y1": 334.841, "r_x2": 348.305, "r_y2": 333.571, "r_x3": 327.83, "r_y3": 333.571, "coord_origin": "TOPLEFT"}, "text": "MODELS AY11230/11234", "orig": "MODELS AY11230/11234", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 337.855, "r_x1": 350.743, "r_y1": 337.855, "r_x2": 350.743, "r_y2": 336.744, "r_x3": 327.83, "r_y3": 336.744, "coord_origin": "TOPLEFT"}, "text": "1. 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Make su", "orig": "3. 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In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.632, "r_y0": 278.03499999999997, "r_x1": 353.135, "r_y1": 278.03499999999997, "r_x2": 353.135, "r_y2": 276.765, "r_x3": 352.632, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.633, "r_y0": 292.527, "r_x1": 353.137, "r_y1": 292.527, "r_x2": 353.137, "r_y2": 291.256, "r_x3": 352.633, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 295.747, "r_x1": 327.385, "r_y1": 295.747, "r_x2": 327.385, "r_y2": 294.477, "r_x3": 326.882, "r_y3": 294.477, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 352.638, "r_y0": 326.341, "r_x1": 353.141, "r_y1": 326.341, "r_x2": 353.141, "r_y2": 325.071, "r_x3": 352.638, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.175, "r_y0": 284.476, "r_x1": 384.678, "r_y1": 284.476, "r_x2": 384.678, "r_y2": 283.205, "r_x3": 384.175, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.176, "r_y0": 287.696, "r_x1": 384.68, "r_y1": 287.696, "r_x2": 384.68, "r_y2": 286.426, "r_x3": 384.176, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.176, "r_y0": 300.578, "r_x1": 384.68, "r_y1": 300.578, "r_x2": 384.68, "r_y2": 299.308, "r_x3": 384.176, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.177, "r_y0": 311.849, "r_x1": 384.681, "r_y1": 311.849, "r_x2": 384.681, "r_y2": 310.579, "r_x3": 384.177, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 384.177, "r_y0": 329.561, "r_x1": 384.681, "r_y1": 329.561, "r_x2": 384.681, "r_y2": 328.291, "r_x3": 384.177, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. 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"Yesterday", "orig": "Yesterday", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 323, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 468.914, "r_x1": 391.382, "r_y1": 468.914, "r_x2": 391.382, "r_y2": 466.909, "r_x3": 363.545, "r_y3": 466.909, "coord_origin": "TOPLEFT"}, "text": "Established in Sydney in 1837, and then ", "orig": "Established in Sydney in 1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 324, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 470.947, "r_x1": 395.018, "r_y1": 470.947, "r_x2": 395.018, "r_y2": 468.942, "r_x3": 363.545, "r_y3": 468.942, "coord_origin": "TOPLEFT"}, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 325, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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"electricity needs of Australian households. ", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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"r_y1": 441.009, "r_x2": 372.166, "r_y2": 431.094, "r_x3": 329.405, "r_y3": 431.094, "coord_origin": "TOPLEFT"}, "text": "175years of ", "orig": "175years of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 336, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.994, "r_y0": 441.009, "r_x1": 342.695, "r_y1": 441.009, "r_x2": 342.695, "r_y2": 431.094, "r_x3": 340.994, "r_y3": 431.094, "coord_origin": "TOPLEFT"}, "text": "\u00a0", "orig": "\u00a0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 337, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 451.181, "r_x1": 385.754, "r_y1": 451.181, "r_x2": 385.754, "r_y2": 441.267, "r_x3": 329.405, "r_y3": 441.267, "coord_origin": "TOPLEFT"}, "text": "lookingforward.", "orig": "lookingforward.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 338, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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"from_ocr": false}, {"index": 341, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 497.777, "r_y0": 254.86599999999999, "r_x1": 542.884, "r_y1": 254.86599999999999, "r_x2": 542.884, "r_y2": 251.74699999999996, "r_x3": 497.777, "r_y3": 251.74699999999996, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings", "orig": "signs, signals and road markings", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 342, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 490.307, "r_y0": 254.90700000000004, "r_x1": 492.1, "r_y1": 254.90700000000004, "r_x2": 492.1, "r_y2": 251.788, "r_x3": 490.307, "r_y3": 251.788, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 343, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 265.87300000000005, "r_x1": 500.04, "r_y1": 265.87300000000005, "r_x2": 500.04, "r_y2": 264.063, 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0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 267.91600000000005, "r_x1": 544.504, "r_y1": 267.91600000000005, "r_x2": 544.504, "r_y2": 266.106, "r_x3": 498.153, "r_y3": 266.106, "coord_origin": "TOPLEFT"}, "text": "some of the controls in your vehicle. This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 293.054, "r_x1": 295.346, "r_y1": 293.054, "r_x2": 295.346, "r_y2": 285.253, "r_x3": 53.798, "r_y3": 285.253, "coord_origin": "TOPLEFT"}, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 304.013, "r_x1": 294.047, "r_y1": 304.013, "r_x2": 294.047, "r_y2": 296.212, "r_x3": 53.798, "r_y3": 296.212, "coord_origin": "TOPLEFT"}, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.972, "r_x1": 294.046, "r_y1": 314.972, "r_x2": 294.046, "r_y2": 307.171, "r_x3": 53.798, "r_y3": 307.171, "coord_origin": "TOPLEFT"}, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, 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or hard copies of part or all of this work for personal or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 648.771, "r_x1": 294.044, "r_y1": 648.771, "r_x2": 294.044, "r_y2": 642.703, "r_x3": 53.798, "r_y3": 642.703, "coord_origin": "TOPLEFT"}, "text": "classroom use is granted without fee provided that copies are not made or distributed", "orig": "classroom use is granted without fee provided that copies are not made or distributed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 656.741, "r_x1": 294.045, "r_y1": 656.741, "r_x2": 294.045, "r_y2": 650.673, "r_x3": 53.798, "r_y3": 650.673, "coord_origin": "TOPLEFT"}, "text": "for profit or commercial advantage and that copies bear this notice and the full citation", "orig": "for profit or commercial 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Copyrights for third-party components of this work must be honored.", "orig": "on the first page. Copyrights for third-party components of this work must be honored.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.681, "r_x1": 187.723, "r_y1": 672.681, "r_x2": 187.723, "r_y2": 666.613, "r_x3": 53.798, "r_y3": 666.613, "coord_origin": "TOPLEFT"}, "text": "For all other uses, contact the owner/author(s).", "orig": "For all other uses, contact the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 134, "label": "text", "bbox": {"l": 53.798, "t": 675.331, "r": 197.863, "b": 681.481, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 681.481, "r_x1": 197.863, "r_y1": 681.481, "r_x2": 197.863, "r_y2": 675.331, "r_x3": 53.798, "r_y3": 675.331, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 135, "label": "text", "bbox": {"l": 53.317, "t": 684.147, "r": 186.746, "b": 690.215, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.317, "r_y0": 690.215, "r_x1": 186.746, "r_y1": 690.215, "r_x2": 186.746, "r_y2": 684.147, "r_x3": 53.317, "r_y3": 684.147, "coord_origin": "TOPLEFT"}, "text": "\u00a9 2022 Copyright held by the owner/author(s).", "orig": "\u00a9 2022 Copyright held by the owner/author(s).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 136, "label": "text", "bbox": {"l": 53.554, "t": 692.1179999999999, "r": 157.031, "b": 698.186, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.554, "r_y0": 698.186, "r_x1": 157.031, "r_y1": 698.186, "r_x2": 157.031, "r_y2": 692.1179999999999, "r_x3": 53.554, "r_y3": 692.1179999999999, "coord_origin": "TOPLEFT"}, "text": "ACM ISBN 978-1-4503-9385-0/22/08.", "orig": "ACM ISBN 978-1-4503-9385-0/22/08.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 53.798, "t": 700.088, "r": 166.941, "b": 706.156, "coord_origin": "TOPLEFT"}, "confidence": 0.509588360786438, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 706.156, "r_x1": 166.941, "r_y1": 706.156, "r_x2": 166.941, "r_y2": 700.088, "r_x3": 53.798, "r_y3": 700.088, "coord_origin": "TOPLEFT"}, "text": "https://doi.org/10.1145/3534678.3539043", "orig": "https://doi.org/10.1145/3534678.3539043", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "picture", "bbox": {"l": 323.408203125, "t": 250.34873962402344, "r": 553.2952270507812, "b": 525.8507080078125, "coord_origin": "TOPLEFT"}, "confidence": 0.5910208821296692, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.87, "r_y0": 353.853, "r_x1": 330.412, "r_y1": 353.853, "r_x2": 330.412, "r_y2": 352.266, "r_x3": 327.87, "r_y3": 352.266, "coord_origin": "TOPLEFT"}, "text": "13", "orig": "13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 333.231, "r_x1": 351.161, "r_y1": 333.231, "r_x2": 351.161, "r_y2": 331.961, "r_x3": 327.83, "r_y3": 331.961, "coord_origin": "TOPLEFT"}, "text": "USING THE VERTICAL TUBE -", "orig": "USING THE VERTICAL TUBE -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 334.841, "r_x1": 348.305, "r_y1": 334.841, "r_x2": 348.305, "r_y2": 333.571, "r_x3": 327.83, "r_y3": 333.571, "coord_origin": "TOPLEFT"}, "text": "MODELS AY11230/11234", "orig": "MODELS AY11230/11234", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 337.855, "r_x1": 350.743, "r_y1": 337.855, "r_x2": 350.743, "r_y2": 336.744, "r_x3": 327.83, "r_y3": 336.744, "coord_origin": "TOPLEFT"}, "text": "1. 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There are two objectives. The lower ", "orig": "1. There are two objectives. The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, "r_x3": 358.426, "r_y3": 334.732, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 340.833, "r_x1": 386.395, "r_y1": 340.833, "r_x2": 386.395, "r_y2": 339.563, "r_x3": 358.426, "r_y3": 339.563, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 342.443, "r_x1": 379.572, "r_y1": 342.443, "r_x2": 379.572, "r_y2": 341.173, "r_x3": 358.426, "r_y3": 341.173, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.427, "r_y0": 344.053, "r_x1": 384.511, "r_y1": 344.053, "r_x2": 384.511, "r_y2": 342.783, "r_x3": 358.427, "r_y3": 342.783, "coord_origin": "TOPLEFT"}, "text": "3. 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To indicate that the new criteria had been applied to a given procedure, a ", "orig": "improved obstacle protection. To indicate that the new criteria had been applied to a given procedure, a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 257, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.311, "r_y0": 387.794, "r_x1": 549.499, "r_y1": 387.794, "r_x2": 549.499, "r_y2": 386.116, "r_x3": 538.311, "r_y3": 386.116, "coord_origin": "TOPLEFT"}, "text": " is placed on ", "orig": " is placed on ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 258, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 390.073, "r_x1": 547.582, "r_y1": 390.073, "r_x2": 547.582, "r_y2": 388.395, "r_x3": 449.101, "r_y3": 388.395, "coord_origin": "TOPLEFT"}, "text": "the circling line of minimums. The new circling tables and explanatory information is located in the Legend of the TPP.", "orig": "the circling line of minimums. 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"r_y3": 393.572, "coord_origin": "TOPLEFT"}, "text": " on the circling line of ", "orig": " on the circling line of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 261, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 397.529, "r_x1": 455.747, "r_y1": 397.529, "r_x2": 455.747, "r_y2": 395.85, "r_x3": 449.101, "r_y3": 395.85, "coord_origin": "TOPLEFT"}, "text": "minima.", "orig": "minima.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 262, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 417.429, "r_x1": 496.283, "r_y1": 417.429, "r_x2": 496.283, "r_y2": 415.919, "r_x3": 449.955, "r_y3": 415.919, "coord_origin": "TOPLEFT"}, "text": "$SSO\\glyph6WDQGDUGglyph&LUFOLQJglyph $SSURDFKglyph0DQHXYHULQJglyph5DGLXVglyph7 DEOH", "orig": "$SSO\\glyph6WDQGDUGglyph&LUFOLQJglyph $SSURDFKglyph0DQHXYHULQJglyph5DGLXVglyph7 DEOH", "text_direction": "left_to_right", 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469.356, "r_y1": 422.632, "r_x2": 469.356, "r_y2": 420.619, "r_x3": 449.101, "r_y3": 420.619, "coord_origin": "TOPLEFT"}, "text": "AIRPORT SKETCH", "orig": "AIRPORT SKETCH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 270, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 427.126, "r_x1": 525.936, "r_y1": 427.126, "r_x2": 525.936, "r_y2": 425.448, "r_x3": 449.101, "r_y3": 425.448, "coord_origin": "TOPLEFT"}, "text": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "orig": "The airport sketch is a depiction of the airport with emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 271, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 429.405, "r_x1": 522.034, "r_y1": 429.405, "r_x2": 522.034, "r_y2": 427.727, "r_x3": 449.101, "r_y3": 427.727, "coord_origin": "TOPLEFT"}, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 272, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 431.683, "r_x1": 524.672, "r_y1": 431.683, "r_x2": 524.672, "r_y2": 430.005, "r_x3": 449.101, "r_y3": 430.005, "coord_origin": "TOPLEFT"}, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 273, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 433.962, "r_x1": 527.172, "r_y1": 433.962, "r_x2": 527.172, "r_y2": 432.284, "r_x3": 449.101, "r_y3": 432.284, "coord_origin": "TOPLEFT"}, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 274, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 436.241, "r_x1": 502.395, "r_y1": 436.241, "r_x2": 502.395, "r_y2": 434.563, "r_x3": 449.101, "r_y3": 434.563, "coord_origin": "TOPLEFT"}, "text": "dimensions (length and width) are shown for al active runways.", "orig": "dimensions (length and width) are shown for al active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 275, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 440.798, "r_x1": 512.927, "r_y1": 440.798, "r_x2": 512.927, "r_y2": 439.12, "r_x3": 449.101, "r_y3": 439.12, "coord_origin": "TOPLEFT"}, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 276, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 445.903, "r_x1": 460.023, "r_y1": 445.903, "r_x2": 460.023, "r_y2": 444.393, "r_x3": 449.955, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 277, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 445.903, "r_x1": 473.988, "r_y1": 445.903, "r_x2": 473.988, "r_y2": 444.393, "r_x3": 464.9, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 278, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 447.954, "r_x1": 474.967, "r_y1": 447.954, "r_x2": 474.967, "r_y2": 446.444, "r_x3": 464.9, "r_y3": 446.444, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 279, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 445.903, "r_x1": 489.457, "r_y1": 445.903, "r_x2": 489.457, "r_y2": 444.393, "r_x3": 478.914, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 280, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 445.903, "r_x1": 505.031, "r_y1": 445.903, "r_x2": 505.031, "r_y2": 444.393, "r_x3": 493.064, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 281, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.581, "r_y0": 445.903, "r_x1": 524.302, "r_y1": 445.903, "r_x2": 524.302, "r_y2": 444.393, "r_x3": 509.581, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 282, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 456.645, "r_x1": 458.314, "r_y1": 456.645, "r_x2": 458.314, "r_y2": 455.135, "r_x3": 449.955, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 283, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 458.696, "r_x1": 461.921, "r_y1": 458.696, "r_x2": 461.921, "r_y2": 457.186, "r_x3": 449.955, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 284, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 460.747, "r_x1": 457.08, "r_y1": 460.747, "r_x2": 457.08, "r_y2": 459.237, "r_x3": 449.955, "r_y3": 459.237, "coord_origin": "TOPLEFT"}, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 285, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 456.645, "r_x1": 472.877, "r_y1": 456.645, "r_x2": 472.877, "r_y2": 455.135, "r_x3": 464.9, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 286, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 458.696, "r_x1": 472.498, "r_y1": 458.696, "r_x2": 472.498, "r_y2": 457.186, "r_x3": 464.9, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 287, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 456.645, "r_x1": 484.707, "r_y1": 456.645, "r_x2": 484.707, "r_y2": 455.135, "r_x3": 478.914, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Closed ", "orig": "Closed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 288, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 458.696, "r_x1": 486.608, "r_y1": 458.696, "r_x2": 486.608, "r_y2": 457.186, "r_x3": 478.914, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 289, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 456.645, "r_x1": 504.207, "r_y1": 456.645, "r_x2": 504.207, "r_y2": 455.135, "r_x3": 493.064, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 290, "rgba": {"r": 0, "g": 0, 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Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. 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Circling only approaches wil not show a TDZE.", "orig": "the landing surface. 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Meeting", "orig": "Annual General Meeting", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 313, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 397.215, "r_x1": 342.658, "r_y1": 396.465, "r_x2": 342.658, "r_y2": 395.296, "r_x3": 329.405, "r_y3": 395.21, "coord_origin": "TOPLEFT"}, "text": "27 February 2013 1", "orig": "27 February 2013 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 315, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.472, "r_y0": 397.213, "r_x1": 386.259, "r_y1": 397.213, "r_x2": 386.259, "r_y2": 395.208, "r_x3": 350.472, "r_y3": 395.208, "coord_origin": "TOPLEFT"}, "text": "2013 interim result and interim dividend announced", "orig": "2013 interim result and interim dividend announced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 316, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, 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confirmation", "orig": "1 Indicative dates only, subject to change/Board confirmation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 320, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 406.543, "r_x1": 391.771, "r_y1": 406.543, "r_x2": 391.771, "r_y2": 404.538, "r_x3": 329.405, "r_y3": 404.538, "coord_origin": "TOPLEFT"}, "text": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney ", "orig": "AGL's Annual General Meeting wil be held at the City Recital Hal , Angel Place, Sydney ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 321, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.405, "r_y0": 408.577, "r_x1": 369.653, "r_y1": 408.577, "r_x2": 369.653, "r_y2": 406.572, "r_x3": 329.405, "r_y3": 406.572, "coord_origin": "TOPLEFT"}, "text": "commencing at 10.30am on Tuesday 23 October 2012.", "orig": "commencing at 10.30am on Tuesday 23 October 2012.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 322, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 465.415, "r_x1": 379.529, "r_y1": 465.415, "r_x2": 379.529, "r_y2": 460.96, "r_x3": 363.545, "r_y3": 460.96, "coord_origin": "TOPLEFT"}, "text": "Yesterday", "orig": "Yesterday", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 323, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 468.914, "r_x1": 391.382, "r_y1": 468.914, "r_x2": 391.382, "r_y2": 466.909, "r_x3": 363.545, "r_y3": 466.909, "coord_origin": "TOPLEFT"}, "text": "Established in Sydney in 1837, and then ", "orig": "Established in Sydney in 1837, and then ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 324, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 470.947, "r_x1": 395.018, "r_y1": 470.947, "r_x2": 395.018, "r_y2": 468.942, "r_x3": 363.545, "r_y3": 468.942, "coord_origin": "TOPLEFT"}, "text": "known as The Australian Gas Light Company, ", "orig": "known as The Australian Gas Light Company, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 325, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 472.981, "r_x1": 394.083, "r_y1": 472.981, "r_x2": 394.083, "r_y2": 470.976, "r_x3": 363.545, "r_y3": 470.976, "coord_origin": "TOPLEFT"}, "text": "the AGL business has an established history ", "orig": "the AGL business has an established history ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 326, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 475.014, "r_x1": 390.607, "r_y1": 475.014, "r_x2": 390.607, "r_y2": 473.009, "r_x3": 363.545, "r_y3": 473.009, "coord_origin": "TOPLEFT"}, "text": "and reputation for serving the gas and ", "orig": "and reputation for serving the gas and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 327, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 363.545, "r_y0": 477.048, "r_x1": 393.496, "r_y1": 477.048, "r_x2": 393.496, "r_y2": 475.043, "r_x3": 363.545, "r_y3": 475.043, "coord_origin": "TOPLEFT"}, "text": "electricity needs of Australian households. ", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. 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Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 159, "label": "text", "bbox": {"l": 326.88, "t": 276.765, "r": 351.888, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 160, "label": "text", "bbox": {"l": 326.88, "t": 278.375, "r": 335.467, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 161, "label": "text", "bbox": {"l": 326.88, "t": 281.595, "r": 354.578, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 162, "label": "text", "bbox": {"l": 326.88, "t": 283.205, "r": 335.175, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 163, "label": "text", "bbox": {"l": 326.88, "t": 284.816, "r": 355.748, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 164, "label": "text", "bbox": {"l": 326.88, "t": 286.426, "r": 355.576, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 165, "label": "text", "bbox": {"l": 326.88, "t": 288.036, "r": 355.17, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 166, "label": "text", "bbox": {"l": 326.882, "t": 289.646, "r": 356.021, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 167, "label": "text", "bbox": {"l": 326.882, "t": 291.256, "r": 351.575, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 168, "label": "text", "bbox": {"l": 326.882, "t": 292.867, "r": 336.207, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 169, "label": "text", "bbox": {"l": 326.882, "t": 296.087, "r": 335.394, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 170, "label": "text", "bbox": {"l": 326.882, "t": 297.697, "r": 353.186, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 171, "label": "text", "bbox": {"l": 326.883, "t": 299.308, "r": 354.432, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 172, "label": "text", "bbox": {"l": 326.883, "t": 300.918, "r": 333.328, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 173, "label": "text", "bbox": {"l": 326.883, "t": 302.528, "r": 355.709, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 174, "label": "text", "bbox": {"l": 326.883, "t": 304.138, "r": 356.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 175, "label": "text", "bbox": {"l": 326.883, "t": 305.748, "r": 354.466, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 176, "label": "text", "bbox": {"l": 326.883, "t": 307.359, "r": 357.261, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 177, "label": "text", "bbox": {"l": 326.883, "t": 308.969, "r": 356.449, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 178, "label": "text", "bbox": {"l": 326.883, "t": 310.579, "r": 343.173, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 179, "label": "text", "bbox": {"l": 326.883, "t": 313.799, "r": 344.134, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 180, "label": "text", "bbox": {"l": 326.883, "t": 315.41, "r": 348.502, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 181, "label": "text", "bbox": {"l": 326.883, "t": 317.02, "r": 354.081, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 182, "label": "text", "bbox": {"l": 326.885, "t": 318.63, "r": 354.842, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 183, "label": "text", "bbox": {"l": 326.885, "t": 320.24, "r": 348.021, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 184, "label": "text", "bbox": {"l": 326.885, "t": 321.851, "r": 352.968, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 185, "label": "text", "bbox": {"l": 326.886, "t": 323.461, "r": 357.529, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 186, "label": "text", "bbox": {"l": 326.886, "t": 325.071, "r": 350.138, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 187, "label": "text", "bbox": {"l": 326.886, "t": 326.681, "r": 353.141, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 188, "label": "text", "bbox": {"l": 326.886, "t": 328.291, "r": 336.892, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 189, "label": "text", "bbox": {"l": 358.42, "t": 263.88300000000004, "r": 366.933, "b": 265.153, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 190, "label": "text", "bbox": {"l": 358.42, "t": 265.49300000000005, "r": 388.997, "b": 266.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 191, "label": "text", "bbox": {"l": 358.42, "t": 267.10299999999995, "r": 384.59, "b": 268.37300000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 192, "label": "text", "bbox": {"l": 358.422, "t": 268.71299999999997, "r": 385.698, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 193, "label": "text", "bbox": {"l": 358.423, "t": 270.32399999999996, "r": 385.612, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 194, "label": "text", "bbox": {"l": 358.423, "t": 271.93399999999997, "r": 385.389, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 195, "label": "text", "bbox": {"l": 358.423, "t": 275.154, "r": 377.35, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 196, "label": "text", "bbox": {"l": 358.423, "t": 276.765, "r": 387.359, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 197, "label": "text", "bbox": {"l": 358.423, "t": 278.375, "r": 387.748, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 198, "label": "text", "bbox": {"l": 358.423, "t": 279.985, "r": 364.169, "b": 281.255, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 199, "label": "text", "bbox": {"l": 358.423, "t": 281.595, "r": 388.266, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 200, "label": "text", "bbox": {"l": 358.423, "t": 283.205, "r": 381.567, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 201, "label": "text", "bbox": {"l": 358.423, "t": 284.816, "r": 387.673, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 202, "label": "text", "bbox": {"l": 358.425, "t": 286.426, "r": 382.771, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 203, "label": "text", "bbox": {"l": 358.425, "t": 288.036, "r": 367.987, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 204, "label": "text", "bbox": {"l": 358.425, "t": 289.646, "r": 387.601, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 205, "label": "text", "bbox": {"l": 358.425, "t": 291.256, "r": 389.075, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 206, "label": "text", "bbox": {"l": 358.425, "t": 292.867, "r": 373.137, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 207, "label": "text", "bbox": {"l": 358.425, "t": 296.087, "r": 381.745, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 208, "label": "text", "bbox": {"l": 358.425, "t": 297.697, "r": 389.01, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 209, "label": "text", "bbox": {"l": 358.425, "t": 299.308, "r": 382.733, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 210, "label": "text", "bbox": {"l": 358.425, "t": 300.918, "r": 388.701, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 211, "label": "text", "bbox": {"l": 358.425, "t": 302.528, "r": 385.147, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 212, "label": "text", "bbox": {"l": 358.425, "t": 304.138, "r": 364.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 213, "label": "text", "bbox": {"l": 358.425, "t": 305.748, "r": 387.708, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 214, "label": "text", "bbox": {"l": 358.425, "t": 307.359, "r": 387.857, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 215, "label": "text", "bbox": {"l": 358.425, "t": 308.969, "r": 385.414, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 216, "label": "text", "bbox": {"l": 358.425, "t": 310.579, "r": 382.561, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 217, "label": "text", "bbox": {"l": 358.426, "t": 312.189, "r": 386.568, "b": 313.459, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 218, "label": "text", "bbox": {"l": 358.426, "t": 313.799, "r": 385.703, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 219, "label": "text", "bbox": {"l": 358.426, "t": 315.41, "r": 366.744, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 220, "label": "text", "bbox": {"l": 358.426, "t": 317.02, "r": 384.99, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 221, "label": "text", "bbox": {"l": 358.426, "t": 318.63, "r": 386.867, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 222, "label": "text", "bbox": {"l": 358.426, "t": 320.24, "r": 385.949, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 223, "label": "text", "bbox": {"l": 358.426, "t": 321.851, "r": 387.137, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 226, "label": "text", "bbox": {"l": 358.426, "t": 326.681, "r": 386.352, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 227, "label": "text", "bbox": {"l": 358.426, "t": 328.291, "r": 383.28, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 231, "label": "text", "bbox": {"l": 358.426, "t": 336.342, "r": 372.014, "b": 337.613, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 232, "label": "text", "bbox": {"l": 358.426, "t": 337.953, "r": 385.634, "b": 339.223, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 233, "label": "text", "bbox": {"l": 358.426, "t": 339.563, "r": 386.395, "b": 340.833, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 340.833, "r_x1": 386.395, "r_y1": 340.833, "r_x2": 386.395, "r_y2": 339.563, "r_x3": 358.426, "r_y3": 339.563, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 234, "label": "text", "bbox": {"l": 358.426, "t": 341.173, "r": 379.572, "b": 342.443, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 342.443, "r_x1": 379.572, "r_y1": 342.443, "r_x2": 379.572, "r_y2": 341.173, "r_x3": 358.426, "r_y3": 341.173, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 235, "label": "text", "bbox": {"l": 358.427, "t": 342.783, "r": 384.511, "b": 344.053, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.427, "r_y0": 344.053, "r_x1": 384.511, "r_y1": 344.053, "r_x2": 384.511, "r_y2": 342.783, "r_x3": 358.427, "r_y3": 342.783, "coord_origin": "TOPLEFT"}, "text": "3. 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The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 299, "label": "text", "bbox": {"l": 449.101, "t": 434.563, "r": 502.395, "b": 436.241, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 274, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 436.241, "r_x1": 502.395, "r_y1": 436.241, "r_x2": 502.395, "r_y2": 434.563, "r_x3": 449.101, "r_y3": 434.563, "coord_origin": "TOPLEFT"}, "text": "dimensions (length and width) are shown for al active runways.", "orig": "dimensions (length and width) are shown for al active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 300, "label": "text", "bbox": {"l": 449.101, "t": 439.12, "r": 512.927, "b": 440.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 275, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 440.798, "r_x1": 512.927, "r_y1": 440.798, "r_x2": 512.927, "r_y2": 439.12, "r_x3": 449.101, "r_y3": 439.12, "coord_origin": "TOPLEFT"}, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 301, "label": "text", "bbox": {"l": 449.955, "t": 444.393, "r": 460.023, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 276, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 445.903, "r_x1": 460.023, "r_y1": 445.903, "r_x2": 460.023, "r_y2": 444.393, "r_x3": 449.955, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 302, "label": "text", "bbox": {"l": 464.9, "t": 444.393, "r": 473.988, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 277, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 445.903, "r_x1": 473.988, "r_y1": 445.903, "r_x2": 473.988, "r_y2": 444.393, "r_x3": 464.9, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 303, "label": "text", "bbox": {"l": 464.9, "t": 446.444, "r": 474.967, "b": 447.954, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 278, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 447.954, "r_x1": 474.967, "r_y1": 447.954, "r_x2": 474.967, "r_y2": 446.444, "r_x3": 464.9, "r_y3": 446.444, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 304, "label": "text", "bbox": {"l": 478.914, "t": 444.393, "r": 489.457, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 279, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 445.903, "r_x1": 489.457, "r_y1": 445.903, "r_x2": 489.457, "r_y2": 444.393, "r_x3": 478.914, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 305, "label": "text", "bbox": {"l": 493.064, "t": 444.393, "r": 505.031, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 280, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 445.903, "r_x1": 505.031, "r_y1": 445.903, "r_x2": 505.031, "r_y2": 444.393, "r_x3": 493.064, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 306, "label": "text", "bbox": {"l": 509.581, "t": 444.393, "r": 524.302, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 281, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.581, "r_y0": 445.903, "r_x1": 524.302, "r_y1": 445.903, "r_x2": 524.302, "r_y2": 444.393, "r_x3": 509.581, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 307, "label": "text", "bbox": {"l": 449.955, "t": 455.135, "r": 458.314, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 282, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 456.645, "r_x1": 458.314, "r_y1": 456.645, "r_x2": 458.314, "r_y2": 455.135, "r_x3": 449.955, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 308, "label": "text", "bbox": {"l": 449.955, "t": 457.186, "r": 461.921, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 283, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 458.696, "r_x1": 461.921, "r_y1": 458.696, "r_x2": 461.921, "r_y2": 457.186, "r_x3": 449.955, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 309, "label": "text", "bbox": {"l": 449.955, "t": 459.237, "r": 457.08, "b": 460.747, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 284, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 460.747, "r_x1": 457.08, "r_y1": 460.747, "r_x2": 457.08, "r_y2": 459.237, "r_x3": 449.955, "r_y3": 459.237, "coord_origin": "TOPLEFT"}, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 310, "label": "text", "bbox": {"l": 464.9, "t": 455.135, "r": 472.877, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 285, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 456.645, "r_x1": 472.877, "r_y1": 456.645, "r_x2": 472.877, "r_y2": 455.135, "r_x3": 464.9, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 311, "label": "text", "bbox": {"l": 464.9, "t": 457.186, "r": 472.498, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 286, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 458.696, "r_x1": 472.498, "r_y1": 458.696, "r_x2": 472.498, "r_y2": 457.186, "r_x3": 464.9, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 312, "label": "text", "bbox": {"l": 478.914, "t": 455.135, "r": 484.707, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 287, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 456.645, "r_x1": 484.707, "r_y1": 456.645, "r_x2": 484.707, "r_y2": 455.135, "r_x3": 478.914, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Closed ", "orig": "Closed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 313, "label": "text", "bbox": {"l": 478.914, "t": 457.186, "r": 486.608, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 288, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 458.696, "r_x1": 486.608, "r_y1": 458.696, "r_x2": 486.608, "r_y2": 457.186, "r_x3": 478.914, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 314, "label": "text", "bbox": {"l": 493.064, "t": 455.135, "r": 504.207, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 289, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 456.645, "r_x1": 504.207, "r_y1": 456.645, "r_x2": 504.207, "r_y2": 455.135, "r_x3": 493.064, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 315, "label": "text", "bbox": {"l": 449.101, "t": 469.689, "r": 548.597, "b": 471.367, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 290, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 471.367, "r_x1": 548.597, "r_y1": 471.367, "r_x2": 548.597, "r_y2": 469.689, "r_x3": 449.101, "r_y3": 469.689, "coord_origin": "TOPLEFT"}, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 316, "label": "text", "bbox": {"l": 449.101, "t": 471.967, "r": 500.082, "b": 473.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 291, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 473.645, "r_x1": 500.082, "r_y1": 473.645, "r_x2": 500.082, "r_y2": 471.967, "r_x3": 449.101, "r_y3": 471.967, "coord_origin": "TOPLEFT"}, "text": "sions, runway slope, arresting gear, and displaced threshold.", "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 317, "label": "text", "bbox": {"l": 449.101, "t": 476.525, "r": 547.826, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 292, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 478.203, "r_x1": 547.826, "r_y1": 478.203, "r_x2": 547.826, "r_y2": 476.525, "r_x3": 449.101, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "orig": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 318, "label": "text", "bbox": {"l": 547.826, "t": 476.525, "r": 548.459, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 293, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 547.826, "r_y0": 478.203, "r_x1": 548.459, "r_y1": 478.203, "r_x2": 548.459, "r_y2": 476.525, "r_x3": 547.826, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 319, "label": "text", "bbox": {"l": 449.101, "t": 478.803, "r": 470.526, "b": 480.481, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 294, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 480.481, "r_x1": 470.526, "r_y1": 480.481, "r_x2": 470.526, "r_y2": 478.803, "r_x3": 449.101, "r_y3": 478.803, "coord_origin": "TOPLEFT"}, "text": "pads may also be shown.", "orig": "pads may also be shown.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 320, "label": "text", "bbox": {"l": 449.101, "t": 483.361, "r": 493.379, "b": 485.039, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 295, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 485.039, "r_x1": 493.379, "r_y1": 485.039, "r_x2": 493.379, "r_y2": 483.361, "r_x3": 449.101, "r_y3": 483.361, "coord_origin": "TOPLEFT"}, "text": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "orig": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 449.101, "t": 487.918, "r": 551.8, "b": 496.432, "coord_origin": "TOPLEFT"}, "confidence": 0.5027860403060913, "cells": [{"index": 296, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 489.596, "r_x1": 549.162, "r_y1": 489.596, "r_x2": 549.162, "r_y2": 487.918, "r_x3": 449.101, "r_y3": 487.918, "coord_origin": "TOPLEFT"}, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 297, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 491.875, "r_x1": 546.909, "r_y1": 491.875, "r_x2": 546.909, "r_y2": 490.197, "r_x3": 449.101, "r_y3": 490.197, "coord_origin": "TOPLEFT"}, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 298, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 494.154, "r_x1": 551.8, "r_y1": 494.154, "r_x2": 551.8, "r_y2": 492.475, "r_x3": 449.101, "r_y3": 492.475, "coord_origin": "TOPLEFT"}, "text": "DLUSRUW\u00b6VglyphXVDEOHglyphUXQZD\\VglyphPHDVXUHGglyphLQglyphIHHWglyphIURPglyphPHDQglyphVHDglyphOHYHOglyphglyph7KHglyph7'=(glyphLVglyphWKHglyphKLJKHVWglyphHOHYDWLRQglyphLQglyphWKHglyph\u00bfUVWglyphglyphglyphglyphglyphglyphglyphIHHWglyphRIglyph", "orig": "DLUSRUW\u00b6VglyphXVDEOHglyphUXQZD\\VglyphPHDVXUHGglyphLQglyphIHHWglyphIURPglyphPHDQglyphVHDglyphOHYHOglyphglyph7KHglyph7'=(glyphLVglyphWKHglyphKLJKHVWglyphHOHYDWLRQglyphLQglyphWKHglyph\u00bfUVWglyphglyphglyphglyphglyphglyphglyphIHHWglyphRIglyph", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 299, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 496.432, "r_x1": 505.851, "r_y1": 496.432, "r_x2": 505.851, "r_y2": 494.754, "r_x3": 449.101, "r_y3": 494.754, "coord_origin": "TOPLEFT"}, "text": "the landing surface. Circling only approaches wil not show a TDZE.", "orig": "the landing surface. 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 364, "label": "text", "bbox": {"l": 498.153, "t": 268.149, "r": 544.013, "b": 269.95899999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 365, "label": "text", "bbox": {"l": 498.153, "t": 270.192, "r": 544.12, "b": 272.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 366, "label": "text", "bbox": {"l": 498.153, "t": 272.235, "r": 515.411, "b": 274.04499999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. 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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"label": "text", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "text", "bbox": {"l": 336.693, "t": 192.63099999999997, "r": 439.457, "b": 238.466, "coord_origin": "TOPLEFT"}, "confidence": 0.8027974367141724, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.528, "r_y0": 203.03300000000002, "r_x1": 414.848, "r_y1": 203.03300000000002, "r_x2": 414.848, "r_y2": 192.63099999999997, "r_x3": 361.528, "r_y3": 192.63099999999997, "coord_origin": "TOPLEFT"}, "text": "Peter Staar", "orig": "Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 360.02, "r_y0": 214.55499999999995, "r_x1": 416.129, "r_y1": 214.55499999999995, "r_x2": 416.129, "r_y2": 205.88699999999994, "r_x3": 360.02, "r_y3": 205.88699999999994, "coord_origin": "TOPLEFT"}, "text": "IBM Research", "orig": "IBM Research", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.693, "r_y0": 226.51, "r_x1": 439.457, "r_y1": 226.51, "r_x2": 439.457, "r_y2": 217.84199999999998, "r_x3": 336.693, "r_y3": 217.84199999999998, "coord_origin": "TOPLEFT"}, "text": "Rueschlikon, Switzerland", "orig": "Rueschlikon, Switzerland", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.207, "r_y0": 238.466, "r_x1": 429.943, "r_y1": 238.466, "r_x2": 429.943, "r_y2": 229.798, "r_x3": 346.207, "r_y3": 229.798, "coord_origin": "TOPLEFT"}, "text": "taa@zurich.ibm.com", "orig": "taa@zurich.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 53.798, "t": 248.64099999999996, "r": 111.944, "b": 257.70799999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8876155018806458, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 257.70799999999997, "r_x1": 111.944, "r_y1": 257.70799999999997, "r_x2": 111.944, "r_y2": 248.64099999999996, "r_x3": 53.798, "r_y3": 248.64099999999996, "coord_origin": "TOPLEFT"}, "text": "ABSTRACT", "orig": "ABSTRACT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ABSTRACT"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.467, "t": 263.33500000000004, "r": 295.56, "b": 534.15, "coord_origin": "TOPLEFT"}, "confidence": 0.9823057055473328, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 271.13599999999997, "r_x1": 295.556, "r_y1": 271.13599999999997, "r_x2": 295.556, "r_y2": 263.33500000000004, "r_x3": 53.484, "r_y3": 263.33500000000004, "coord_origin": "TOPLEFT"}, "text": "Accurate document layout analysis is a key requirement for high-", "orig": "Accurate document layout analysis is a key requirement for high-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 282.095, "r_x1": 294.042, "r_y1": 282.095, "r_x2": 294.042, "r_y2": 274.294, "r_x3": 53.798, "r_y3": 274.294, "coord_origin": "TOPLEFT"}, "text": "quality PDF document conversion. With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 293.054, "r_x1": 295.346, "r_y1": 293.054, "r_x2": 295.346, "r_y2": 285.253, "r_x3": 53.798, "r_y3": 285.253, "coord_origin": "TOPLEFT"}, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 304.013, "r_x1": 294.047, "r_y1": 304.013, "r_x2": 294.047, "r_y2": 296.212, "r_x3": 53.798, "r_y3": 296.212, "coord_origin": "TOPLEFT"}, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.972, "r_x1": 294.046, "r_y1": 314.972, "r_x2": 294.046, "r_y2": 307.171, "r_x3": 53.798, "r_y3": 307.171, "coord_origin": "TOPLEFT"}, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 512.232, "r_x1": 295.556, "r_y1": 512.232, "r_x2": 295.556, "r_y2": 504.431, "r_x3": 53.798, "r_y3": 504.431, "coord_origin": "TOPLEFT"}, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 523.191, "r_x1": 294.216, "r_y1": 523.191, "r_x2": 294.216, "r_y2": 515.39, "r_x3": 53.798, "r_y3": 515.39, "coord_origin": "TOPLEFT"}, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 534.15, "r_x1": 212.055, "r_y1": 534.15, "r_x2": 212.055, "r_y2": 526.3489999999999, "r_x3": 53.798, "r_y3": 526.3489999999999, "coord_origin": "TOPLEFT"}, "text": "general-purpose document-layout analysis.", "orig": "general-purpose document-layout analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.798, "t": 551.935, "r": 134.82, "b": 561.002, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 561.002, "r_x1": 134.82, "r_y1": 561.002, "r_x2": 134.82, "r_y2": 551.935, "r_x3": 53.798, "r_y3": 551.935, "coord_origin": "TOPLEFT"}, "text": "CCS CONCEPTS", "orig": "CCS CONCEPTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.798, "t": 564.083, "r": 297.853, "b": 597.29, "coord_origin": "TOPLEFT"}, "confidence": 0.8458839058876038, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.431, "r_x1": 56.945, "r_y1": 574.431, "r_x2": 56.945, "r_y2": 566.63, "r_x3": 53.798, "r_y3": 566.63, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 58.44, "r_y0": 574.307, "r_x1": 142.446, "r_y1": 574.307, "r_x2": 142.446, "r_y2": 566.854, "r_x3": 58.44, "r_y3": 566.854, "coord_origin": "TOPLEFT"}, "text": "Informationsystems", "orig": "Informationsystems", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.938, "r_y0": 575.3720000000001, "r_x1": 153.151, "r_y1": 575.3720000000001, "r_x2": 153.151, "r_y2": 564.083, "r_x3": 143.938, "r_y3": 564.083, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.646, "r_y0": 574.307, "r_x1": 235.46, "r_y1": 574.307, "r_x2": 235.46, "r_y2": 566.854, "r_x3": 154.646, "r_y3": 566.854, "coord_origin": "TOPLEFT"}, "text": "Documentstructure", "orig": "Documentstructure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.457, "r_y0": 574.431, "r_x1": 242.174, "r_y1": 574.431, "r_x2": 242.174, "r_y2": 566.63, "r_x3": 235.457, "r_y3": 566.63, "coord_origin": "TOPLEFT"}, "text": "; 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Loosen the retention screw, then rotate ", "orig": "2. Loosen the retention screw, then rotate ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.594, "r_y0": 345.073, "r_x1": 351.67, "r_y1": 345.073, "r_x2": 351.67, "r_y2": 343.961, "r_x3": 329.594, "r_y3": 343.961, "coord_origin": "TOPLEFT"}, "text": "the adjustment ring to change the ", "orig": "the adjustment ring to change the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.269, "r_y0": 347.876, "r_x1": 351.18, "r_y1": 348.298, "r_x2": 351.18, "r_y2": 347.186, "r_x3": 334.269, "r_y3": 346.764, "coord_origin": "TOPLEFT"}, "text": " re that both the images in", "orig": " re that both the images in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.093, "r_y0": 346.504, "r_x1": 346.332, "r_y1": 346.504, "r_x2": 346.332, "r_y2": 345.393, "r_x3": 329.093, "r_y3": 345.393, "coord_origin": "TOPLEFT"}, "text": " length of the vertical tube.", "orig": " length of the vertical tube.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 348.298, "r_x1": 334.702, "r_y1": 348.298, "r_x2": 334.702, "r_y2": 347.186, "r_x3": 327.83, "r_y3": 347.186, "coord_origin": "TOPLEFT"}, "text": "3. Make su", "orig": "3. Make su", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.253, "r_y0": 258.679, "r_x1": 362.6, "r_y1": 258.49800000000005, "r_x2": 362.6, "r_y2": 255.64099999999996, "r_x3": 327.253, "r_y3": 255.82100000000003, "coord_origin": "TOPLEFT"}, "text": "OPERATION (cont.)", "orig": "OPERATION (cont.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 265.153, "r_x1": 345.844, "r_y1": 265.153, "r_x2": 345.844, "r_y2": 263.88300000000004, "r_x3": 326.88, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "SELECTING OBJECTIVE ", "orig": "SELECTING OBJECTIVE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 266.76300000000003, "r_x1": 340.542, "r_y1": 266.76300000000003, "r_x2": 340.542, "r_y2": 265.49300000000005, "r_x3": 326.88, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "MAGNIFICATION", "orig": "MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 268.37300000000005, "r_x1": 355.17, "r_y1": 268.37300000000005, "r_x2": 355.17, "r_y2": 267.10299999999995, "r_x3": 326.88, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": "1. There are two objectives. The lower ", "orig": "1. There are two objectives. The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. 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", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 154, "label": "text", "bbox": {"l": 326.88, "t": 268.71299999999997, "r": 356.201, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 155, "label": "text", "bbox": {"l": 326.88, "t": 270.32399999999996, "r": 345.801, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 156, "label": "text", "bbox": {"l": 326.88, "t": 271.93399999999997, "r": 353.377, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 157, "label": "text", "bbox": {"l": 326.88, "t": 273.544, "r": 353.892, "b": 274.81399999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 158, "label": "text", "bbox": {"l": 326.88, "t": 275.154, "r": 356.017, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 159, "label": "text", "bbox": {"l": 326.88, "t": 276.765, "r": 351.888, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 160, "label": "text", "bbox": {"l": 326.88, "t": 278.375, "r": 335.467, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 161, "label": "text", "bbox": {"l": 326.88, "t": 281.595, "r": 354.578, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 162, "label": "text", "bbox": {"l": 326.88, "t": 283.205, "r": 335.175, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 163, "label": "text", "bbox": {"l": 326.88, "t": 284.816, "r": 355.748, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 164, "label": "text", "bbox": {"l": 326.88, "t": 286.426, "r": 355.576, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 165, "label": "text", "bbox": {"l": 326.88, "t": 288.036, "r": 355.17, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 166, "label": "text", "bbox": {"l": 326.882, "t": 289.646, "r": 356.021, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 167, "label": "text", "bbox": {"l": 326.882, "t": 291.256, "r": 351.575, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 168, "label": "text", "bbox": {"l": 326.882, "t": 292.867, "r": 336.207, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 169, "label": "text", "bbox": {"l": 326.882, "t": 296.087, "r": 335.394, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 170, "label": "text", "bbox": {"l": 326.882, "t": 297.697, "r": 353.186, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 171, "label": "text", "bbox": {"l": 326.883, "t": 299.308, "r": 354.432, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 172, "label": "text", "bbox": {"l": 326.883, "t": 300.918, "r": 333.328, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 173, "label": "text", "bbox": {"l": 326.883, "t": 302.528, "r": 355.709, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 174, "label": "text", "bbox": {"l": 326.883, "t": 304.138, "r": 356.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 175, "label": "text", "bbox": {"l": 326.883, "t": 305.748, "r": 354.466, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 176, "label": "text", "bbox": {"l": 326.883, "t": 307.359, "r": 357.261, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 177, "label": "text", "bbox": {"l": 326.883, "t": 308.969, "r": 356.449, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 178, "label": "text", "bbox": {"l": 326.883, "t": 310.579, "r": 343.173, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 179, "label": "text", "bbox": {"l": 326.883, "t": 313.799, "r": 344.134, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 180, "label": "text", "bbox": {"l": 326.883, "t": 315.41, "r": 348.502, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 181, "label": "text", "bbox": {"l": 326.883, "t": 317.02, "r": 354.081, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 182, "label": "text", "bbox": {"l": 326.885, "t": 318.63, "r": 354.842, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 183, "label": "text", "bbox": {"l": 326.885, "t": 320.24, "r": 348.021, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 184, "label": "text", "bbox": {"l": 326.885, "t": 321.851, "r": 352.968, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 185, "label": "text", "bbox": {"l": 326.886, "t": 323.461, "r": 357.529, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 186, "label": "text", "bbox": {"l": 326.886, "t": 325.071, "r": 350.138, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 187, "label": "text", "bbox": {"l": 326.886, "t": 326.681, "r": 353.141, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 188, "label": "text", "bbox": {"l": 326.886, "t": 328.291, "r": 336.892, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 189, "label": "text", "bbox": {"l": 358.42, "t": 263.88300000000004, "r": 366.933, "b": 265.153, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 190, "label": "text", "bbox": {"l": 358.42, "t": 265.49300000000005, "r": 388.997, "b": 266.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 191, "label": "text", "bbox": {"l": 358.42, "t": 267.10299999999995, "r": 384.59, "b": 268.37300000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 192, "label": "text", "bbox": {"l": 358.422, "t": 268.71299999999997, "r": 385.698, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 193, "label": "text", "bbox": {"l": 358.423, "t": 270.32399999999996, "r": 385.612, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 194, "label": "text", "bbox": {"l": 358.423, "t": 271.93399999999997, "r": 385.389, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 195, "label": "text", "bbox": {"l": 358.423, "t": 275.154, "r": 377.35, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 196, "label": "text", "bbox": {"l": 358.423, "t": 276.765, "r": 387.359, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 197, "label": "text", "bbox": {"l": 358.423, "t": 278.375, "r": 387.748, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 198, "label": "text", "bbox": {"l": 358.423, "t": 279.985, "r": 364.169, "b": 281.255, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 199, "label": "text", "bbox": {"l": 358.423, "t": 281.595, "r": 388.266, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 200, "label": "text", "bbox": {"l": 358.423, "t": 283.205, "r": 381.567, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 201, "label": "text", "bbox": {"l": 358.423, "t": 284.816, "r": 387.673, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 202, "label": "text", "bbox": {"l": 358.425, "t": 286.426, "r": 382.771, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 203, "label": "text", "bbox": {"l": 358.425, "t": 288.036, "r": 367.987, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 204, "label": "text", "bbox": {"l": 358.425, "t": 289.646, "r": 387.601, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 205, "label": "text", "bbox": {"l": 358.425, "t": 291.256, "r": 389.075, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 206, "label": "text", "bbox": {"l": 358.425, "t": 292.867, "r": 373.137, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 207, "label": "text", "bbox": {"l": 358.425, "t": 296.087, "r": 381.745, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 208, "label": "text", "bbox": {"l": 358.425, "t": 297.697, "r": 389.01, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 209, "label": "text", "bbox": {"l": 358.425, "t": 299.308, "r": 382.733, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 210, "label": "text", "bbox": {"l": 358.425, "t": 300.918, "r": 388.701, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 211, "label": "text", "bbox": {"l": 358.425, "t": 302.528, "r": 385.147, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 212, "label": "text", "bbox": {"l": 358.425, "t": 304.138, "r": 364.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 213, "label": "text", "bbox": {"l": 358.425, "t": 305.748, "r": 387.708, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 214, "label": "text", "bbox": {"l": 358.425, "t": 307.359, "r": 387.857, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 215, "label": "text", "bbox": {"l": 358.425, "t": 308.969, "r": 385.414, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 216, "label": "text", "bbox": {"l": 358.425, "t": 310.579, "r": 382.561, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 217, "label": "text", "bbox": {"l": 358.426, "t": 312.189, "r": 386.568, "b": 313.459, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 218, "label": "text", "bbox": {"l": 358.426, "t": 313.799, "r": 385.703, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 219, "label": "text", "bbox": {"l": 358.426, "t": 315.41, "r": 366.744, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 220, "label": "text", "bbox": {"l": 358.426, "t": 317.02, "r": 384.99, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 221, "label": "text", "bbox": {"l": 358.426, "t": 318.63, "r": 386.867, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 222, "label": "text", "bbox": {"l": 358.426, "t": 320.24, "r": 385.949, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 223, "label": "text", "bbox": {"l": 358.426, "t": 321.851, "r": 387.137, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 224, "label": "text", "bbox": {"l": 358.426, "t": 323.461, "r": 384.369, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 225, "label": "text", "bbox": {"l": 358.426, "t": 325.071, "r": 386.093, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 226, "label": "text", "bbox": {"l": 358.426, "t": 326.681, "r": 386.352, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 227, "label": "text", "bbox": {"l": 358.426, "t": 328.291, "r": 383.28, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 228, "label": "text", "bbox": {"l": 358.426, "t": 329.902, "r": 382.634, "b": 331.172, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 229, "label": "text", "bbox": {"l": 358.426, "t": 333.122, "r": 375.677, "b": 334.392, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 230, "label": "text", "bbox": {"l": 358.426, "t": 334.732, "r": 386.737, "b": 336.002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, "r_x3": 358.426, "r_y3": 334.732, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 231, "label": "text", "bbox": {"l": 358.426, "t": 336.342, "r": 372.014, "b": 337.613, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 232, "label": "text", "bbox": {"l": 358.426, "t": 337.953, "r": 385.634, "b": 339.223, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 233, "label": "text", "bbox": {"l": 358.426, "t": 339.563, "r": 386.395, "b": 340.833, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 340.833, "r_x1": 386.395, "r_y1": 340.833, "r_x2": 386.395, "r_y2": 339.563, "r_x3": 358.426, "r_y3": 339.563, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 234, "label": "text", "bbox": {"l": 358.426, "t": 341.173, "r": 379.572, "b": 342.443, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 342.443, "r_x1": 379.572, "r_y1": 342.443, "r_x2": 379.572, "r_y2": 341.173, "r_x3": 358.426, "r_y3": 341.173, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 235, "label": "text", "bbox": {"l": 358.427, "t": 342.783, "r": 384.511, "b": 344.053, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.427, "r_y0": 344.053, "r_x1": 384.511, "r_y1": 344.053, "r_x2": 384.511, "r_y2": 342.783, "r_x3": 358.427, "r_y3": 342.783, "coord_origin": "TOPLEFT"}, "text": "3. 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emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 296, "label": "text", "bbox": {"l": 449.101, "t": 427.727, "r": 522.034, "b": 429.405, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 271, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 429.405, "r_x1": 522.034, "r_y1": 429.405, "r_x2": 522.034, "r_y2": 427.727, "r_x3": 449.101, "r_y3": 427.727, "coord_origin": "TOPLEFT"}, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 297, "label": "text", "bbox": {"l": 449.101, "t": 430.005, "r": 524.672, "b": 431.683, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 272, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 431.683, "r_x1": 524.672, "r_y1": 431.683, "r_x2": 524.672, "r_y2": 430.005, "r_x3": 449.101, "r_y3": 430.005, "coord_origin": "TOPLEFT"}, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 298, "label": "text", "bbox": {"l": 449.101, "t": 432.284, "r": 527.172, "b": 433.962, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 273, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 433.962, "r_x1": 527.172, "r_y1": 433.962, "r_x2": 527.172, "r_y2": 432.284, "r_x3": 449.101, "r_y3": 432.284, "coord_origin": "TOPLEFT"}, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 299, "label": "text", "bbox": {"l": 449.101, "t": 434.563, "r": 502.395, "b": 436.241, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 274, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 436.241, "r_x1": 502.395, "r_y1": 436.241, "r_x2": 502.395, "r_y2": 434.563, "r_x3": 449.101, "r_y3": 434.563, "coord_origin": "TOPLEFT"}, "text": "dimensions (length and width) are shown for al active runways.", "orig": "dimensions (length and width) are shown for al active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 300, "label": "text", "bbox": {"l": 449.101, "t": 439.12, "r": 512.927, "b": 440.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 275, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 440.798, "r_x1": 512.927, "r_y1": 440.798, "r_x2": 512.927, "r_y2": 439.12, "r_x3": 449.101, "r_y3": 439.12, "coord_origin": "TOPLEFT"}, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 301, "label": "text", "bbox": {"l": 449.955, "t": 444.393, "r": 460.023, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 276, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 445.903, "r_x1": 460.023, "r_y1": 445.903, "r_x2": 460.023, "r_y2": 444.393, "r_x3": 449.955, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 302, "label": "text", "bbox": {"l": 464.9, "t": 444.393, "r": 473.988, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 277, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 445.903, "r_x1": 473.988, "r_y1": 445.903, "r_x2": 473.988, "r_y2": 444.393, "r_x3": 464.9, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 303, "label": "text", "bbox": {"l": 464.9, "t": 446.444, "r": 474.967, "b": 447.954, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 278, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 447.954, "r_x1": 474.967, "r_y1": 447.954, "r_x2": 474.967, "r_y2": 446.444, "r_x3": 464.9, "r_y3": 446.444, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 304, "label": "text", "bbox": {"l": 478.914, "t": 444.393, "r": 489.457, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 279, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 445.903, "r_x1": 489.457, "r_y1": 445.903, "r_x2": 489.457, "r_y2": 444.393, "r_x3": 478.914, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 305, "label": "text", "bbox": {"l": 493.064, "t": 444.393, "r": 505.031, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 280, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 445.903, "r_x1": 505.031, "r_y1": 445.903, "r_x2": 505.031, "r_y2": 444.393, "r_x3": 493.064, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 306, "label": "text", "bbox": {"l": 509.581, "t": 444.393, "r": 524.302, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 281, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.581, "r_y0": 445.903, "r_x1": 524.302, "r_y1": 445.903, "r_x2": 524.302, "r_y2": 444.393, "r_x3": 509.581, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 307, "label": "text", "bbox": {"l": 449.955, "t": 455.135, "r": 458.314, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 282, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 456.645, "r_x1": 458.314, "r_y1": 456.645, "r_x2": 458.314, "r_y2": 455.135, "r_x3": 449.955, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 308, "label": "text", "bbox": {"l": 449.955, "t": 457.186, "r": 461.921, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 283, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 458.696, "r_x1": 461.921, "r_y1": 458.696, "r_x2": 461.921, "r_y2": 457.186, "r_x3": 449.955, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 309, "label": "text", "bbox": {"l": 449.955, "t": 459.237, "r": 457.08, "b": 460.747, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 284, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 460.747, "r_x1": 457.08, "r_y1": 460.747, "r_x2": 457.08, "r_y2": 459.237, "r_x3": 449.955, "r_y3": 459.237, "coord_origin": "TOPLEFT"}, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 310, "label": "text", "bbox": {"l": 464.9, "t": 455.135, "r": 472.877, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 285, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 456.645, "r_x1": 472.877, "r_y1": 456.645, "r_x2": 472.877, "r_y2": 455.135, "r_x3": 464.9, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 311, "label": "text", "bbox": {"l": 464.9, "t": 457.186, "r": 472.498, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 286, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 458.696, "r_x1": 472.498, "r_y1": 458.696, "r_x2": 472.498, "r_y2": 457.186, "r_x3": 464.9, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 312, "label": "text", "bbox": {"l": 478.914, "t": 455.135, "r": 484.707, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 287, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 456.645, "r_x1": 484.707, "r_y1": 456.645, "r_x2": 484.707, "r_y2": 455.135, "r_x3": 478.914, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Closed ", "orig": "Closed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 313, "label": "text", "bbox": {"l": 478.914, "t": 457.186, "r": 486.608, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 288, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 458.696, "r_x1": 486.608, "r_y1": 458.696, "r_x2": 486.608, "r_y2": 457.186, "r_x3": 478.914, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 314, "label": "text", "bbox": {"l": 493.064, "t": 455.135, "r": 504.207, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 289, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 456.645, "r_x1": 504.207, "r_y1": 456.645, "r_x2": 504.207, "r_y2": 455.135, "r_x3": 493.064, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 315, "label": "text", "bbox": {"l": 449.101, "t": 469.689, "r": 548.597, "b": 471.367, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 290, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 471.367, "r_x1": 548.597, "r_y1": 471.367, "r_x2": 548.597, "r_y2": 469.689, "r_x3": 449.101, "r_y3": 469.689, "coord_origin": "TOPLEFT"}, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 316, "label": "text", "bbox": {"l": 449.101, "t": 471.967, "r": 500.082, "b": 473.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 291, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 473.645, "r_x1": 500.082, "r_y1": 473.645, "r_x2": 500.082, "r_y2": 471.967, "r_x3": 449.101, "r_y3": 471.967, "coord_origin": "TOPLEFT"}, "text": "sions, runway slope, arresting gear, and displaced threshold.", "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 317, "label": "text", "bbox": {"l": 449.101, "t": 476.525, "r": 547.826, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 292, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 478.203, "r_x1": 547.826, "r_y1": 478.203, "r_x2": 547.826, "r_y2": 476.525, "r_x3": 449.101, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "orig": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 318, "label": "text", "bbox": {"l": 547.826, "t": 476.525, "r": 548.459, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 293, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 547.826, "r_y0": 478.203, "r_x1": 548.459, "r_y1": 478.203, "r_x2": 548.459, "r_y2": 476.525, "r_x3": 547.826, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 319, "label": "text", "bbox": {"l": 449.101, "t": 478.803, "r": 470.526, "b": 480.481, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 294, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 480.481, "r_x1": 470.526, "r_y1": 480.481, "r_x2": 470.526, "r_y2": 478.803, "r_x3": 449.101, "r_y3": 478.803, "coord_origin": "TOPLEFT"}, "text": "pads may also be shown.", "orig": "pads may also be shown.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 320, "label": "text", "bbox": {"l": 449.101, "t": 483.361, "r": 493.379, "b": 485.039, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 295, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 485.039, "r_x1": 493.379, "r_y1": 485.039, "r_x2": 493.379, "r_y2": 483.361, "r_x3": 449.101, "r_y3": 483.361, "coord_origin": "TOPLEFT"}, "text": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "orig": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 449.101, "t": 487.918, "r": 551.8, "b": 496.432, "coord_origin": "TOPLEFT"}, "confidence": 0.5027860403060913, "cells": [{"index": 296, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 489.596, "r_x1": 549.162, "r_y1": 489.596, "r_x2": 549.162, "r_y2": 487.918, "r_x3": 449.101, "r_y3": 487.918, "coord_origin": "TOPLEFT"}, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 297, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 491.875, "r_x1": 546.909, "r_y1": 491.875, "r_x2": 546.909, "r_y2": 490.197, "r_x3": 449.101, "r_y3": 490.197, "coord_origin": "TOPLEFT"}, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. 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Circling only approaches wil not show a TDZE.", "orig": "the landing surface. 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", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 364, "label": "text", "bbox": {"l": 498.153, "t": 268.149, "r": 544.013, "b": 269.95899999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 365, "label": "text", "bbox": {"l": 498.153, "t": 270.192, "r": 544.12, "b": 272.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 366, "label": "text", "bbox": {"l": 498.153, "t": 272.235, "r": 515.411, "b": 274.04499999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 367, "label": "text", "bbox": {"l": 498.153, "t": 277.663, "r": 505.646, "b": 280.851, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 368, "label": "text", "bbox": {"l": 498.153, "t": 281.996, "r": 543.93, "b": 283.805, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 369, "label": "text", "bbox": {"l": 498.153, "t": 284.039, "r": 545.678, "b": 285.848, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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", "orig": "that are new to you. 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Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com"}, {"label": "text", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "text", "bbox": {"l": 336.693, "t": 192.63099999999997, "r": 439.457, "b": 238.466, "coord_origin": "TOPLEFT"}, "confidence": 0.8027974367141724, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 361.528, "r_y0": 203.03300000000002, "r_x1": 414.848, "r_y1": 203.03300000000002, "r_x2": 414.848, "r_y2": 192.63099999999997, "r_x3": 361.528, "r_y3": 192.63099999999997, "coord_origin": "TOPLEFT"}, "text": "Peter Staar", "orig": "Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 360.02, "r_y0": 214.55499999999995, "r_x1": 416.129, "r_y1": 214.55499999999995, "r_x2": 416.129, "r_y2": 205.88699999999994, "r_x3": 360.02, "r_y3": 205.88699999999994, "coord_origin": "TOPLEFT"}, "text": "IBM Research", "orig": "IBM Research", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.693, "r_y0": 226.51, "r_x1": 439.457, "r_y1": 226.51, "r_x2": 439.457, "r_y2": 217.84199999999998, "r_x3": 336.693, "r_y3": 217.84199999999998, "coord_origin": "TOPLEFT"}, "text": "Rueschlikon, Switzerland", "orig": "Rueschlikon, Switzerland", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.207, "r_y0": 238.466, "r_x1": 429.943, "r_y1": 238.466, "r_x2": 429.943, "r_y2": 229.798, "r_x3": 346.207, "r_y3": 229.798, "coord_origin": "TOPLEFT"}, "text": "taa@zurich.ibm.com", "orig": "taa@zurich.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com"}, {"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 53.798, "t": 248.64099999999996, "r": 111.944, "b": 257.70799999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.8876155018806458, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 257.70799999999997, "r_x1": 111.944, "r_y1": 257.70799999999997, "r_x2": 111.944, "r_y2": 248.64099999999996, "r_x3": 53.798, "r_y3": 248.64099999999996, "coord_origin": "TOPLEFT"}, "text": "ABSTRACT", "orig": "ABSTRACT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ABSTRACT"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.467, "t": 263.33500000000004, "r": 295.56, "b": 534.15, "coord_origin": "TOPLEFT"}, "confidence": 0.9823057055473328, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 271.13599999999997, "r_x1": 295.556, "r_y1": 271.13599999999997, "r_x2": 295.556, "r_y2": 263.33500000000004, "r_x3": 53.484, "r_y3": 263.33500000000004, "coord_origin": "TOPLEFT"}, "text": "Accurate document layout analysis is a key requirement for high-", "orig": "Accurate document layout analysis is a key requirement for high-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 282.095, "r_x1": 294.042, "r_y1": 282.095, "r_x2": 294.042, "r_y2": 274.294, "r_x3": 53.798, "r_y3": 274.294, "coord_origin": "TOPLEFT"}, "text": "quality PDF document conversion. With the recent availability of", "orig": "quality PDF document conversion. With the recent availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 293.054, "r_x1": 295.346, "r_y1": 293.054, "r_x2": 295.346, "r_y2": 285.253, "r_x3": 53.798, "r_y3": 285.253, "coord_origin": "TOPLEFT"}, "text": "public, large ground-truth datasets such as PubLayNet and DocBank,", "orig": "public, large ground-truth datasets such as PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 304.013, "r_x1": 294.047, "r_y1": 304.013, "r_x2": 294.047, "r_y2": 296.212, "r_x3": 53.798, "r_y3": 296.212, "coord_origin": "TOPLEFT"}, "text": "deep-learning models have proven to be very effective at layout", "orig": "deep-learning models have proven to be very effective at layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.972, "r_x1": 294.046, "r_y1": 314.972, "r_x2": 294.046, "r_y2": 307.171, "r_x3": 53.798, "r_y3": 307.171, "coord_origin": "TOPLEFT"}, "text": "detection and segmentation. While these datasets are of adequate", "orig": "detection and segmentation. While these datasets are of adequate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.931, "r_x1": 294.276, "r_y1": 325.931, "r_x2": 294.276, "r_y2": 318.13, "r_x3": 53.798, "r_y3": 318.13, "coord_origin": "TOPLEFT"}, "text": "size to train such models, they severely lack in layout variability", "orig": "size to train such models, they severely lack in layout variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.89, "r_x1": 294.047, "r_y1": 336.89, "r_x2": 294.047, "r_y2": 329.089, "r_x3": 53.798, "r_y3": 329.089, "coord_origin": "TOPLEFT"}, "text": "since they are sourced from scientific article repositories such as", "orig": "since they are sourced from scientific article repositories such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 347.849, "r_x1": 294.044, "r_y1": 347.849, "r_x2": 294.044, "r_y2": 340.048, "r_x3": 53.798, "r_y3": 340.048, "coord_origin": "TOPLEFT"}, "text": "PubMed and arXiv only. Consequently, the accuracy of the layout", "orig": "PubMed and arXiv only. Consequently, the accuracy of the layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 358.808, "r_x1": 294.047, "r_y1": 358.808, "r_x2": 294.047, "r_y2": 351.007, "r_x3": 53.798, "r_y3": 351.007, "coord_origin": "TOPLEFT"}, "text": "segmentation drops significantly when these models are applied", "orig": "segmentation drops significantly when these models are applied", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 369.766, "r_x1": 294.044, "r_y1": 369.766, "r_x2": 294.044, "r_y2": 361.965, "r_x3": 53.798, "r_y3": 361.965, "coord_origin": "TOPLEFT"}, "text": "on more challenging and diverse layouts. In this paper, we present", "orig": "on more challenging and diverse layouts. In this paper, we present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 380.769, "r_x1": 92.863, "r_y1": 380.769, "r_x2": 92.863, "r_y2": 372.861, "r_x3": 53.798, "r_y3": 372.861, "coord_origin": "TOPLEFT"}, "text": "DocLayNet", "orig": "DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 92.864, "r_y0": 380.725, "r_x1": 94.821, "r_y1": 380.725, "r_x2": 94.821, "r_y2": 372.924, "r_x3": 92.864, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.062, "r_y0": 380.725, "r_x1": 294.044, "r_y1": 380.725, "r_x2": 294.044, "r_y2": 372.924, "r_x3": 97.062, "r_y3": 372.924, "coord_origin": "TOPLEFT"}, "text": "a new, publicly available, document-layout annotation", "orig": "a new, publicly available, document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 391.684, "r_x1": 294.047, "r_y1": 391.684, "r_x2": 294.047, "r_y2": 383.883, "r_x3": 53.798, "r_y3": 383.883, "coord_origin": "TOPLEFT"}, "text": "dataset in COCO format. It contains 80863 manually annotated", "orig": "dataset in COCO format. It contains 80863 manually annotated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 402.643, "r_x1": 294.044, "r_y1": 402.643, "r_x2": 294.044, "r_y2": 394.842, "r_x3": 53.798, "r_y3": 394.842, "coord_origin": "TOPLEFT"}, "text": "pages from diverse data sources to represent a wide variability in", "orig": "pages from diverse data sources to represent a wide variability in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 413.602, "r_x1": 294.045, "r_y1": 413.602, "r_x2": 294.045, "r_y2": 405.801, "r_x3": 53.798, "r_y3": 405.801, "coord_origin": "TOPLEFT"}, "text": "layouts. For each PDF page, the layout annotations provide labelled", "orig": "layouts. For each PDF page, the layout annotations provide labelled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 424.561, "r_x1": 294.047, "r_y1": 424.561, "r_x2": 294.047, "r_y2": 416.76, "r_x3": 53.798, "r_y3": 416.76, "coord_origin": "TOPLEFT"}, "text": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "orig": "bounding-boxes with a choice of 11 distinct classes. DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 435.52, "r_x1": 294.047, "r_y1": 435.52, "r_x2": 294.047, "r_y2": 427.719, "r_x3": 53.798, "r_y3": 427.719, "coord_origin": "TOPLEFT"}, "text": "also provides a subset of double- and triple-annotated pages to", "orig": "also provides a subset of double- and triple-annotated pages to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 446.479, "r_x1": 295.03, "r_y1": 446.479, "r_x2": 295.03, "r_y2": 438.678, "r_x3": 53.798, "r_y3": 438.678, "coord_origin": "TOPLEFT"}, "text": "determine the inter-annotator agreement. In multiple experiments,", "orig": "determine the inter-annotator agreement. In multiple experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 457.438, "r_x1": 294.216, "r_y1": 457.438, "r_x2": 294.216, "r_y2": 449.637, "r_x3": 53.467, "r_y3": 449.637, "coord_origin": "TOPLEFT"}, "text": "we provide baseline accuracy scores (in mAP) for a set of popular", "orig": "we provide baseline accuracy scores (in mAP) for a set of popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 468.397, "r_x1": 294.047, "r_y1": 468.397, "r_x2": 294.047, "r_y2": 460.596, "r_x3": 53.798, "r_y3": 460.596, "coord_origin": "TOPLEFT"}, "text": "object detection models. We also demonstrate that these models", "orig": "object detection models. We also demonstrate that these models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 479.355, "r_x1": 295.56, "r_y1": 479.355, "r_x2": 295.56, "r_y2": 471.554, "r_x3": 53.798, "r_y3": 471.554, "coord_origin": "TOPLEFT"}, "text": "fall approximately 10% behind the inter-annotator agreement. Fur-", "orig": "fall approximately 10% behind the inter-annotator agreement. Fur-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 490.314, "r_x1": 295.428, "r_y1": 490.314, "r_x2": 295.428, "r_y2": 482.513, "r_x3": 53.798, "r_y3": 482.513, "coord_origin": "TOPLEFT"}, "text": "thermore, we provide evidence that DocLayNet is of sufficient size.", "orig": "thermore, we provide evidence that DocLayNet is of sufficient size.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 501.273, "r_x1": 294.047, "r_y1": 501.273, "r_x2": 294.047, "r_y2": 493.472, "r_x3": 53.798, "r_y3": 493.472, "coord_origin": "TOPLEFT"}, "text": "Lastly, we compare models trained on PubLayNet, DocBank and", "orig": "Lastly, we compare models trained on PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 512.232, "r_x1": 295.556, "r_y1": 512.232, "r_x2": 295.556, "r_y2": 504.431, "r_x3": 53.798, "r_y3": 504.431, "coord_origin": "TOPLEFT"}, "text": "DocLayNet, showing that layout predictions of the DocLayNet-", "orig": "DocLayNet, showing that layout predictions of the DocLayNet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 523.191, "r_x1": 294.216, "r_y1": 523.191, "r_x2": 294.216, "r_y2": 515.39, "r_x3": 53.798, "r_y3": 515.39, "coord_origin": "TOPLEFT"}, "text": "trained models are more robust and thus the preferred choice for", "orig": "trained models are more robust and thus the preferred choice for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 534.15, "r_x1": 212.055, "r_y1": 534.15, "r_x2": 212.055, "r_y2": 526.3489999999999, "r_x3": 53.798, "r_y3": 526.3489999999999, "coord_origin": "TOPLEFT"}, "text": "general-purpose document-layout analysis.", "orig": "general-purpose document-layout analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis."}, {"label": "section_header", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 53.798, "t": 551.935, "r": 134.82, "b": 561.002, "coord_origin": "TOPLEFT"}, "confidence": 0.9149598479270935, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 561.002, "r_x1": 134.82, "r_y1": 561.002, "r_x2": 134.82, "r_y2": 551.935, "r_x3": 53.798, "r_y3": 551.935, "coord_origin": "TOPLEFT"}, "text": "CCS CONCEPTS", "orig": "CCS CONCEPTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CCS CONCEPTS"}, {"label": "text", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "text", "bbox": {"l": 53.798, "t": 564.083, "r": 297.853, "b": 597.29, "coord_origin": "TOPLEFT"}, "confidence": 0.8458839058876038, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.431, "r_x1": 56.945, "r_y1": 574.431, "r_x2": 56.945, "r_y2": 566.63, "r_x3": 53.798, "r_y3": 566.63, "coord_origin": "TOPLEFT"}, "text": "\u2022", "orig": "\u2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 58.44, "r_y0": 574.307, "r_x1": 142.446, "r_y1": 574.307, "r_x2": 142.446, "r_y2": 566.854, "r_x3": 58.44, "r_y3": 566.854, "coord_origin": "TOPLEFT"}, "text": "Informationsystems", "orig": "Informationsystems", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.938, "r_y0": 575.3720000000001, "r_x1": 153.151, "r_y1": 575.3720000000001, "r_x2": 153.151, "r_y2": 564.083, "r_x3": 143.938, "r_y3": 564.083, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.646, "r_y0": 574.307, "r_x1": 235.46, "r_y1": 574.307, "r_x2": 235.46, "r_y2": 566.854, "r_x3": 154.646, "r_y3": 566.854, "coord_origin": "TOPLEFT"}, "text": "Documentstructure", "orig": "Documentstructure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.457, "r_y0": 574.431, "r_x1": 242.174, "r_y1": 574.431, "r_x2": 242.174, "r_y2": 566.63, "r_x3": 235.457, "r_y3": 566.63, "coord_origin": "TOPLEFT"}, "text": "; 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Loosen the retention screw, then rotate ", "orig": "2. Loosen the retention screw, then rotate ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.594, "r_y0": 345.073, "r_x1": 351.67, "r_y1": 345.073, "r_x2": 351.67, "r_y2": 343.961, "r_x3": 329.594, "r_y3": 343.961, "coord_origin": "TOPLEFT"}, "text": "the adjustment ring to change the ", "orig": "the adjustment ring to change the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.269, "r_y0": 347.876, "r_x1": 351.18, "r_y1": 348.298, "r_x2": 351.18, "r_y2": 347.186, "r_x3": 334.269, "r_y3": 346.764, "coord_origin": "TOPLEFT"}, "text": " re that both the images in", "orig": " re that both the images in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.093, "r_y0": 346.504, "r_x1": 346.332, "r_y1": 346.504, "r_x2": 346.332, "r_y2": 345.393, "r_x3": 329.093, "r_y3": 345.393, "coord_origin": "TOPLEFT"}, "text": " length of the vertical tube.", "orig": " length of the vertical tube.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.83, "r_y0": 348.298, "r_x1": 334.702, "r_y1": 348.298, "r_x2": 334.702, "r_y2": 347.186, "r_x3": 327.83, "r_y3": 347.186, "coord_origin": "TOPLEFT"}, "text": "3. Make su", "orig": "3. Make su", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.253, "r_y0": 258.679, "r_x1": 362.6, "r_y1": 258.49800000000005, "r_x2": 362.6, "r_y2": 255.64099999999996, "r_x3": 327.253, "r_y3": 255.82100000000003, "coord_origin": "TOPLEFT"}, "text": "OPERATION (cont.)", "orig": "OPERATION (cont.)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 265.153, "r_x1": 345.844, "r_y1": 265.153, "r_x2": 345.844, "r_y2": 263.88300000000004, "r_x3": 326.88, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "SELECTING OBJECTIVE ", "orig": "SELECTING OBJECTIVE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 266.76300000000003, "r_x1": 340.542, "r_y1": 266.76300000000003, "r_x2": 340.542, "r_y2": 265.49300000000005, "r_x3": 326.88, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "MAGNIFICATION", "orig": "MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 268.37300000000005, "r_x1": 355.17, "r_y1": 268.37300000000005, "r_x2": 355.17, "r_y2": 267.10299999999995, "r_x3": 326.88, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": "1. There are two objectives. The lower ", "orig": "1. There are two objectives. The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, 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Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. 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", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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The lower ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 154, "label": "text", "bbox": {"l": 326.88, "t": 268.71299999999997, "r": 356.201, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 269.98400000000004, "r_x1": 356.201, "r_y1": 269.98400000000004, "r_x2": 356.201, "r_y2": 268.71299999999997, "r_x3": 326.88, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": " magnification objective has a greater ", "orig": " magnification objective has a greater ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 155, "label": "text", "bbox": {"l": 326.88, "t": 270.32399999999996, "r": 345.801, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 271.59400000000005, "r_x1": 345.801, "r_y1": 271.59400000000005, "r_x2": 345.801, "r_y2": 270.32399999999996, "r_x3": 326.88, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " depth of field and view.", "orig": " depth of field and view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 156, "label": "text", "bbox": {"l": 326.88, "t": 271.93399999999997, "r": 353.377, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 273.20399999999995, "r_x1": 353.377, "r_y1": 273.20399999999995, "r_x2": 353.377, "r_y2": 271.93399999999997, "r_x3": 326.88, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": "2. In order to observe the specimen ", "orig": "2. In order to observe the specimen ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 157, "label": "text", "bbox": {"l": 326.88, "t": 273.544, "r": 353.892, "b": 274.81399999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 274.81399999999996, "r_x1": 353.892, "r_y1": 274.81399999999996, "r_x2": 353.892, "r_y2": 273.544, "r_x3": 326.88, "r_y3": 273.544, "coord_origin": "TOPLEFT"}, "text": " easily use the lower magnification ", "orig": " easily use the lower magnification ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 158, "label": "text", "bbox": {"l": 326.88, "t": 275.154, "r": 356.017, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 276.424, "r_x1": 356.017, "r_y1": 276.424, "r_x2": 356.017, "r_y2": 275.154, "r_x3": 326.88, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": " objective first. Then, by rotating the ", "orig": " objective first. Then, by rotating the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 159, "label": "text", "bbox": {"l": 326.88, "t": 276.765, "r": 351.888, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 278.03499999999997, "r_x1": 351.888, "r_y1": 278.03499999999997, "r_x2": 351.888, "r_y2": 276.765, "r_x3": 326.88, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": " case, the magnification can be ", "orig": " case, the magnification can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 160, "label": "text", "bbox": {"l": 326.88, "t": 278.375, "r": 335.467, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 279.645, "r_x1": 335.467, "r_y1": 279.645, "r_x2": 335.467, "r_y2": 278.375, "r_x3": 326.88, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " changed.", "orig": " changed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 161, "label": "text", "bbox": {"l": 326.88, "t": 281.595, "r": 354.578, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 282.865, "r_x1": 354.578, "r_y1": 282.865, "r_x2": 354.578, "r_y2": 281.595, "r_x3": 326.88, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE INTERPUPILLARY ", "orig": "CHANGING THE INTERPUPILLARY ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 162, "label": "text", "bbox": {"l": 326.88, "t": 283.205, "r": 335.175, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 284.476, "r_x1": 335.175, "r_y1": 284.476, "r_x2": 335.175, "r_y2": 283.205, "r_x3": 326.88, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": "DISTANCE", "orig": "DISTANCE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 163, "label": "text", "bbox": {"l": 326.88, "t": 284.816, "r": 355.748, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 286.086, "r_x1": 355.748, "r_y1": 286.086, "r_x2": 355.748, "r_y2": 284.816, "r_x3": 326.88, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": "1. The distance between the observer's ", "orig": "1. The distance between the observer's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 164, "label": "text", "bbox": {"l": 326.88, "t": 286.426, "r": 355.576, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 287.696, "r_x1": 355.576, "r_y1": 287.696, "r_x2": 355.576, "r_y2": 286.426, "r_x3": 326.88, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " pupils is the interpupil ary distance. ", "orig": " pupils is the interpupil ary distance. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 165, "label": "text", "bbox": {"l": 326.88, "t": 288.036, "r": 355.17, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.88, "r_y0": 289.306, "r_x1": 355.17, "r_y1": 289.306, "r_x2": 355.17, "r_y2": 288.036, "r_x3": 326.88, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": "2. To adjust the interpupil ary distance ", "orig": "2. To adjust the interpupil ary distance ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 166, "label": "text", "bbox": {"l": 326.882, "t": 289.646, "r": 356.021, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 290.916, "r_x1": 356.021, "r_y1": 290.916, "r_x2": 356.021, "r_y2": 289.646, "r_x3": 326.882, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": " rotate the prism caps until both eyes ", "orig": " rotate the prism caps until both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 167, "label": "text", "bbox": {"l": 326.882, "t": 291.256, "r": 351.575, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 292.527, "r_x1": 351.575, "r_y1": 292.527, "r_x2": 351.575, "r_y2": 291.256, "r_x3": 326.882, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " coincide with the image in the ", "orig": " coincide with the image in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 168, "label": "text", "bbox": {"l": 326.882, "t": 292.867, "r": 336.207, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 294.137, "r_x1": 336.207, "r_y1": 294.137, "r_x2": 336.207, "r_y2": 292.867, "r_x3": 326.882, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " eyepiece. ", "orig": " eyepiece. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 169, "label": "text", "bbox": {"l": 326.882, "t": 296.087, "r": 335.394, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 297.357, "r_x1": 335.394, "r_y1": 297.357, "r_x2": 335.394, "r_y2": 296.087, "r_x3": 326.882, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 170, "label": "text", "bbox": {"l": 326.882, "t": 297.697, "r": 353.186, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.882, "r_y0": 298.967, "r_x1": 353.186, "r_y1": 298.967, "r_x2": 353.186, "r_y2": 297.697, "r_x3": 326.882, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. Remove the lens protective cover.", "orig": "1. Remove the lens protective cover.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 171, "label": "text", "bbox": {"l": 326.883, "t": 299.308, "r": 354.432, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 300.578, "r_x1": 354.432, "r_y1": 300.578, "r_x2": 354.432, "r_y2": 299.308, "r_x3": 326.883, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": "2. Place the specimen on the working ", "orig": "2. Place the specimen on the working ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 172, "label": "text", "bbox": {"l": 326.883, "t": 300.918, "r": 333.328, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 302.188, "r_x1": 333.328, "r_y1": 302.188, "r_x2": 333.328, "r_y2": 300.918, "r_x3": 326.883, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " stage.", "orig": " stage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 173, "label": "text", "bbox": {"l": 326.883, "t": 302.528, "r": 355.709, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 303.798, "r_x1": 355.709, "r_y1": 303.798, "r_x2": 355.709, "r_y2": 302.528, "r_x3": 326.883, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": "3. Focus the specimen with the left eye ", "orig": "3. Focus the specimen with the left eye ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 174, "label": "text", "bbox": {"l": 326.883, "t": 304.138, "r": 356.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 305.408, "r_x1": 356.887, "r_y1": 305.408, "r_x2": 356.887, "r_y2": 304.138, "r_x3": 326.883, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " first while turning the focus knob until ", "orig": " first while turning the focus knob until ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 175, "label": "text", "bbox": {"l": 326.883, "t": 305.748, "r": 354.466, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 307.019, "r_x1": 354.466, "r_y1": 307.019, "r_x2": 354.466, "r_y2": 305.748, "r_x3": 326.883, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " the image appears clear and sharp.", "orig": " the image appears clear and sharp.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 176, "label": "text", "bbox": {"l": 326.883, "t": 307.359, "r": 357.261, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 308.629, "r_x1": 357.261, "r_y1": 308.629, "r_x2": 357.261, "r_y2": 307.359, "r_x3": 326.883, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": "4. Rotate the right eyepiece ring until the ", "orig": "4. Rotate the right eyepiece ring until the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 177, "label": "text", "bbox": {"l": 326.883, "t": 308.969, "r": 356.449, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 310.239, "r_x1": 356.449, "r_y1": 310.239, "r_x2": 356.449, "r_y2": 308.969, "r_x3": 326.883, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " images in each eyepiece coincide and ", "orig": " images in each eyepiece coincide and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 178, "label": "text", "bbox": {"l": 326.883, "t": 310.579, "r": 343.173, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 138, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 311.849, "r_x1": 343.173, "r_y1": 311.849, "r_x2": 343.173, "r_y2": 310.579, "r_x3": 326.883, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " are sharp and clear.", "orig": " are sharp and clear.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 179, "label": "text", "bbox": {"l": 326.883, "t": 313.799, "r": 344.134, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 139, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 315.07, "r_x1": 344.134, "r_y1": 315.07, "r_x2": 344.134, "r_y2": 313.799, "r_x3": 326.883, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 180, "label": "text", "bbox": {"l": 326.883, "t": 315.41, "r": 348.502, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 140, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 316.68, "r_x1": 348.502, "r_y1": 316.68, "r_x2": 348.502, "r_y2": 315.41, "r_x3": 326.883, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord.", "orig": "1. Disconnect the power cord.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 181, "label": "text", "bbox": {"l": 326.883, "t": 317.02, "r": 354.081, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 141, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.883, "r_y0": 318.29, "r_x1": 354.081, "r_y1": 318.29, "r_x2": 354.081, "r_y2": 317.02, "r_x3": 326.883, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 182, "label": "text", "bbox": {"l": 326.885, "t": 318.63, "r": 354.842, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 142, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 319.9, "r_x1": 354.842, "r_y1": 319.9, "r_x2": 354.842, "r_y2": 318.63, "r_x3": 326.885, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 183, "label": "text", "bbox": {"l": 326.885, "t": 320.24, "r": 348.021, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 321.51, "r_x1": 348.021, "r_y1": 321.51, "r_x2": 348.021, "r_y2": 320.24, "r_x3": 326.885, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 184, "label": "text", "bbox": {"l": 326.885, "t": 321.851, "r": 352.968, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.885, "r_y0": 323.121, "r_x1": 352.968, "r_y1": 323.121, "r_x2": 352.968, "r_y2": 321.851, "r_x3": 326.885, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": "3. Replace with a new halogen bulb.", "orig": "3. Replace with a new halogen bulb.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 185, "label": "text", "bbox": {"l": 326.886, "t": 323.461, "r": 357.529, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 324.731, "r_x1": 357.529, "r_y1": 324.731, "r_x2": 357.529, "r_y2": 323.461, "r_x3": 326.886, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": "4. Open the window in the base plate and ", "orig": "4. Open the window in the base plate and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 186, "label": "text", "bbox": {"l": 326.886, "t": 325.071, "r": 350.138, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 326.341, "r_x1": 350.138, "r_y1": 326.341, "r_x2": 350.138, "r_y2": 325.071, "r_x3": 326.886, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " replace the halogen lamp or ", "orig": " replace the halogen lamp or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 187, "label": "text", "bbox": {"l": 326.886, "t": 326.681, "r": 353.141, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 327.951, "r_x1": 353.141, "r_y1": 327.951, "r_x2": 353.141, "r_y2": 326.681, "r_x3": 326.886, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " fluorescent lamp of transmitted ", "orig": " fluorescent lamp of transmitted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 188, "label": "text", "bbox": {"l": 326.886, "t": 328.291, "r": 336.892, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 326.886, "r_y0": 329.561, "r_x1": 336.892, "r_y1": 329.561, "r_x2": 336.892, "r_y2": 328.291, "r_x3": 326.886, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " il uminator.", "orig": " il uminator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 189, "label": "text", "bbox": {"l": 358.42, "t": 263.88300000000004, "r": 366.933, "b": 265.153, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 265.153, "r_x1": 366.933, "r_y1": 265.153, "r_x2": 366.933, "r_y2": 263.88300000000004, "r_x3": 358.42, "r_y3": 263.88300000000004, "coord_origin": "TOPLEFT"}, "text": "FOCUSING", "orig": "FOCUSING", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 190, "label": "text", "bbox": {"l": 358.42, "t": 265.49300000000005, "r": 388.997, "b": 266.76300000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 266.76300000000003, "r_x1": 388.997, "r_y1": 266.76300000000003, "r_x2": 388.997, "r_y2": 265.49300000000005, "r_x3": 358.42, "r_y3": 265.49300000000005, "coord_origin": "TOPLEFT"}, "text": "1. Turn the focusing knob away or toward ", "orig": "1. Turn the focusing knob away or toward ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 191, "label": "text", "bbox": {"l": 358.42, "t": 267.10299999999995, "r": 384.59, "b": 268.37300000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.42, "r_y0": 268.37300000000005, "r_x1": 384.59, "r_y1": 268.37300000000005, "r_x2": 384.59, "r_y2": 267.10299999999995, "r_x3": 358.42, "r_y3": 267.10299999999995, "coord_origin": "TOPLEFT"}, "text": " you until a clear image is viewed.", "orig": " you until a clear image is viewed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 192, "label": "text", "bbox": {"l": 358.422, "t": 268.71299999999997, "r": 385.698, "b": 269.98400000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.422, "r_y0": 269.98400000000004, "r_x1": 385.698, "r_y1": 269.98400000000004, "r_x2": 385.698, "r_y2": 268.71299999999997, "r_x3": 358.422, "r_y3": 268.71299999999997, "coord_origin": "TOPLEFT"}, "text": "2. If the image is unclear, adjust the ", "orig": "2. If the image is unclear, adjust the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 193, "label": "text", "bbox": {"l": 358.423, "t": 270.32399999999996, "r": 385.612, "b": 271.59400000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 271.59400000000005, "r_x1": 385.612, "r_y1": 271.59400000000005, "r_x2": 385.612, "r_y2": 270.32399999999996, "r_x3": 358.423, "r_y3": 270.32399999999996, "coord_origin": "TOPLEFT"}, "text": " height of the elevator up or down, ", "orig": " height of the elevator up or down, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 194, "label": "text", "bbox": {"l": 358.423, "t": 271.93399999999997, "r": 385.389, "b": 273.20399999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 273.20399999999995, "r_x1": 385.389, "r_y1": 273.20399999999995, "r_x2": 385.389, "r_y2": 271.93399999999997, "r_x3": 358.423, "r_y3": 271.93399999999997, "coord_origin": "TOPLEFT"}, "text": " then turn the focusing knob again.", "orig": " then turn the focusing knob again.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 195, "label": "text", "bbox": {"l": 358.423, "t": 275.154, "r": 377.35, "b": 276.424, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 276.424, "r_x1": 377.35, "r_y1": 276.424, "r_x2": 377.35, "r_y2": 275.154, "r_x3": 358.423, "r_y3": 275.154, "coord_origin": "TOPLEFT"}, "text": "ZOOM MAGNIFICATION", "orig": "ZOOM MAGNIFICATION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 196, "label": "text", "bbox": {"l": 358.423, "t": 276.765, "r": 387.359, "b": 278.03499999999997, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 278.03499999999997, "r_x1": 387.359, "r_y1": 278.03499999999997, "r_x2": 387.359, "r_y2": 276.765, "r_x3": 358.423, "r_y3": 276.765, "coord_origin": "TOPLEFT"}, "text": "1. Turn the zoom magnification knob to ", "orig": "1. Turn the zoom magnification knob to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 197, "label": "text", "bbox": {"l": 358.423, "t": 278.375, "r": 387.748, "b": 279.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 279.645, "r_x1": 387.748, "r_y1": 279.645, "r_x2": 387.748, "r_y2": 278.375, "r_x3": 358.423, "r_y3": 278.375, "coord_origin": "TOPLEFT"}, "text": " the desired magnification and field of ", "orig": " the desired magnification and field of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 198, "label": "text", "bbox": {"l": 358.423, "t": 279.985, "r": 364.169, "b": 281.255, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 281.255, "r_x1": 364.169, "r_y1": 281.255, "r_x2": 364.169, "r_y2": 279.985, "r_x3": 358.423, "r_y3": 279.985, "coord_origin": "TOPLEFT"}, "text": " view.", "orig": " view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 199, "label": "text", "bbox": {"l": 358.423, "t": 281.595, "r": 388.266, "b": 282.865, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 282.865, "r_x1": 388.266, "r_y1": 282.865, "r_x2": 388.266, "r_y2": 281.595, "r_x3": 358.423, "r_y3": 281.595, "coord_origin": "TOPLEFT"}, "text": "2. In most situations, it is recommended ", "orig": "2. In most situations, it is recommended ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 200, "label": "text", "bbox": {"l": 358.423, "t": 283.205, "r": 381.567, "b": 284.476, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 284.476, "r_x1": 381.567, "r_y1": 284.476, "r_x2": 381.567, "r_y2": 283.205, "r_x3": 358.423, "r_y3": 283.205, "coord_origin": "TOPLEFT"}, "text": " that you focus at the lowest ", "orig": " that you focus at the lowest ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 201, "label": "text", "bbox": {"l": 358.423, "t": 284.816, "r": 387.673, "b": 286.086, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.423, "r_y0": 286.086, "r_x1": 387.673, "r_y1": 286.086, "r_x2": 387.673, "r_y2": 284.816, "r_x3": 358.423, "r_y3": 284.816, "coord_origin": "TOPLEFT"}, "text": " magnification, then move to a higher ", "orig": " magnification, then move to a higher ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 202, "label": "text", "bbox": {"l": 358.425, "t": 286.426, "r": 382.771, "b": 287.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 287.696, "r_x1": 382.771, "r_y1": 287.696, "r_x2": 382.771, "r_y2": 286.426, "r_x3": 358.425, "r_y3": 286.426, "coord_origin": "TOPLEFT"}, "text": " magnification and re-focus as ", "orig": " magnification and re-focus as ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 203, "label": "text", "bbox": {"l": 358.425, "t": 288.036, "r": 367.987, "b": 289.306, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 289.306, "r_x1": 367.987, "r_y1": 289.306, "r_x2": 367.987, "r_y2": 288.036, "r_x3": 358.425, "r_y3": 288.036, "coord_origin": "TOPLEFT"}, "text": " necessary.", "orig": " necessary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 204, "label": "text", "bbox": {"l": 358.425, "t": 289.646, "r": 387.601, "b": 290.916, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 290.916, "r_x1": 387.601, "r_y1": 290.916, "r_x2": 387.601, "r_y2": 289.646, "r_x3": 358.425, "r_y3": 289.646, "coord_origin": "TOPLEFT"}, "text": "3. If the image is not clear to both eyes ", "orig": "3. If the image is not clear to both eyes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 205, "label": "text", "bbox": {"l": 358.425, "t": 291.256, "r": 389.075, "b": 292.527, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 292.527, "r_x1": 389.075, "r_y1": 292.527, "r_x2": 389.075, "r_y2": 291.256, "r_x3": 358.425, "r_y3": 291.256, "coord_origin": "TOPLEFT"}, "text": " at the same time, the diopter ring may ", "orig": " at the same time, the diopter ring may ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 206, "label": "text", "bbox": {"l": 358.425, "t": 292.867, "r": 373.137, "b": 294.137, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 294.137, "r_x1": 373.137, "r_y1": 294.137, "r_x2": 373.137, "r_y2": 292.867, "r_x3": 358.425, "r_y3": 292.867, "coord_origin": "TOPLEFT"}, "text": " need adjustment.", "orig": " need adjustment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 207, "label": "text", "bbox": {"l": 358.425, "t": 296.087, "r": 381.745, "b": 297.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 297.357, "r_x1": 381.745, "r_y1": 297.357, "r_x2": 381.745, "r_y2": 296.087, "r_x3": 358.425, "r_y3": 296.087, "coord_origin": "TOPLEFT"}, "text": "DIOPTER RING ADJUSTMENT", "orig": "DIOPTER RING ADJUSTMENT", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 208, "label": "text", "bbox": {"l": 358.425, "t": 297.697, "r": 389.01, "b": 298.967, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 298.967, "r_x1": 389.01, "r_y1": 298.967, "r_x2": 389.01, "r_y2": 297.697, "r_x3": 358.425, "r_y3": 297.697, "coord_origin": "TOPLEFT"}, "text": "1. To adjust the eyepiece for viewing with ", "orig": "1. To adjust the eyepiece for viewing with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 209, "label": "text", "bbox": {"l": 358.425, "t": 299.308, "r": 382.733, "b": 300.578, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 300.578, "r_x1": 382.733, "r_y1": 300.578, "r_x2": 382.733, "r_y2": 299.308, "r_x3": 358.425, "r_y3": 299.308, "coord_origin": "TOPLEFT"}, "text": " or without eyeglasses and for ", "orig": " or without eyeglasses and for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 210, "label": "text", "bbox": {"l": 358.425, "t": 300.918, "r": 388.701, "b": 302.188, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 302.188, "r_x1": 388.701, "r_y1": 302.188, "r_x2": 388.701, "r_y2": 300.918, "r_x3": 358.425, "r_y3": 300.918, "coord_origin": "TOPLEFT"}, "text": " differences in acuity between the right ", "orig": " differences in acuity between the right ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 211, "label": "text", "bbox": {"l": 358.425, "t": 302.528, "r": 385.147, "b": 303.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 303.798, "r_x1": 385.147, "r_y1": 303.798, "r_x2": 385.147, "r_y2": 302.528, "r_x3": 358.425, "r_y3": 302.528, "coord_origin": "TOPLEFT"}, "text": " and left eyes, fol ow the fol owing ", "orig": " and left eyes, fol ow the fol owing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 212, "label": "text", "bbox": {"l": 358.425, "t": 304.138, "r": 364.887, "b": 305.408, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 305.408, "r_x1": 364.887, "r_y1": 305.408, "r_x2": 364.887, "r_y2": 304.138, "r_x3": 358.425, "r_y3": 304.138, "coord_origin": "TOPLEFT"}, "text": " steps:", "orig": " steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 213, "label": "text", "bbox": {"l": 358.425, "t": 305.748, "r": 387.708, "b": 307.019, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 307.019, "r_x1": 387.708, "r_y1": 307.019, "r_x2": 387.708, "r_y2": 305.748, "r_x3": 358.425, "r_y3": 305.748, "coord_origin": "TOPLEFT"}, "text": " a. Observe an image through the left ", "orig": " a. Observe an image through the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 214, "label": "text", "bbox": {"l": 358.425, "t": 307.359, "r": 387.857, "b": 308.629, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 308.629, "r_x1": 387.857, "r_y1": 308.629, "r_x2": 387.857, "r_y2": 307.359, "r_x3": 358.425, "r_y3": 307.359, "coord_origin": "TOPLEFT"}, "text": " eyepiece and bring a specific point ", "orig": " eyepiece and bring a specific point ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 215, "label": "text", "bbox": {"l": 358.425, "t": 308.969, "r": 385.414, "b": 310.239, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 310.239, "r_x1": 385.414, "r_y1": 310.239, "r_x2": 385.414, "r_y2": 308.969, "r_x3": 358.425, "r_y3": 308.969, "coord_origin": "TOPLEFT"}, "text": " into focus using the focus knob.", "orig": " into focus using the focus knob.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 216, "label": "text", "bbox": {"l": 358.425, "t": 310.579, "r": 382.561, "b": 311.849, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.425, "r_y0": 311.849, "r_x1": 382.561, "r_y1": 311.849, "r_x2": 382.561, "r_y2": 310.579, "r_x3": 358.425, "r_y3": 310.579, "coord_origin": "TOPLEFT"}, "text": " b. By turning the diopter ring ", "orig": " b. By turning the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 217, "label": "text", "bbox": {"l": 358.426, "t": 312.189, "r": 386.568, "b": 313.459, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 313.459, "r_x1": 386.568, "r_y1": 313.459, "r_x2": 386.568, "r_y2": 312.189, "r_x3": 358.426, "r_y3": 312.189, "coord_origin": "TOPLEFT"}, "text": " adjustment for the left eyepiece, ", "orig": " adjustment for the left eyepiece, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 218, "label": "text", "bbox": {"l": 358.426, "t": 313.799, "r": 385.703, "b": 315.07, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 315.07, "r_x1": 385.703, "r_y1": 315.07, "r_x2": 385.703, "r_y2": 313.799, "r_x3": 358.426, "r_y3": 313.799, "coord_origin": "TOPLEFT"}, "text": " bring the same point into sharp ", "orig": " bring the same point into sharp ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 219, "label": "text", "bbox": {"l": 358.426, "t": 315.41, "r": 366.744, "b": 316.68, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 316.68, "r_x1": 366.744, "r_y1": 316.68, "r_x2": 366.744, "r_y2": 315.41, "r_x3": 358.426, "r_y3": 315.41, "coord_origin": "TOPLEFT"}, "text": " focus.", "orig": " focus.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 220, "label": "text", "bbox": {"l": 358.426, "t": 317.02, "r": 384.99, "b": 318.29, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 318.29, "r_x1": 384.99, "r_y1": 318.29, "r_x2": 384.99, "r_y2": 317.02, "r_x3": 358.426, "r_y3": 317.02, "coord_origin": "TOPLEFT"}, "text": " c.Then bring the same point into ", "orig": " c.Then bring the same point into ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 221, "label": "text", "bbox": {"l": 358.426, "t": 318.63, "r": 386.867, "b": 319.9, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 319.9, "r_x1": 386.867, "r_y1": 319.9, "r_x2": 386.867, "r_y2": 318.63, "r_x3": 358.426, "r_y3": 318.63, "coord_origin": "TOPLEFT"}, "text": " focus through the right eyepiece ", "orig": " focus through the right eyepiece ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 222, "label": "text", "bbox": {"l": 358.426, "t": 320.24, "r": 385.949, "b": 321.51, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 321.51, "r_x1": 385.949, "r_y1": 321.51, "r_x2": 385.949, "r_y2": 320.24, "r_x3": 358.426, "r_y3": 320.24, "coord_origin": "TOPLEFT"}, "text": " by turning the right diopter ring.", "orig": " by turning the right diopter ring.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 223, "label": "text", "bbox": {"l": 358.426, "t": 321.851, "r": 387.137, "b": 323.121, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 323.121, "r_x1": 387.137, "r_y1": 323.121, "r_x2": 387.137, "r_y2": 321.851, "r_x3": 358.426, "r_y3": 321.851, "coord_origin": "TOPLEFT"}, "text": " d.With more than one viewer, each ", "orig": " d.With more than one viewer, each ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 224, "label": "text", "bbox": {"l": 358.426, "t": 323.461, "r": 384.369, "b": 324.731, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 324.731, "r_x1": 384.369, "r_y1": 324.731, "r_x2": 384.369, "r_y2": 323.461, "r_x3": 358.426, "r_y3": 323.461, "coord_origin": "TOPLEFT"}, "text": " viewer should note their own ", "orig": " viewer should note their own ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 225, "label": "text", "bbox": {"l": 358.426, "t": 325.071, "r": 386.093, "b": 326.341, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 326.341, "r_x1": 386.093, "r_y1": 326.341, "r_x2": 386.093, "r_y2": 325.071, "r_x3": 358.426, "r_y3": 325.071, "coord_origin": "TOPLEFT"}, "text": " diopter ring position for the left ", "orig": " diopter ring position for the left ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 226, "label": "text", "bbox": {"l": 358.426, "t": 326.681, "r": 386.352, "b": 327.951, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 327.951, "r_x1": 386.352, "r_y1": 327.951, "r_x2": 386.352, "r_y2": 326.681, "r_x3": 358.426, "r_y3": 326.681, "coord_origin": "TOPLEFT"}, "text": " and right eyepieces, then before ", "orig": " and right eyepieces, then before ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 227, "label": "text", "bbox": {"l": 358.426, "t": 328.291, "r": 383.28, "b": 329.561, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 329.561, "r_x1": 383.28, "r_y1": 329.561, "r_x2": 383.28, "r_y2": 328.291, "r_x3": 358.426, "r_y3": 328.291, "coord_origin": "TOPLEFT"}, "text": " viewing set the diopter ring ", "orig": " viewing set the diopter ring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 228, "label": "text", "bbox": {"l": 358.426, "t": 329.902, "r": 382.634, "b": 331.172, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 331.172, "r_x1": 382.634, "r_y1": 331.172, "r_x2": 382.634, "r_y2": 329.902, "r_x3": 358.426, "r_y3": 329.902, "coord_origin": "TOPLEFT"}, "text": " adjustments to that setting.", "orig": " adjustments to that setting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 229, "label": "text", "bbox": {"l": 358.426, "t": 333.122, "r": 375.677, "b": 334.392, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 334.392, "r_x1": 375.677, "r_y1": 334.392, "r_x2": 375.677, "r_y2": 333.122, "r_x3": 358.426, "r_y3": 333.122, "coord_origin": "TOPLEFT"}, "text": "CHANGING THE BULB", "orig": "CHANGING THE BULB", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 230, "label": "text", "bbox": {"l": 358.426, "t": 334.732, "r": 386.737, "b": 336.002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 336.002, "r_x1": 386.737, "r_y1": 336.002, "r_x2": 386.737, "r_y2": 334.732, "r_x3": 358.426, "r_y3": 334.732, "coord_origin": "TOPLEFT"}, "text": "1. Disconnect the power cord from the ", "orig": "1. Disconnect the power cord from the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 231, "label": "text", "bbox": {"l": 358.426, "t": 336.342, "r": 372.014, "b": 337.613, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 337.613, "r_x1": 372.014, "r_y1": 337.613, "r_x2": 372.014, "r_y2": 336.342, "r_x3": 358.426, "r_y3": 336.342, "coord_origin": "TOPLEFT"}, "text": " electrical outlet.", "orig": " electrical outlet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 232, "label": "text", "bbox": {"l": 358.426, "t": 337.953, "r": 385.634, "b": 339.223, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 339.223, "r_x1": 385.634, "r_y1": 339.223, "r_x2": 385.634, "r_y2": 337.953, "r_x3": 358.426, "r_y3": 337.953, "coord_origin": "TOPLEFT"}, "text": "2. When the bulb is cool, remove the ", "orig": "2. When the bulb is cool, remove the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 233, "label": "text", "bbox": {"l": 358.426, "t": 339.563, "r": 386.395, "b": 340.833, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 340.833, "r_x1": 386.395, "r_y1": 340.833, "r_x2": 386.395, "r_y2": 339.563, "r_x3": 358.426, "r_y3": 339.563, "coord_origin": "TOPLEFT"}, "text": " oblique il uminator cap and remove ", "orig": " oblique il uminator cap and remove ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 234, "label": "text", "bbox": {"l": 358.426, "t": 341.173, "r": 379.572, "b": 342.443, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.426, "r_y0": 342.443, "r_x1": 379.572, "r_y1": 342.443, "r_x2": 379.572, "r_y2": 341.173, "r_x3": 358.426, "r_y3": 341.173, "coord_origin": "TOPLEFT"}, "text": " the halogen bulb with cap.", "orig": " the halogen bulb with cap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 235, "label": "text", "bbox": {"l": 358.427, "t": 342.783, "r": 384.511, "b": 344.053, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.427, "r_y0": 344.053, "r_x1": 384.511, "r_y1": 344.053, "r_x2": 384.511, "r_y2": 342.783, "r_x3": 358.427, "r_y3": 342.783, "coord_origin": "TOPLEFT"}, "text": "3. 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emphasis on runway pattern and related ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 296, "label": "text", "bbox": {"l": 449.101, "t": 427.727, "r": 522.034, "b": 429.405, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 271, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 429.405, "r_x1": 522.034, "r_y1": 429.405, "r_x2": 522.034, "r_y2": 427.727, "r_x3": 449.101, "r_y3": 427.727, "coord_origin": "TOPLEFT"}, "text": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "orig": "information, positioned in either the lower left or lower right corner of the chart to aid pi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 297, "label": "text", "bbox": {"l": 449.101, "t": 430.005, "r": 524.672, "b": 431.683, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 272, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 431.683, "r_x1": 524.672, "r_y1": 431.683, "r_x2": 524.672, "r_y2": 430.005, "r_x3": 449.101, "r_y3": 430.005, "coord_origin": "TOPLEFT"}, "text": "lot recognition of the airport from the air and to provide some information to aid on ground ", "orig": "lot recognition of the airport from the air and to provide some information to aid on ground ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 298, "label": "text", "bbox": {"l": 449.101, "t": 432.284, "r": 527.172, "b": 433.962, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 273, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 433.962, "r_x1": 527.172, "r_y1": 433.962, "r_x2": 527.172, "r_y2": 432.284, "r_x3": 449.101, "r_y3": 432.284, "coord_origin": "TOPLEFT"}, "text": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "orig": "navigation of the airport. The runways are drawn to scale and oriented to true north. Runway ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 299, "label": "text", "bbox": {"l": 449.101, "t": 434.563, "r": 502.395, "b": 436.241, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 274, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 436.241, "r_x1": 502.395, "r_y1": 436.241, "r_x2": 502.395, "r_y2": 434.563, "r_x3": 449.101, "r_y3": 434.563, "coord_origin": "TOPLEFT"}, "text": "dimensions (length and width) are shown for al active runways.", "orig": "dimensions (length and width) are shown for al active runways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 300, "label": "text", "bbox": {"l": 449.101, "t": 439.12, "r": 512.927, "b": 440.798, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 275, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 440.798, "r_x1": 512.927, "r_y1": 440.798, "r_x2": 512.927, "r_y2": 439.12, "r_x3": 449.101, "r_y3": 439.12, "coord_origin": "TOPLEFT"}, "text": "Runway(s) are depicted based on what type and construction of the runway.", "orig": "Runway(s) are depicted based on what type and construction of the runway.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 301, "label": "text", "bbox": {"l": 449.955, "t": 444.393, "r": 460.023, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 276, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 445.903, "r_x1": 460.023, "r_y1": 445.903, "r_x2": 460.023, "r_y2": 444.393, "r_x3": 449.955, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 302, "label": "text", "bbox": {"l": 464.9, "t": 444.393, "r": 473.988, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 277, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 445.903, "r_x1": 473.988, "r_y1": 445.903, "r_x2": 473.988, "r_y2": 444.393, "r_x3": 464.9, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Other Than ", "orig": "Other Than ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 303, "label": "text", "bbox": {"l": 464.9, "t": 446.444, "r": 474.967, "b": 447.954, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 278, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 447.954, "r_x1": 474.967, "r_y1": 447.954, "r_x2": 474.967, "r_y2": 446.444, "r_x3": 464.9, "r_y3": 446.444, "coord_origin": "TOPLEFT"}, "text": "Hard Surface", "orig": "Hard Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 304, "label": "text", "bbox": {"l": 478.914, "t": 444.393, "r": 489.457, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 279, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 445.903, "r_x1": 489.457, "r_y1": 445.903, "r_x2": 489.457, "r_y2": 444.393, "r_x3": 478.914, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Metal Surface", "orig": "Metal Surface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 305, "label": "text", "bbox": {"l": 493.064, "t": 444.393, "r": 505.031, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 280, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 445.903, "r_x1": 505.031, "r_y1": 445.903, "r_x2": 505.031, "r_y2": 444.393, "r_x3": 493.064, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Closed Runway", "orig": "Closed Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 306, "label": "text", "bbox": {"l": 509.581, "t": 444.393, "r": 524.302, "b": 445.903, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 281, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 509.581, "r_y0": 445.903, "r_x1": 524.302, "r_y1": 445.903, "r_x2": 524.302, "r_y2": 444.393, "r_x3": 509.581, "r_y3": 444.393, "coord_origin": "TOPLEFT"}, "text": "Under Construction", "orig": "Under Construction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 307, "label": "text", "bbox": {"l": 449.955, "t": 455.135, "r": 458.314, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 282, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 456.645, "r_x1": 458.314, "r_y1": 456.645, "r_x2": 458.314, "r_y2": 455.135, "r_x3": 449.955, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Stopways, ", "orig": "Stopways, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 308, "label": "text", "bbox": {"l": 449.955, "t": 457.186, "r": 461.921, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 283, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 458.696, "r_x1": 461.921, "r_y1": 458.696, "r_x2": 461.921, "r_y2": 457.186, "r_x3": 449.955, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Taxiways, Park-", "orig": "Taxiways, Park-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 309, "label": "text", "bbox": {"l": 449.955, "t": 459.237, "r": 457.08, "b": 460.747, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 284, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.955, "r_y0": 460.747, "r_x1": 457.08, "r_y1": 460.747, "r_x2": 457.08, "r_y2": 459.237, "r_x3": 449.955, "r_y3": 459.237, "coord_origin": "TOPLEFT"}, "text": "ing Areas", "orig": "ing Areas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 310, "label": "text", "bbox": {"l": 464.9, "t": 455.135, "r": 472.877, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 285, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 456.645, "r_x1": 472.877, "r_y1": 456.645, "r_x2": 472.877, "r_y2": 455.135, "r_x3": 464.9, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Displaced ", "orig": "Displaced ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 311, "label": "text", "bbox": {"l": 464.9, "t": 457.186, "r": 472.498, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 286, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 464.9, "r_y0": 458.696, "r_x1": 472.498, "r_y1": 458.696, "r_x2": 472.498, "r_y2": 457.186, "r_x3": 464.9, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Threshold", "orig": "Threshold", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 312, "label": "text", "bbox": {"l": 478.914, "t": 455.135, "r": 484.707, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 287, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 456.645, "r_x1": 484.707, "r_y1": 456.645, "r_x2": 484.707, "r_y2": 455.135, "r_x3": 478.914, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Closed ", "orig": "Closed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 313, "label": "text", "bbox": {"l": 478.914, "t": 457.186, "r": 486.608, "b": 458.696, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 288, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 478.914, "r_y0": 458.696, "r_x1": 486.608, "r_y1": 458.696, "r_x2": 486.608, "r_y2": 457.186, "r_x3": 478.914, "r_y3": 457.186, "coord_origin": "TOPLEFT"}, "text": "Pavement", "orig": "Pavement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 314, "label": "text", "bbox": {"l": 493.064, "t": 455.135, "r": 504.207, "b": 456.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 289, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 493.064, "r_y0": 456.645, "r_x1": 504.207, "r_y1": 456.645, "r_x2": 504.207, "r_y2": 455.135, "r_x3": 493.064, "r_y3": 455.135, "coord_origin": "TOPLEFT"}, "text": "Water Runway", "orig": "Water Runway", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 315, "label": "text", "bbox": {"l": 449.101, "t": 469.689, "r": 548.597, "b": 471.367, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 290, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 471.367, "r_x1": 548.597, "r_y1": 471.367, "r_x2": 548.597, "r_y2": 469.689, "r_x3": 449.101, "r_y3": 469.689, "coord_origin": "TOPLEFT"}, "text": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "orig": "Taxiways and aprons are shaded grey. Other runway features that may be shown are runway numbers, runway dimen-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 316, "label": "text", "bbox": {"l": 449.101, "t": 471.967, "r": 500.082, "b": 473.645, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 291, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 473.645, "r_x1": 500.082, "r_y1": 473.645, "r_x2": 500.082, "r_y2": 471.967, "r_x3": 449.101, "r_y3": 471.967, "coord_origin": "TOPLEFT"}, "text": "sions, runway slope, arresting gear, and displaced threshold.", "orig": "sions, runway slope, arresting gear, and displaced threshold.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 317, "label": "text", "bbox": {"l": 449.101, "t": 476.525, "r": 547.826, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 292, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 478.203, "r_x1": 547.826, "r_y1": 478.203, "r_x2": 547.826, "r_y2": 476.525, "r_x3": 449.101, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "orig": "2WKHUglyphLQIRUPDWLRQglyphFRQFHUQLQJglyphOLJKWLQJglyphglyph\u00bfQDOglyphDSSURDFKglyphEHDULQJVglyphglyphDLUSRUWglyphEHDFRQglyphglyphREVWDFOHVglyphglyphFRQWUROglyphWRZHU glyphglyph1$ 9 $,'VglyphglyphKHOL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 318, "label": "text", "bbox": {"l": 547.826, "t": 476.525, "r": 548.459, "b": 478.203, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 293, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 547.826, "r_y0": 478.203, "r_x1": 548.459, "r_y1": 478.203, "r_x2": 548.459, "r_y2": 476.525, "r_x3": 547.826, "r_y3": 476.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 319, "label": "text", "bbox": {"l": 449.101, "t": 478.803, "r": 470.526, "b": 480.481, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 294, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 480.481, "r_x1": 470.526, "r_y1": 480.481, "r_x2": 470.526, "r_y2": 478.803, "r_x3": 449.101, "r_y3": 478.803, "coord_origin": "TOPLEFT"}, "text": "pads may also be shown.", "orig": "pads may also be shown.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 320, "label": "text", "bbox": {"l": 449.101, "t": 483.361, "r": 493.379, "b": 485.039, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 295, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 485.039, "r_x1": 493.379, "r_y1": 485.039, "r_x2": 493.379, "r_y2": 483.361, "r_x3": 449.101, "r_y3": 483.361, "coord_origin": "TOPLEFT"}, "text": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "orig": "$LUSRUWglyph(OHYDWLRQglyphDQGglyph7 RXFKGRZQglyph=RQHglyph(OHYDWLRQ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 449.101, "t": 487.918, "r": 551.8, "b": 496.432, "coord_origin": "TOPLEFT"}, "confidence": 0.5027860403060913, "cells": [{"index": 296, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 489.596, "r_x1": 549.162, "r_y1": 489.596, "r_x2": 549.162, "r_y2": 487.918, "r_x3": 449.101, "r_y3": 487.918, "coord_origin": "TOPLEFT"}, "text": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "orig": "The airport elevation is shown enclosed within a box in the upper left corner of the sketch box and the touchdown zone ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 297, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.101, "r_y0": 491.875, "r_x1": 546.909, "r_y1": 491.875, "r_x2": 546.909, "r_y2": 490.197, "r_x3": 449.101, "r_y3": 490.197, "coord_origin": "TOPLEFT"}, "text": "elevation (TDZE) is shown in the upper right corner of the sketch box. The airport elevation is the highest point of an ", "orig": "elevation (TDZE) is shown in the upper right corner of the sketch box. 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Circling only approaches wil not show a TDZE.", "orig": "the landing surface. 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", "orig": "electricity needs of Australian households. 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Within two years, ", "orig": "by mechanical ingenuity.' 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This chapter is a handy ", "orig": "some of the controls in your vehicle. This chapter is a handy ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 364, "label": "text", "bbox": {"l": 498.153, "t": 268.149, "r": 544.013, "b": 269.95899999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 347, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 269.95899999999995, "r_x1": 544.013, "r_y1": 269.95899999999995, "r_x2": 544.013, "r_y2": 268.149, "r_x3": 498.153, "r_y3": 268.149, "coord_origin": "TOPLEFT"}, "text": "reference section that gives examples of the most common ", "orig": "reference section that gives examples of the most common ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 365, "label": "text", "bbox": {"l": 498.153, "t": 270.192, "r": 544.12, "b": 272.00199999999995, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 348, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 272.00199999999995, "r_x1": 544.12, "r_y1": 272.00199999999995, "r_x2": 544.12, "r_y2": 270.192, "r_x3": 498.153, "r_y3": 270.192, "coord_origin": "TOPLEFT"}, "text": "signs, signals and road markings that keep traffic organized ", "orig": "signs, signals and road markings that keep traffic organized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 366, "label": "text", "bbox": {"l": 498.153, "t": 272.235, "r": 515.411, "b": 274.04499999999996, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 349, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 274.04499999999996, "r_x1": 515.411, "r_y1": 274.04499999999996, "r_x2": 515.411, "r_y2": 272.235, "r_x3": 498.153, "r_y3": 272.235, "coord_origin": "TOPLEFT"}, "text": "and flowing smoothly. ", "orig": "and flowing smoothly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 367, "label": "text", "bbox": {"l": 498.153, "t": 277.663, "r": 505.646, "b": 280.851, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 350, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 280.851, "r_x1": 505.646, "r_y1": 280.851, "r_x2": 505.646, "r_y2": 277.663, "r_x3": 498.153, "r_y3": 277.663, "coord_origin": "TOPLEFT"}, "text": "Signs", "orig": "Signs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 368, "label": "text", "bbox": {"l": 498.153, "t": 281.996, "r": 543.93, "b": 283.805, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 351, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 283.805, "r_x1": 543.93, "r_y1": 283.805, "r_x2": 543.93, "r_y2": 281.996, "r_x3": 498.153, "r_y3": 281.996, "coord_origin": "TOPLEFT"}, "text": "There are three ways to read signs: by their shape, colour and ", "orig": "There are three ways to read signs: by their shape, colour and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 369, "label": "text", "bbox": {"l": 498.153, "t": 284.039, "r": 545.678, "b": 285.848, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 352, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 498.153, "r_y0": 285.848, "r_x1": 545.678, "r_y1": 285.848, "r_x2": 545.678, "r_y2": 284.039, "r_x3": 498.153, "r_y3": 284.039, "coord_origin": "TOPLEFT"}, "text": "the messages printed on them. 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", "orig": "that are new to you. 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ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043"}], "headers": [{"label": "page_header", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 18.34, "t": 165.0, "r": 36.34, "b": 555.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8537883758544922, "cells": [{"index": 460, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "orig": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "arXiv:2206.01062v1 [cs.CV] 2 Jun 2022"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. 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The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 625.734, "r_x1": 167.917, "r_y1": 625.734, "r_x2": 167.917, "r_y2": 617.826, "r_x3": 78.207, "r_y3": 617.826, "coord_origin": "TOPLEFT"}, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.335, "r_y0": 625.69, "r_x1": 170.493, "r_y1": 625.69, "r_x2": 170.493, "r_y2": 617.889, "r_x3": 168.335, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.981, "r_y0": 625.69, "r_x1": 294.263, "r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 647.6510000000001, "r_x1": 143.517, "r_y1": 647.6510000000001, "r_x2": 143.517, "r_y2": 639.7429999999999, "r_x3": 78.207, "r_y3": 639.7429999999999, "coord_origin": "TOPLEFT"}, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.02, "r_y0": 647.607, "r_x1": 146.178, "r_y1": 647.607, "r_x2": 146.178, "r_y2": 639.806, "r_x3": 144.02, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 117.55700000000002, "r_x1": 487.253, "r_y1": 117.55700000000002, "r_x2": 487.253, "r_y2": 109.649, "r_x3": 342.364, "r_y3": 109.649, "coord_origin": "TOPLEFT"}, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.759, "r_y0": 117.51300000000003, "r_x1": 489.867, "r_y1": 117.51300000000003, "r_x2": 489.867, "r_y2": 109.71199999999999, "r_x3": 487.759, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 542.158, "r_y0": 642.554, "r_x1": 559.099, "r_y1": 642.554, "r_x2": 559.099, "r_y2": 634.646, "r_x3": 542.158, "r_y3": 634.646, "coord_origin": "TOPLEFT"}, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 653.513, "r_x1": 331.863, "r_y1": 653.513, "r_x2": 331.863, "r_y2": 645.605, "r_x3": 317.955, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.863, "r_y0": 653.469, "r_x1": 333.84, "r_y1": 653.469, "r_x2": 333.84, "r_y2": 645.668, "r_x3": 331.863, 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370.254, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "Formula List-item", "orig": "Formula List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.055, "r_y0": 653.469, "r_x1": 402.032, "r_y1": 653.469, "r_x2": 402.032, "r_y2": 645.668, "r_x3": 400.055, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.195, "r_y0": 653.469, "r_x1": 438.172, "r_y1": 653.469, "r_x2": 438.172, "r_y2": 645.668, "r_x3": 436.195, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.396, "r_y0": 653.513, "r_x1": 480.61, "r_y1": 653.513, "r_x2": 480.61, 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{"r_x0": 449.62, "r_y0": 664.428, "r_x1": 558.202, "r_y1": 664.428, "r_x2": 558.202, "r_y2": 656.627, "r_x3": 449.62, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": "Our reasoning for picking this", "orig": "Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.387, "r_x1": 472.222, "r_y1": 675.387, "r_x2": 472.222, "r_y2": 667.586, "r_x3": 317.955, "r_y3": 667.586, "coord_origin": "TOPLEFT"}, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.3050000000001, "r_x1": 558.199, "r_y1": 697.3050000000001, "r_x2": 558.199, "r_y2": 689.504, "r_x3": 317.955, "r_y3": 689.504, "coord_origin": "TOPLEFT"}, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.264, "r_x1": 558.204, "r_y1": 708.264, "r_x2": 558.204, "r_y2": 700.463, "r_x3": 317.955, "r_y3": 700.463, "coord_origin": "TOPLEFT"}, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 156.529, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 95.86500000000001, "r_x1": 156.529, "r_y1": 95.86500000000001, "r_x2": 156.529, "r_y2": 86.798, "r_x3": 70.314, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.529, "t": 111.41399999999999, "r": 303.017, "b": 228.80399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 130.17399999999998, "r_x1": 295.557, "r_y1": 130.17399999999998, "r_x2": 295.557, "r_y2": 122.37300000000005, "r_x3": 53.529, "r_y3": 122.37300000000005, "coord_origin": "TOPLEFT"}, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 141.13300000000004, "r_x1": 294.046, "r_y1": 141.13300000000004, "r_x2": 294.046, "r_y2": 133.332, "r_x3": 53.798, "r_y3": 133.332, "coord_origin": "TOPLEFT"}, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 152.091, "r_x1": 294.047, "r_y1": 152.091, "r_x2": 294.047, "r_y2": 144.28999999999996, "r_x3": 53.798, "r_y3": 144.28999999999996, "coord_origin": "TOPLEFT"}, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 53.529, "t": 231.962, "r": 295.564, "b": 502.776, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.592, "t": 505.934, "r": 295.564, "b": 579.489, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.9490000000001, "r": 295.562, "b": 614.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.826, "r": 294.263, "b": 636.648, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 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"r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.7429999999999, "r": 294.684, "b": 669.525, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 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0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.62, "r": 295.564, "b": 691.443, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 216.027, "b": 708.629, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 106.55399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.649, "r": 559.721, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, 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":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.623, "t": 168.40599999999995, "r": 559.19, "b": 220.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.731, "t": 223.20100000000002, "r": 559.582, "b": 307.714, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 421.744, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.716, "b": 464.153, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 467.311, "r": 559.186, "b": 551.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 477.457, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 559.713, "b": 675.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. 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Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 156.529, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 95.86500000000001, "r_x1": 156.529, "r_y1": 95.86500000000001, "r_x2": 156.529, "r_y2": 86.798, "r_x3": 70.314, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.529, "t": 111.41399999999999, "r": 303.017, "b": 228.80399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 130.17399999999998, "r_x1": 295.557, "r_y1": 130.17399999999998, "r_x2": 295.557, "r_y2": 122.37300000000005, "r_x3": 53.529, "r_y3": 122.37300000000005, "coord_origin": "TOPLEFT"}, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 141.13300000000004, "r_x1": 294.046, "r_y1": 141.13300000000004, "r_x2": 294.046, "r_y2": 133.332, "r_x3": 53.798, "r_y3": 133.332, "coord_origin": "TOPLEFT"}, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 152.091, "r_x1": 294.047, "r_y1": 152.091, "r_x2": 294.047, "r_y2": 144.28999999999996, "r_x3": 53.798, "r_y3": 144.28999999999996, "coord_origin": "TOPLEFT"}, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.529, "t": 231.962, "r": 295.564, "b": 502.776, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.592, "t": 505.934, "r": 295.564, "b": 579.489, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.9490000000001, "r": 295.562, "b": 614.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.826, "r": 294.263, "b": 636.648, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 625.734, "r_x1": 167.917, "r_y1": 625.734, "r_x2": 167.917, "r_y2": 617.826, "r_x3": 78.207, "r_y3": 617.826, "coord_origin": "TOPLEFT"}, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.335, "r_y0": 625.69, "r_x1": 170.493, "r_y1": 625.69, "r_x2": 170.493, "r_y2": 617.889, "r_x3": 168.335, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.981, "r_y0": 625.69, "r_x1": 294.263, "r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.7429999999999, "r": 294.684, "b": 669.525, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 647.6510000000001, "r_x1": 143.517, "r_y1": 647.6510000000001, "r_x2": 143.517, "r_y2": 639.7429999999999, "r_x3": 78.207, "r_y3": 639.7429999999999, "coord_origin": "TOPLEFT"}, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.02, "r_y0": 647.607, "r_x1": 146.178, "r_y1": 647.607, "r_x2": 146.178, "r_y2": 639.806, "r_x3": 144.02, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.62, "r": 295.564, "b": 691.443, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 216.027, "b": 708.629, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 106.55399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.649, "r": 559.721, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 117.55700000000002, "r_x1": 487.253, "r_y1": 117.55700000000002, "r_x2": 487.253, "r_y2": 109.649, "r_x3": 342.364, "r_y3": 109.649, "coord_origin": "TOPLEFT"}, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.759, "r_y0": 117.51300000000003, "r_x1": 489.867, "r_y1": 117.51300000000003, "r_x2": 489.867, "r_y2": 109.71199999999999, "r_x3": 487.759, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.623, "t": 168.40599999999995, "r": 559.19, "b": 220.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.731, "t": 223.20100000000002, "r": 559.582, "b": 307.714, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 421.744, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.716, "b": 464.153, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 467.311, "r": 559.186, "b": 551.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 477.457, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 559.713, "b": 675.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 542.158, "r_y0": 642.554, "r_x1": 559.099, "r_y1": 642.554, "r_x2": 559.099, "r_y2": 634.646, "r_x3": 542.158, "r_y3": 634.646, "coord_origin": "TOPLEFT"}, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 653.513, "r_x1": 331.863, "r_y1": 653.513, "r_x2": 331.863, "r_y2": 645.605, "r_x3": 317.955, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.863, "r_y0": 653.469, "r_x1": 333.84, "r_y1": 653.469, "r_x2": 333.84, "r_y2": 645.668, "r_x3": 331.863, 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414.343, "r_y0": 664.428, "r_x1": 427.568, "r_y1": 664.428, "r_x2": 427.568, "r_y2": 656.627, "r_x3": 414.343, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 429.744, "r_y0": 664.472, "r_x1": 445.508, "r_y1": 664.472, "r_x2": 445.508, "r_y2": 656.564, "r_x3": 429.744, "r_y3": 656.564, "coord_origin": "TOPLEFT"}, "text": "Title", "orig": "Title", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.508, "r_y0": 664.428, "r_x1": 447.441, "r_y1": 664.428, "r_x2": 447.441, "r_y2": 656.627, "r_x3": 445.508, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.62, "r_y0": 664.428, "r_x1": 558.202, "r_y1": 664.428, "r_x2": 558.202, "r_y2": 656.627, "r_x3": 449.62, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": "Our reasoning for picking this", "orig": "Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.387, "r_x1": 472.222, "r_y1": 675.387, "r_x2": 472.222, "r_y2": 667.586, "r_x3": 317.955, "r_y3": 667.586, "coord_origin": "TOPLEFT"}, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 678.545, "r": 558.204, "b": 708.264, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.3050000000001, "r_x1": 558.199, "r_y1": 697.3050000000001, "r_x2": 558.199, "r_y2": 689.504, "r_x3": 317.955, "r_y3": 689.504, "coord_origin": "TOPLEFT"}, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.264, "r_x1": 558.204, "r_y1": 708.264, "r_x2": 558.204, "r_y2": 700.463, "r_x3": 317.955, "r_y3": 700.463, "coord_origin": "TOPLEFT"}, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "body": [{"label": "section_header", "id": 14, "page_no": 1, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 53.798, "t": 86.798, "r": 156.529, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9367801547050476, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.86500000000001, "r_x1": 59.405, "r_y1": 95.86500000000001, "r_x2": 59.405, "r_y2": 86.798, "r_x3": 53.798, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 95.86500000000001, "r_x1": 156.529, "r_y1": 95.86500000000001, "r_x2": 156.529, "r_y2": 86.798, "r_x3": 70.314, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "INTRODUCTION", "orig": "INTRODUCTION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 INTRODUCTION"}, {"label": "text", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.529, "t": 111.41399999999999, "r": 303.017, "b": 228.80399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9802607893943787, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 119.21500000000003, "r_x1": 303.017, "r_y1": 119.21500000000003, "r_x2": 303.017, "r_y2": 111.41399999999999, "r_x3": 53.798, "r_y3": 111.41399999999999, "coord_origin": "TOPLEFT"}, "text": "Despite the substantial improvements achieved with machine-learning", "orig": "Despite the substantial improvements achieved with machine-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 130.17399999999998, "r_x1": 295.557, "r_y1": 130.17399999999998, "r_x2": 295.557, "r_y2": 122.37300000000005, "r_x3": 53.529, "r_y3": 122.37300000000005, "coord_origin": "TOPLEFT"}, "text": "(ML) approaches and deep neural networks in recent years, docu-", "orig": "(ML) approaches and deep neural networks in recent years, docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 141.13300000000004, "r_x1": 294.046, "r_y1": 141.13300000000004, "r_x2": 294.046, "r_y2": 133.332, "r_x3": 53.798, "r_y3": 133.332, "coord_origin": "TOPLEFT"}, "text": "ment conversion remains a challenging problem, as demonstrated", "orig": "ment conversion remains a challenging problem, as demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 152.091, "r_x1": 294.047, "r_y1": 152.091, "r_x2": 294.047, "r_y2": 144.28999999999996, "r_x3": 53.798, "r_y3": 144.28999999999996, "coord_origin": "TOPLEFT"}, "text": "by the numerous public competitions held on this topic [1-4]. The", "orig": "by the numerous public competitions held on this topic [1-4]. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 163.04999999999995, "r_x1": 294.043, "r_y1": 163.04999999999995, "r_x2": 294.043, "r_y2": 155.24900000000002, "r_x3": 53.798, "r_y3": 155.24900000000002, "coord_origin": "TOPLEFT"}, "text": "challenge originates from the huge variability in PDF documents", "orig": "challenge originates from the huge variability in PDF documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 174.00900000000001, "r_x1": 294.047, "r_y1": 174.00900000000001, "r_x2": 294.047, "r_y2": 166.20799999999997, "r_x3": 53.798, "r_y3": 166.20799999999997, "coord_origin": "TOPLEFT"}, "text": "regarding layout, language and formats (scanned, programmatic", "orig": "regarding layout, language and formats (scanned, programmatic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 184.96799999999996, "r_x1": 294.049, "r_y1": 184.96799999999996, "r_x2": 294.049, "r_y2": 177.16700000000003, "r_x3": 53.798, "r_y3": 177.16700000000003, "coord_origin": "TOPLEFT"}, "text": "or a combination of both). Engineering a single ML model that can", "orig": "or a combination of both). Engineering a single ML model that can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 195.92700000000002, "r_x1": 294.276, "r_y1": 195.92700000000002, "r_x2": 294.276, "r_y2": 188.12599999999998, "r_x3": 53.798, "r_y3": 188.12599999999998, "coord_origin": "TOPLEFT"}, "text": "be applied on all types of documents and provides high-quality", "orig": "be applied on all types of documents and provides high-quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 206.88599999999997, "r_x1": 295.426, "r_y1": 206.88599999999997, "r_x2": 295.426, "r_y2": 199.08500000000004, "r_x3": 53.798, "r_y3": 199.08500000000004, "coord_origin": "TOPLEFT"}, "text": "layout segmentation remains to this day extremely challenging [5].", "orig": "layout segmentation remains to this day extremely challenging [5].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 217.84500000000003, "r_x1": 294.373, "r_y1": 217.84500000000003, "r_x2": 294.373, "r_y2": 210.04399999999998, "r_x3": 53.529, "r_y3": 210.04399999999998, "coord_origin": "TOPLEFT"}, "text": "To highlight the variability in document layouts, we show a few", "orig": "To highlight the variability in document layouts, we show a few", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 228.80399999999997, "r_x1": 275.483, "r_y1": 228.80399999999997, "r_x2": 275.483, "r_y2": 221.00300000000004, "r_x3": 53.798, "r_y3": 221.00300000000004, "coord_origin": "TOPLEFT"}, "text": "example documents from the DocLayNet dataset in Figure 1.", "orig": "example documents from the DocLayNet dataset in Figure 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1."}, {"label": "text", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "text", "bbox": {"l": 53.529, "t": 231.962, "r": 295.564, "b": 502.776, "coord_origin": "TOPLEFT"}, "confidence": 0.9793015718460083, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 239.76300000000003, "r_x1": 295.564, "r_y1": 239.76300000000003, "r_x2": 295.564, "r_y2": 231.962, "r_x3": 63.761, "r_y3": 231.962, "coord_origin": "TOPLEFT"}, "text": "Akeyproblem in the process of document conversion is to under-", "orig": "Akeyproblem in the process of document conversion is to under-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 250.72199999999998, "r_x1": 294.049, "r_y1": 250.72199999999998, "r_x2": 294.049, "r_y2": 242.92100000000005, "r_x3": 53.798, "r_y3": 242.92100000000005, "coord_origin": "TOPLEFT"}, "text": "stand the structure of a single document page, i.e. which segments", "orig": "stand the structure of a single document page, i.e. which segments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 261.67999999999995, "r_x1": 294.045, "r_y1": 261.67999999999995, "r_x2": 294.045, "r_y2": 253.87900000000002, "r_x3": 53.798, "r_y3": 253.87900000000002, "coord_origin": "TOPLEFT"}, "text": "of text should be grouped together in a unit. To train models for this", "orig": "of text should be grouped together in a unit. To train models for this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 272.639, "r_x1": 295.556, "r_y1": 272.639, "r_x2": 295.556, "r_y2": 264.83799999999997, "r_x3": 53.798, "r_y3": 264.83799999999997, "coord_origin": "TOPLEFT"}, "text": "task, there are currently two large datasets available to the com-", "orig": "task, there are currently two large datasets available to the com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 283.598, "r_x1": 294.041, "r_y1": 283.598, "r_x2": 294.041, "r_y2": 275.797, "r_x3": 53.798, "r_y3": 275.797, "coord_origin": "TOPLEFT"}, "text": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "orig": "munity, PubLayNet [6] and DocBank [7]. They were introduced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 294.557, "r_x1": 295.558, "r_y1": 294.557, "r_x2": 295.558, "r_y2": 286.756, "r_x3": 53.798, "r_y3": 286.756, "coord_origin": "TOPLEFT"}, "text": "in 2019 and 2020 respectively and significantly accelerated the im-", "orig": "in 2019 and 2020 respectively and significantly accelerated the im-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 305.516, "r_x1": 294.045, "r_y1": 305.516, "r_x2": 294.045, "r_y2": 297.715, "r_x3": 53.798, "r_y3": 297.715, "coord_origin": "TOPLEFT"}, "text": "plementation of layout detection and segmentation models due to", "orig": "plementation of layout detection and segmentation models due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 316.475, "r_x1": 294.043, "r_y1": 316.475, "r_x2": 294.043, "r_y2": 308.674, "r_x3": 53.798, "r_y3": 308.674, "coord_origin": "TOPLEFT"}, "text": "their sizes of 300K and 500K ground-truth pages. These sizes were", "orig": "their sizes of 300K and 500K ground-truth pages. These sizes were", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 327.434, "r_x1": 295.564, "r_y1": 327.434, "r_x2": 295.564, "r_y2": 319.633, "r_x3": 53.798, "r_y3": 319.633, "coord_origin": "TOPLEFT"}, "text": "achieved by leveraging an automation approach. The benefit of au-", "orig": "achieved by leveraging an automation approach. The benefit of au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 338.393, "r_x1": 294.045, "r_y1": 338.393, "r_x2": 294.045, "r_y2": 330.592, "r_x3": 53.798, "r_y3": 330.592, "coord_origin": "TOPLEFT"}, "text": "tomated ground-truth generation is obvious: one can generate large", "orig": "tomated ground-truth generation is obvious: one can generate large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 349.352, "r_x1": 294.045, "r_y1": 349.352, "r_x2": 294.045, "r_y2": 341.551, "r_x3": 53.798, "r_y3": 341.551, "coord_origin": "TOPLEFT"}, "text": "ground-truth datasets at virtually no cost. However, the automation", "orig": "ground-truth datasets at virtually no cost. However, the automation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 360.311, "r_x1": 294.047, "r_y1": 360.311, "r_x2": 294.047, "r_y2": 352.51, "r_x3": 53.798, "r_y3": 352.51, "coord_origin": "TOPLEFT"}, "text": "introduces a constraint on the variability in the dataset, because", "orig": "introduces a constraint on the variability in the dataset, because", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 371.27, "r_x1": 294.045, "r_y1": 371.27, "r_x2": 294.045, "r_y2": 363.469, "r_x3": 53.798, "r_y3": 363.469, "coord_origin": "TOPLEFT"}, "text": "corresponding structured source data must be available. PubLayNet", "orig": "corresponding structured source data must be available. PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 382.228, "r_x1": 295.556, "r_y1": 382.228, "r_x2": 295.556, "r_y2": 374.427, "r_x3": 53.798, "r_y3": 374.427, "coord_origin": "TOPLEFT"}, "text": "and DocBank were both generated from scientific document repos-", "orig": "and DocBank were both generated from scientific document repos-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 393.187, "r_x1": 260.879, "r_y1": 393.187, "r_x2": 260.879, "r_y2": 385.386, "r_x3": 53.798, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "itories (PubMed and arXiv), which provide XML or L A T E X", "orig": "itories (PubMed and arXiv), which provide XML or L A T E X", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.101, "r_y0": 393.187, "r_x1": 295.428, "r_y1": 393.187, "r_x2": 295.428, "r_y2": 385.386, "r_x3": 266.101, "r_y3": 385.386, "coord_origin": "TOPLEFT"}, "text": "sources.", "orig": "sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 404.146, "r_x1": 294.217, "r_y1": 404.146, "r_x2": 294.217, "r_y2": 396.345, "r_x3": 53.529, "r_y3": 396.345, "coord_origin": "TOPLEFT"}, "text": "Those scientific documents present a limited variability in their", "orig": "Those scientific documents present a limited variability in their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 415.105, "r_x1": 294.274, "r_y1": 415.105, "r_x2": 294.274, "r_y2": 407.304, "r_x3": 53.798, "r_y3": 407.304, "coord_origin": "TOPLEFT"}, "text": "layouts, because they are typeset in uniform templates provided by", "orig": "layouts, because they are typeset in uniform templates provided by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 426.064, "r_x1": 295.035, "r_y1": 426.064, "r_x2": 295.035, "r_y2": 418.263, "r_x3": 53.798, "r_y3": 418.263, "coord_origin": "TOPLEFT"}, "text": "the publishers. Obviously, documents such as technical manuals,", "orig": "the publishers. Obviously, documents such as technical manuals,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 437.023, "r_x1": 294.047, "r_y1": 437.023, "r_x2": 294.047, "r_y2": 429.222, "r_x3": 53.798, "r_y3": 429.222, "coord_origin": "TOPLEFT"}, "text": "annual company reports, legal text, government tenders, etc. have", "orig": "annual company reports, legal text, government tenders, etc. have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 447.982, "r_x1": 294.049, "r_y1": 447.982, "r_x2": 294.049, "r_y2": 440.181, "r_x3": 53.574, "r_y3": 440.181, "coord_origin": "TOPLEFT"}, "text": "very different and partially unique layouts. As a consequence, the", "orig": "very different and partially unique layouts. As a consequence, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 458.941, "r_x1": 294.216, "r_y1": 458.941, "r_x2": 294.216, "r_y2": 451.14, "r_x3": 53.798, "r_y3": 451.14, "coord_origin": "TOPLEFT"}, "text": "layout predictions obtained from models trained on PubLayNet or", "orig": "layout predictions obtained from models trained on PubLayNet or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 469.9, "r_x1": 295.422, "r_y1": 469.9, "r_x2": 295.422, "r_y2": 462.099, "r_x3": 53.798, "r_y3": 462.099, "coord_origin": "TOPLEFT"}, "text": "DocBank is very reasonable when applied on scientific documents.", "orig": "DocBank is very reasonable when applied on scientific documents.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 480.859, "r_x1": 125.528, "r_y1": 480.859, "r_x2": 125.528, "r_y2": 473.058, "r_x3": 53.798, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "However, for more", "orig": "However, for more", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 128.608, "r_y0": 480.903, "r_x1": 153.768, "r_y1": 480.903, "r_x2": 153.768, "r_y2": 472.995, "r_x3": 128.608, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "artistic", "orig": "artistic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.152, "r_y0": 480.859, "r_x1": 165.164, "r_y1": 480.859, "r_x2": 165.164, "r_y2": 473.058, "r_x3": 157.152, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.248, "r_y0": 480.903, "r_x1": 201.493, "r_y1": 480.903, "r_x2": 201.493, "r_y2": 472.995, "r_x3": 168.248, "r_y3": 472.995, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.788, "r_y0": 480.859, "r_x1": 294.215, "r_y1": 480.859, "r_x2": 294.215, "r_y2": 473.058, "r_x3": 204.788, "r_y3": 473.058, "coord_origin": "TOPLEFT"}, "text": "layouts, we see sub-par", "orig": "layouts, we see sub-par", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 491.817, "r_x1": 294.047, "r_y1": 491.817, "r_x2": 294.047, "r_y2": 484.016, "r_x3": 53.798, "r_y3": 484.016, "coord_origin": "TOPLEFT"}, "text": "prediction quality from these models, which we demonstrate in", "orig": "prediction quality from these models, which we demonstrate in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 502.776, "r_x1": 89.081, "r_y1": 502.776, "r_x2": 89.081, "r_y2": 494.975, "r_x3": 53.798, "r_y3": 494.975, "coord_origin": "TOPLEFT"}, "text": "Section 5.", "orig": "Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Akeyproblem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or L A T E X sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5."}, {"label": "text", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.592, "t": 505.934, "r": 295.564, "b": 579.489, "coord_origin": "TOPLEFT"}, "confidence": 0.9795253276824951, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 513.735, "r_x1": 295.564, "r_y1": 513.735, "r_x2": 295.564, "r_y2": 505.934, "r_x3": 63.761, "r_y3": 505.934, "coord_origin": "TOPLEFT"}, "text": "In this paper, we present the DocLayNet dataset. It provides page-", "orig": "In this paper, we present the DocLayNet dataset. It provides page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 524.694, "r_x1": 294.213, "r_y1": 524.694, "r_x2": 294.213, "r_y2": 516.893, "r_x3": 53.798, "r_y3": 516.893, "coord_origin": "TOPLEFT"}, "text": "by-page layout annotation ground-truth using bounding-boxes for", "orig": "by-page layout annotation ground-truth using bounding-boxes for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.592, "r_y0": 535.653, "r_x1": 294.046, "r_y1": 535.653, "r_x2": 294.046, "r_y2": 527.852, "r_x3": 53.592, "r_y3": 527.852, "coord_origin": "TOPLEFT"}, "text": "11 distinct class labels on 80863 unique document pages, of which", "orig": "11 distinct class labels on 80863 unique document pages, of which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 546.612, "r_x1": 294.212, "r_y1": 546.612, "r_x2": 294.212, "r_y2": 538.811, "r_x3": 53.798, "r_y3": 538.811, "coord_origin": "TOPLEFT"}, "text": "a fraction carry double- or triple-annotations. DocLayNet is similar", "orig": "a fraction carry double- or triple-annotations. DocLayNet is similar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 557.571, "r_x1": 294.047, "r_y1": 557.571, "r_x2": 294.047, "r_y2": 549.77, "r_x3": 53.798, "r_y3": 549.77, "coord_origin": "TOPLEFT"}, "text": "in spirit to PubLayNet and DocBank and will likewise be made", "orig": "in spirit to PubLayNet and DocBank and will likewise be made", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 568.53, "r_x1": 134.289, "r_y1": 568.53, "r_x2": 134.289, "r_y2": 560.729, "r_x3": 53.798, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "available to the public", "orig": "available to the public", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.292, "r_y0": 564.908, "r_x1": 137.674, "r_y1": 564.908, "r_x2": 137.674, "r_y2": 558.58, "r_x3": 134.292, "r_y3": 558.58, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.418, "r_y0": 568.53, "r_x1": 294.047, "r_y1": 568.53, "r_x2": 294.047, "r_y2": 560.729, "r_x3": 140.418, "r_y3": 560.729, "coord_origin": "TOPLEFT"}, "text": "in order to stimulate the document-layout", "orig": "in order to stimulate the document-layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 579.489, "r_x1": 295.105, "r_y1": 579.489, "r_x2": 295.105, "r_y2": 571.688, "r_x3": 53.798, "r_y3": 571.688, "coord_origin": "TOPLEFT"}, "text": "analysis community. It distinguishes itself in the following aspects:", "orig": "analysis community. It distinguishes itself in the following aspects:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:"}, {"label": "list_item", "id": 9, "page_no": 1, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 584.9490000000001, "r": 295.562, "b": 614.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9687220454216003, "cells": [{"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 592.813, "r_x1": 74.221, "r_y1": 592.813, "r_x2": 74.221, "r_y2": 585.012, "r_x3": 64.708, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 592.857, "r_x1": 146.396, "r_y1": 592.857, "r_x2": 146.396, "r_y2": 584.9490000000001, "r_x3": 78.207, "r_y3": 584.9490000000001, "coord_origin": "TOPLEFT"}, "text": "Human Annotation", "orig": "Human Annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.417, "r_y0": 592.813, "r_x1": 148.514, "r_y1": 592.813, "r_x2": 148.514, "r_y2": 585.012, "r_x3": 146.417, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.753, "r_y0": 592.813, "r_x1": 295.03, "r_y1": 592.813, "r_x2": 295.03, "r_y2": 585.012, "r_x3": 150.753, "r_y3": 585.012, "coord_origin": "TOPLEFT"}, "text": "In contrast to PubLayNet and DocBank,", "orig": "In contrast to PubLayNet and DocBank,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 77.875, "r_y0": 603.7719999999999, "r_x1": 295.562, "r_y1": 603.7719999999999, "r_x2": 295.562, "r_y2": 595.971, "r_x3": 77.875, "r_y3": 595.971, "coord_origin": "TOPLEFT"}, "text": "we relied on human annotation instead of automation ap-", "orig": "we relied on human annotation instead of automation ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 614.731, "r_x1": 200.643, "r_y1": 614.731, "r_x2": 200.643, "r_y2": 606.9300000000001, "r_x3": 78.207, "r_y3": 606.9300000000001, "coord_origin": "TOPLEFT"}, "text": "proaches to generate the data set.", "orig": "proaches to generate the data set.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set."}, {"label": "list_item", "id": 11, "page_no": 1, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 617.826, "r": 294.263, "b": 636.648, "coord_origin": "TOPLEFT"}, "confidence": 0.9594229459762573, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 625.69, "r_x1": 74.221, "r_y1": 625.69, "r_x2": 74.221, "r_y2": 617.889, "r_x3": 64.708, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 625.734, "r_x1": 167.917, "r_y1": 625.734, "r_x2": 167.917, "r_y2": 617.826, "r_x3": 78.207, "r_y3": 617.826, "coord_origin": "TOPLEFT"}, "text": "Large Layout Variability", "orig": "Large Layout Variability", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.335, "r_y0": 625.69, "r_x1": 170.493, "r_y1": 625.69, "r_x2": 170.493, "r_y2": 617.889, "r_x3": 168.335, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.981, "r_y0": 625.69, "r_x1": 294.263, "r_y1": 625.69, "r_x2": 294.263, "r_y2": 617.889, "r_x3": 172.981, "r_y3": 617.889, "coord_origin": "TOPLEFT"}, "text": "We include diverse and complex", "orig": "We include diverse and complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 636.648, "r_x1": 245.457, "r_y1": 636.648, "r_x2": 245.457, "r_y2": 628.847, "r_x3": 78.207, "r_y3": 628.847, "coord_origin": "TOPLEFT"}, "text": "layouts from a large variety of public sources.", "orig": "layouts from a large variety of public sources.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources."}, {"label": "list_item", "id": 10, "page_no": 1, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 639.7429999999999, "r": 294.684, "b": 669.525, "coord_origin": "TOPLEFT"}, "confidence": 0.9615103006362915, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 647.607, "r_x1": 74.221, "r_y1": 647.607, "r_x2": 74.221, "r_y2": 639.806, "r_x3": 64.708, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 647.6510000000001, "r_x1": 143.517, "r_y1": 647.6510000000001, "r_x2": 143.517, "r_y2": 639.7429999999999, "r_x3": 78.207, "r_y3": 639.7429999999999, "coord_origin": "TOPLEFT"}, "text": "Detailed Label Set", "orig": "Detailed Label Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.02, "r_y0": 647.607, "r_x1": 146.178, "r_y1": 647.607, "r_x2": 146.178, "r_y2": 639.806, "r_x3": 144.02, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.556, "r_y0": 647.607, "r_x1": 294.047, "r_y1": 647.607, "r_x2": 294.047, "r_y2": 639.806, "r_x3": 148.556, "r_y3": 639.806, "coord_origin": "TOPLEFT"}, "text": "We define 11 class labels to distinguish", "orig": "We define 11 class labels to distinguish", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 658.566, "r_x1": 294.684, "r_y1": 658.566, "r_x2": 294.684, "r_y2": 650.765, "r_x3": 78.207, "r_y3": 650.765, "coord_origin": "TOPLEFT"}, "text": "layout features in high detail. PubLayNet provides 5 labels;", "orig": "layout features in high detail. PubLayNet provides 5 labels;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 669.525, "r_x1": 276.338, "r_y1": 669.525, "r_x2": 276.338, "r_y2": 661.7239999999999, "r_x3": 78.207, "r_y3": 661.7239999999999, "coord_origin": "TOPLEFT"}, "text": "DocBank provides 13, although not a superset of ours.", "orig": "DocBank provides 13, although not a superset of ours.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours."}, {"label": "list_item", "id": 13, "page_no": 1, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 64.708, "t": 672.62, "r": 295.564, "b": 691.443, "coord_origin": "TOPLEFT"}, "confidence": 0.9447500109672546, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 680.484, "r_x1": 74.221, "r_y1": 680.484, "r_x2": 74.221, "r_y2": 672.683, "r_x3": 64.708, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 680.528, "r_x1": 163.784, "r_y1": 680.528, "r_x2": 163.784, "r_y2": 672.62, "r_x3": 78.207, "r_y3": 672.62, "coord_origin": "TOPLEFT"}, "text": "Redundant Annotations", "orig": "Redundant Annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.994, "r_y0": 680.484, "r_x1": 166.152, "r_y1": 680.484, "r_x2": 166.152, "r_y2": 672.683, "r_x3": 163.994, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.631, "r_y0": 680.484, "r_x1": 295.564, "r_y1": 680.484, "r_x2": 295.564, "r_y2": 672.683, "r_x3": 168.631, "r_y3": 672.683, "coord_origin": "TOPLEFT"}, "text": "A fraction of the pages in the Do-", "orig": "A fraction of the pages in the Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 691.443, "r_x1": 295.427, "r_y1": 691.443, "r_x2": 295.427, "r_y2": 683.642, "r_x3": 78.207, "r_y3": 683.642, "coord_origin": "TOPLEFT"}, "text": "cLayNet data set carry more than one human annotation.", "orig": "cLayNet data set carry more than one human annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation."}, {"label": "footnote", "id": 17, "page_no": 1, "cluster": {"id": 17, "label": "footnote", "bbox": {"l": 53.672, "t": 700.897, "r": 216.027, "b": 708.629, "coord_origin": "TOPLEFT"}, "confidence": 0.8584771752357483, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.672, "r_y0": 705.664, "r_x1": 56.22, "r_y1": 705.664, "r_x2": 56.22, "r_y2": 700.897, "r_x3": 53.672, "r_y3": 700.897, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.718, "r_y0": 708.629, "r_x1": 216.027, "r_y1": 708.629, "r_x2": 216.027, "r_y2": 702.561, "r_x3": 56.718, "r_y3": 702.561, "coord_origin": "TOPLEFT"}, "text": "https://developer.ibm.com/exchanges/data/all/doclaynet", "orig": "https://developer.ibm.com/exchanges/data/all/doclaynet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 https://developer.ibm.com/exchanges/data/all/doclaynet"}, {"label": "text", "id": 18, "page_no": 1, "cluster": {"id": 18, "label": "text", "bbox": {"l": 342.095, "t": 87.79399999999998, "r": 558.432, "b": 106.55399999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.7997146844863892, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.095, "r_y0": 95.59500000000003, "r_x1": 558.432, "r_y1": 95.59500000000003, "r_x2": 558.432, "r_y2": 87.79399999999998, "r_x3": 342.095, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "This enables experimentation with annotation uncertainty", "orig": "This enables experimentation with annotation uncertainty", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 106.55399999999997, "r_x1": 445.836, "r_y1": 106.55399999999997, "r_x2": 445.836, "r_y2": 98.75300000000004, "r_x3": 342.364, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "and quality control analysis.", "orig": "and quality control analysis.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This enables experimentation with annotation uncertainty and quality control analysis."}, {"label": "list_item", "id": 12, "page_no": 1, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 328.865, "t": 109.649, "r": 559.721, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9563499093055725, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.865, "r_y0": 117.51300000000003, "r_x1": 338.378, "r_y1": 117.51300000000003, "r_x2": 338.378, "r_y2": 109.71199999999999, "r_x3": 328.865, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 117.55700000000002, "r_x1": 487.253, "r_y1": 117.55700000000002, "r_x2": 487.253, "r_y2": 109.649, "r_x3": 342.364, "r_y3": 109.649, "coord_origin": "TOPLEFT"}, "text": "Pre-defined Train-, Test- & Validation-set", "orig": "Pre-defined Train-, Test- & Validation-set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.759, "r_y0": 117.51300000000003, "r_x1": 489.867, "r_y1": 117.51300000000003, "r_x2": 489.867, "r_y2": 109.71199999999999, "r_x3": 487.759, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.099, "r_y0": 117.51300000000003, "r_x1": 558.203, "r_y1": 117.51300000000003, "r_x2": 558.203, "r_y2": 109.71199999999999, "r_x3": 492.099, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "Like DocBank, we", "orig": "Like DocBank, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 128.47199999999998, "r_x1": 559.721, "r_y1": 128.47199999999998, "r_x2": 559.721, "r_y2": 120.67100000000005, "r_x3": 342.364, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "provide fixed train-, test- & validation-sets to ensure propor-", "orig": "provide fixed train-, test- & validation-sets to ensure propor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 139.42999999999995, "r_x1": 558.201, "r_y1": 139.42999999999995, "r_x2": 558.201, "r_y2": 131.62900000000002, "r_x3": 342.364, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "tional representation of the class-labels. Further, we prevent", "orig": "tional representation of the class-labels. Further, we prevent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 150.389, "r_x1": 558.201, "r_y1": 150.389, "r_x2": 558.201, "r_y2": 142.58799999999997, "r_x3": 342.364, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "leakage of unique layouts across sets, which has a large effect", "orig": "leakage of unique layouts across sets, which has a large effect", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 342.364, "r_y0": 161.34799999999996, "r_x1": 438.062, "r_y1": 161.34799999999996, "r_x2": 438.062, "r_y2": 153.54700000000003, "r_x3": 342.364, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "on model accuracy scores.", "orig": "on model accuracy scores.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.623, "t": 168.40599999999995, "r": 559.19, "b": 220.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9813903570175171, "cells": [{"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 176.207, "r_x1": 559.19, "r_y1": 176.207, "r_x2": 559.19, "r_y2": 168.40599999999995, "r_x3": 327.918, "r_y3": 168.40599999999995, "coord_origin": "TOPLEFT"}, "text": "All aspects outlined above are detailed in Section 3. In Section 4,", "orig": "All aspects outlined above are detailed in Section 3. In Section 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 187.16600000000005, "r_x1": 558.204, "r_y1": 187.16600000000005, "r_x2": 558.204, "r_y2": 179.365, "r_x3": 317.623, "r_y3": 179.365, "coord_origin": "TOPLEFT"}, "text": "we will elaborate on how we designed and executed this large-scale", "orig": "we will elaborate on how we designed and executed this large-scale", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 198.125, "r_x1": 558.198, "r_y1": 198.125, "r_x2": 558.198, "r_y2": 190.32399999999996, "r_x3": 317.955, "r_y3": 190.32399999999996, "coord_origin": "TOPLEFT"}, "text": "human annotation campaign. We will also share key insights and", "orig": "human annotation campaign. We will also share key insights and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 209.08399999999995, "r_x1": 558.206, "r_y1": 209.08399999999995, "r_x2": 558.206, "r_y2": 201.28300000000002, "r_x3": 317.955, "r_y3": 201.28300000000002, "coord_origin": "TOPLEFT"}, "text": "lessons learned that might prove helpful for other parties planning", "orig": "lessons learned that might prove helpful for other parties planning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 220.043, "r_x1": 434.949, "r_y1": 220.043, "r_x2": 434.949, "r_y2": 212.24199999999996, "r_x3": 317.955, "r_y3": 212.24199999999996, "coord_origin": "TOPLEFT"}, "text": "to set up annotation campaigns.", "orig": "to set up annotation campaigns.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.731, "t": 223.20100000000002, "r": 559.582, "b": 307.714, "coord_origin": "TOPLEFT"}, "confidence": 0.9844217300415039, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 231.00199999999995, "r_x1": 558.198, "r_y1": 231.00199999999995, "r_x2": 558.198, "r_y2": 223.20100000000002, "r_x3": 327.918, "r_y3": 223.20100000000002, "coord_origin": "TOPLEFT"}, "text": "In Section 5, we will present baseline accuracy numbers for a", "orig": "In Section 5, we will present baseline accuracy numbers for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.731, "r_y0": 241.961, "r_x1": 558.199, "r_y1": 241.961, "r_x2": 558.199, "r_y2": 234.15999999999997, "r_x3": 317.731, "r_y3": 234.15999999999997, "coord_origin": "TOPLEFT"}, "text": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "orig": "variety of object detection methods (Faster R-CNN, Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 252.91999999999996, "r_x1": 558.204, "r_y1": 252.91999999999996, "r_x2": 558.204, "r_y2": 245.11900000000003, "r_x3": 317.955, "r_y3": 245.11900000000003, "coord_origin": "TOPLEFT"}, "text": "and YOLOv5) trained on DocLayNet. We further show how the", "orig": "and YOLOv5) trained on DocLayNet. We further show how the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 263.879, "r_x1": 558.206, "r_y1": 263.879, "r_x2": 558.206, "r_y2": 256.078, "r_x3": 317.955, "r_y3": 256.078, "coord_origin": "TOPLEFT"}, "text": "model performance is impacted by varying the DocLayNet dataset", "orig": "model performance is impacted by varying the DocLayNet dataset", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 274.837, "r_x1": 558.199, "r_y1": 274.837, "r_x2": 558.199, "r_y2": 267.03599999999994, "r_x3": 317.955, "r_y3": 267.03599999999994, "coord_origin": "TOPLEFT"}, "text": "size, reducing the label set and modifying the train/test-split. Last", "orig": "size, reducing the label set and modifying the train/test-split. Last", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 285.796, "r_x1": 558.204, "r_y1": 285.796, "r_x2": 558.204, "r_y2": 277.995, "r_x3": 317.955, "r_y3": 277.995, "coord_origin": "TOPLEFT"}, "text": "but not least, we compare the performance of models trained on", "orig": "but not least, we compare the performance of models trained on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 296.755, "r_x1": 558.202, "r_y1": 296.755, "r_x2": 558.202, "r_y2": 288.954, "r_x3": 317.955, "r_y3": 288.954, "coord_origin": "TOPLEFT"}, "text": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "orig": "PubLayNet, DocBank and DocLayNet and demonstrate that a model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 307.714, "r_x1": 559.582, "r_y1": 307.714, "r_x2": 559.582, "r_y2": 299.913, "r_x3": 317.955, "r_y3": 299.913, "coord_origin": "TOPLEFT"}, "text": "trained on DocLayNet provides overall more robust layout recovery.", "orig": "trained on DocLayNet provides overall more robust layout recovery.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Section 5, we will present baseline accuracy numbers for a variety of object detection methods (Faster R-CNN, Mask R-CNN and YOLOv5) trained on DocLayNet. We further show how the model performance is impacted by varying the DocLayNet dataset size, reducing the label set and modifying the train/test-split. Last but not least, we compare the performance of models trained on PubLayNet, DocBank and DocLayNet and demonstrate that a model trained on DocLayNet provides overall more robust layout recovery."}, {"label": "section_header", "id": 15, "page_no": 1, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 317.955, "t": 322.147, "r": 421.744, "b": 331.214, "coord_origin": "TOPLEFT"}, "confidence": 0.9347459077835083, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.214, "r_x1": 323.562, "r_y1": 331.214, "r_x2": 323.562, "r_y2": 322.147, "r_x3": 317.955, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 331.214, "r_x1": 421.744, "r_y1": 331.214, "r_x2": 421.744, "r_y2": 322.147, "r_x3": 334.471, "r_y3": 322.147, "coord_origin": "TOPLEFT"}, "text": "RELATED WORK", "orig": "RELATED WORK", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 RELATED WORK"}, {"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.525, "t": 346.763, "r": 559.716, "b": 464.153, "coord_origin": "TOPLEFT"}, "confidence": 0.9860377907752991, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.525, "r_y0": 354.564, "r_x1": 559.713, "r_y1": 354.564, "r_x2": 559.713, "r_y2": 346.763, "r_x3": 317.525, "r_y3": 346.763, "coord_origin": "TOPLEFT"}, "text": "While early approaches in document-layout analysis used rule-", "orig": "While early approaches in document-layout analysis used rule-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 365.523, "r_x1": 558.203, "r_y1": 365.523, "r_x2": 558.203, "r_y2": 357.722, "r_x3": 317.955, "r_y3": 357.722, "coord_origin": "TOPLEFT"}, "text": "based algorithms and heuristics [8], the problem is lately addressed", "orig": "based algorithms and heuristics [8], the problem is lately addressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 376.482, "r_x1": 559.716, "r_y1": 376.482, "r_x2": 559.716, "r_y2": 368.681, "r_x3": 317.623, "r_y3": 368.681, "coord_origin": "TOPLEFT"}, "text": "with deep learning methods. The most common approach is to lever-", "orig": "with deep learning methods. The most common approach is to lever-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 387.441, "r_x1": 558.434, "r_y1": 387.441, "r_x2": 558.434, "r_y2": 379.64, "r_x3": 317.955, "r_y3": 379.64, "coord_origin": "TOPLEFT"}, "text": "age object detection models [9-15]. In the last decade, the accuracy", "orig": "age object detection models [9-15]. In the last decade, the accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 398.4, "r_x1": 559.186, "r_y1": 398.4, "r_x2": 559.186, "r_y2": 390.599, "r_x3": 317.955, "r_y3": 390.599, "coord_origin": "TOPLEFT"}, "text": "and speed of these models has increased dramatically. Furthermore,", "orig": "and speed of these models has increased dramatically. Furthermore,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 409.359, "r_x1": 558.205, "r_y1": 409.359, "r_x2": 558.205, "r_y2": 401.558, "r_x3": 317.955, "r_y3": 401.558, "coord_origin": "TOPLEFT"}, "text": "most state-of-the-art object detection methods can be trained and", "orig": "most state-of-the-art object detection methods can be trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 420.318, "r_x1": 558.204, "r_y1": 420.318, "r_x2": 558.204, "r_y2": 412.517, "r_x3": 317.955, "r_y3": 412.517, "coord_origin": "TOPLEFT"}, "text": "applied with very little work, thanks to a standardisation effort", "orig": "applied with very little work, thanks to a standardisation effort", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 431.277, "r_x1": 558.205, "r_y1": 431.277, "r_x2": 558.205, "r_y2": 423.476, "r_x3": 317.955, "r_y3": 423.476, "coord_origin": "TOPLEFT"}, "text": "of the ground-truth data format [16] and common deep-learning", "orig": "of the ground-truth data format [16] and common deep-learning", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 442.235, "r_x1": 558.2, "r_y1": 442.235, "r_x2": 558.2, "r_y2": 434.434, "r_x3": 317.955, "r_y3": 434.434, "coord_origin": "TOPLEFT"}, "text": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "orig": "frameworks [17]. Reference data sets such as PubLayNet [6] and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 453.194, "r_x1": 559.716, "r_y1": 453.194, "r_x2": 559.716, "r_y2": 445.393, "r_x3": 317.955, "r_y3": 445.393, "coord_origin": "TOPLEFT"}, "text": "DocBank provide their data in the commonly accepted COCO for-", "orig": "DocBank provide their data in the commonly accepted COCO for-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 464.153, "r_x1": 350.907, "r_y1": 464.153, "r_x2": 350.907, "r_y2": 456.352, "r_x3": 317.955, "r_y3": 456.352, "coord_origin": "TOPLEFT"}, "text": "mat [16].", "orig": "mat [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 467.311, "r": 559.186, "b": 551.825, "coord_origin": "TOPLEFT"}, "confidence": 0.9861046671867371, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 475.112, "r_x1": 558.198, "r_y1": 475.112, "r_x2": 558.198, "r_y2": 467.311, "r_x3": 327.918, "r_y3": 467.311, "coord_origin": "TOPLEFT"}, "text": "Lately, new types of ML models for document-layout analysis", "orig": "Lately, new types of ML models for document-layout analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 486.071, "r_x1": 558.205, "r_y1": 486.071, "r_x2": 558.205, "r_y2": 478.27, "r_x3": 317.955, "r_y3": 478.27, "coord_origin": "TOPLEFT"}, "text": "have emerged in the community [18-21]. These models do not", "orig": "have emerged in the community [18-21]. These models do not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 497.03, "r_x1": 558.206, "r_y1": 497.03, "r_x2": 558.206, "r_y2": 489.229, "r_x3": 317.955, "r_y3": 489.229, "coord_origin": "TOPLEFT"}, "text": "approach the problem of layout analysis purely based on an image", "orig": "approach the problem of layout analysis purely based on an image", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 507.989, "r_x1": 559.186, "r_y1": 507.989, "r_x2": 559.186, "r_y2": 500.188, "r_x3": 317.955, "r_y3": 500.188, "coord_origin": "TOPLEFT"}, "text": "representation of the page, as computer vision methods do. Instead,", "orig": "representation of the page, as computer vision methods do. Instead,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.948, "r_x1": 558.2, "r_y1": 518.948, "r_x2": 558.2, "r_y2": 511.147, "r_x3": 317.955, "r_y3": 511.147, "coord_origin": "TOPLEFT"}, "text": "they combine the text tokens and image representation of a page", "orig": "they combine the text tokens and image representation of a page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.9069999999999, "r_x1": 558.206, "r_y1": 529.9069999999999, "r_x2": 558.206, "r_y2": 522.106, "r_x3": 317.955, "r_y3": 522.106, "coord_origin": "TOPLEFT"}, "text": "in order to obtain a segmentation. While the reported accuracies", "orig": "in order to obtain a segmentation. While the reported accuracies", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.866, "r_x1": 558.202, "r_y1": 540.866, "r_x2": 558.202, "r_y2": 533.065, "r_x3": 317.955, "r_y3": 533.065, "coord_origin": "TOPLEFT"}, "text": "appear to be promising, a broadly accepted data format which links", "orig": "appear to be promising, a broadly accepted data format which links", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.825, "r_x1": 503.326, "r_y1": 551.825, "r_x2": 503.326, "r_y2": 544.024, "r_x3": 317.955, "r_y3": 544.024, "coord_origin": "TOPLEFT"}, "text": "geometric and textual features has yet to establish.", "orig": "geometric and textual features has yet to establish.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish."}, {"label": "section_header", "id": 16, "page_no": 1, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 317.955, "t": 566.258, "r": 477.457, "b": 575.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9331073760986328, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.325, "r_x1": 323.562, "r_y1": 575.325, "r_x2": 323.562, "r_y2": 566.258, "r_x3": 317.955, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 575.325, "r_x1": 477.457, "r_y1": 575.325, "r_x2": 477.457, "r_y2": 566.258, "r_x3": 334.471, "r_y3": 566.258, "coord_origin": "TOPLEFT"}, "text": "THE DOCLAYNET DATASET", "orig": "THE DOCLAYNET DATASET", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 THE DOCLAYNET DATASET"}, {"label": "text", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.955, "t": 590.874, "r": 559.713, "b": 675.387, "coord_origin": "TOPLEFT"}, "confidence": 0.9852981567382812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 598.675, "r_x1": 558.202, "r_y1": 598.675, "r_x2": 558.202, "r_y2": 590.874, "r_x3": 317.955, "r_y3": 590.874, "coord_origin": "TOPLEFT"}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "orig": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 609.634, "r_x1": 558.202, "r_y1": 609.634, "r_x2": 558.202, "r_y2": 601.833, "r_x3": 317.955, "r_y3": 601.833, "coord_origin": "TOPLEFT"}, "text": "instances of human annotations, and 1591 carry three. This amounts", "orig": "instances of human annotations, and 1591 carry three. This amounts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 620.592, "r_x1": 559.713, "r_y1": 620.592, "r_x2": 559.713, "r_y2": 612.7909999999999, "r_x3": 317.955, "r_y3": 612.7909999999999, "coord_origin": "TOPLEFT"}, "text": "to 91104 total annotation instances. The annotations provide lay-", "orig": "to 91104 total annotation instances. The annotations provide lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 631.5509999999999, "r_x1": 559.713, "r_y1": 631.5509999999999, "r_x2": 559.713, "r_y2": 623.75, "r_x3": 317.955, "r_y3": 623.75, "coord_origin": "TOPLEFT"}, "text": "out information in the shape of labeled, rectangular bounding-", "orig": "out information in the shape of labeled, rectangular bounding-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.51, "r_x1": 539.92, "r_y1": 642.51, "r_x2": 539.92, "r_y2": 634.7090000000001, "r_x3": 317.955, "r_y3": 634.7090000000001, "coord_origin": "TOPLEFT"}, "text": "boxes. We define 11 distinct labels for layout features, namely", "orig": "boxes. We define 11 distinct labels for layout features, namely", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 542.158, "r_y0": 642.554, "r_x1": 559.099, "r_y1": 642.554, "r_x2": 559.099, "r_y2": 634.646, "r_x3": 542.158, "r_y3": 634.646, "coord_origin": "TOPLEFT"}, "text": "Cap-", "orig": "Cap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 653.513, "r_x1": 331.863, "r_y1": 653.513, "r_x2": 331.863, "r_y2": 645.605, "r_x3": 317.955, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "tion", "orig": "tion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.863, "r_y0": 653.469, "r_x1": 333.84, "r_y1": 653.469, "r_x2": 333.84, "r_y2": 645.668, "r_x3": 331.863, 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370.254, "r_y3": 645.605, "coord_origin": "TOPLEFT"}, "text": "Formula List-item", "orig": "Formula List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.055, "r_y0": 653.469, "r_x1": 402.032, "r_y1": 653.469, "r_x2": 402.032, "r_y2": 645.668, "r_x3": 400.055, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 436.195, "r_y0": 653.469, "r_x1": 438.172, "r_y1": 653.469, "r_x2": 438.172, "r_y2": 645.668, "r_x3": 436.195, "r_y3": 645.668, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 440.396, "r_y0": 653.513, "r_x1": 480.61, "r_y1": 653.513, "r_x2": 480.61, 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{"r_x0": 449.62, "r_y0": 664.428, "r_x1": 558.202, "r_y1": 664.428, "r_x2": 558.202, "r_y2": 656.627, "r_x3": 449.62, "r_y3": 656.627, "coord_origin": "TOPLEFT"}, "text": "Our reasoning for picking this", "orig": "Our reasoning for picking this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.387, "r_x1": 472.222, "r_y1": 675.387, "r_x2": 472.222, "r_y2": 667.586, "r_x3": 317.955, "r_y3": 667.586, "coord_origin": "TOPLEFT"}, "text": "particular label set is detailed in Section 4.", "orig": "particular label set is detailed in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula List-item , , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4."}, {"label": "text", "id": 8, "page_no": 1, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 678.545, "r": 558.204, "b": 708.264, "coord_origin": "TOPLEFT"}, "confidence": 0.9749757051467896, "cells": [{"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 686.346, "r_x1": 558.198, "r_y1": 686.346, "r_x2": 558.198, "r_y2": 678.545, "r_x3": 327.918, "r_y3": 678.545, "coord_origin": "TOPLEFT"}, "text": "In addition to open intellectual property constraints for the", "orig": "In addition to open intellectual property constraints for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.3050000000001, "r_x1": 558.199, "r_y1": 697.3050000000001, "r_x2": 558.199, "r_y2": 689.504, "r_x3": 317.955, "r_y3": 689.504, "coord_origin": "TOPLEFT"}, "text": "source documents, we required that the documents in DocLayNet", "orig": "source documents, we required that the documents in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.264, "r_x1": 558.204, "r_y1": 708.264, "r_x2": 558.204, "r_y2": 700.463, "r_x3": 317.955, "r_y3": 700.463, "coord_origin": "TOPLEFT"}, "text": "adhere to a few conditions. Firstly, we kept scanned documents", "orig": "adhere to a few conditions. Firstly, we kept scanned documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents"}], "headers": [{"label": "page_header", "id": 19, "page_no": 1, "cluster": {"id": 19, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7682238221168518, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", 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"coord_origin": "TOPLEFT"}, "text": "Manuals", "orig": "Manuals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.407, "r_y0": 179.30899999999997, "r_x1": 209.669, "r_y1": 179.30899999999997, "r_x2": 209.669, "r_y2": 172.57100000000003, "r_x3": 194.407, "r_y3": 172.57100000000003, "coord_origin": "TOPLEFT"}, "text": "21%", "orig": "21%", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 244.33900000000006, "r_x1": 294.044, "r_y1": 244.33900000000006, "r_x2": 294.044, "r_y2": 236.88599999999997, "r_x3": 53.798, "r_y3": 236.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Figure 2: Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 255.298, "r_x1": 96.756, "r_y1": 255.298, "r_x2": 96.756, "r_y2": 247.84500000000003, "r_x3": 53.798, "r_y3": 247.84500000000003, "coord_origin": "TOPLEFT"}, "text": "categories.", "orig": "categories.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 300.994, "r_x1": 294.049, "r_y1": 300.994, "r_x2": 294.049, "r_y2": 293.193, "r_x3": 53.798, "r_y3": 293.193, "coord_origin": "TOPLEFT"}, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 311.953, "r_x1": 98.881, "r_y1": 311.953, "r_x2": 98.881, "r_y2": 304.152, "r_x3": 53.798, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.071, "r_y0": 310.088, "r_x1": 104.774, "r_y1": 310.088, "r_x2": 104.774, "r_y2": 306.08, "r_x3": 99.071, "r_y3": 306.08, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.464, "r_y0": 311.953, "r_x1": 294.262, "r_y1": 311.953, "r_x2": 294.262, "r_y2": 304.152, "r_x3": 107.464, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "10 pages) with technical content, dense in complex", "orig": "10 pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 322.912, "r_x1": 294.047, "r_y1": 322.912, "r_x2": 294.047, "r_y2": 315.111, "r_x3": 53.798, "r_y3": 315.111, "coord_origin": "TOPLEFT"}, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, 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For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. 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"coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 300.994, "r_x1": 294.049, "r_y1": 300.994, "r_x2": 294.049, "r_y2": 293.193, "r_x3": 53.798, "r_y3": 293.193, "coord_origin": "TOPLEFT"}, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 311.953, "r_x1": 98.881, "r_y1": 311.953, "r_x2": 98.881, "r_y2": 304.152, "r_x3": 53.798, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.071, "r_y0": 310.088, "r_x1": 104.774, "r_y1": 310.088, "r_x2": 104.774, "r_y2": 306.08, "r_x3": 99.071, "r_y3": 306.08, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.464, "r_y0": 311.953, "r_x1": 294.262, "r_y1": 311.953, "r_x2": 294.262, "r_y2": 304.152, "r_x3": 107.464, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "10 pages) with technical content, dense in complex", "orig": "10 pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 322.912, "r_x1": 294.047, "r_y1": 322.912, "r_x2": 294.047, "r_y2": 315.111, "r_x3": 53.798, "r_y3": 315.111, "coord_origin": "TOPLEFT"}, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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"coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.367, "r_y0": 399.624, "r_x1": 295.557, "r_y1": 399.624, "r_x2": 295.557, "r_y2": 391.823, "r_x3": 219.367, "r_y3": 391.823, "coord_origin": "TOPLEFT"}, "text": "Each document cate-", "orig": "Each document cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 410.583, "r_x1": 294.045, "r_y1": 410.583, "r_x2": 294.045, "r_y2": 402.782, "r_x3": 53.798, "r_y3": 402.782, "coord_origin": "TOPLEFT"}, "text": "gory was sourced from various repositories. For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 512.371, "r": 295.562, "b": 607.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 611.001, "r": 295.564, "b": 684.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 195.79, "b": 708.059, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 87.79399999999998, "r": 559.192, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 164.50599999999997, "r": 558.438, "b": 270.937, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "text", "bbox": {"l": 316.942, "t": 274.095, "r": 559.722, "b": 588.746, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 470.213, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 559.714, "b": 705.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. 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"coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 300.994, "r_x1": 294.049, "r_y1": 300.994, "r_x2": 294.049, "r_y2": 293.193, "r_x3": 53.798, "r_y3": 293.193, "coord_origin": "TOPLEFT"}, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). 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Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. 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For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 512.371, "r": 295.562, "b": 607.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 611.001, "r": 295.564, "b": 684.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 195.79, "b": 708.059, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 87.79399999999998, "r": 559.192, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 164.50599999999997, "r": 558.438, "b": 270.937, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.942, "t": 274.095, "r": 559.722, "b": 588.746, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 470.213, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 559.714, "b": 705.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. In phase four,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "body": [{"label": "picture", "id": 8, "page_no": 2, "cluster": {"id": 8, "label": "picture", "bbox": {"l": 88.33030700683594, "t": 92.88652038574219, "r": 263.7049560546875, "b": 220.56826782226562, "coord_origin": "TOPLEFT"}, "confidence": 0.9698190689086914, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.113, "r_y0": 141.27499999999998, "r_x1": 262.976, "r_y1": 141.27499999999998, "r_x2": 262.976, "r_y2": 134.53599999999994, "r_x3": 237.113, "r_y3": 134.53599999999994, "coord_origin": "TOPLEFT"}, "text": "Patents", "orig": "Patents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.879, "r_y0": 148.64999999999998, "r_x1": 213.9, "r_y1": 148.64999999999998, "r_x2": 213.9, "r_y2": 141.91099999999994, "r_x3": 202.879, "r_y3": 141.91099999999994, "coord_origin": "TOPLEFT"}, "text": "8%", "orig": "8%", "text_direction": 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Distribution of DocLayNet pages across document", "orig": "Figure 2: Distribution of DocLayNet pages across document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 255.298, "r_x1": 96.756, "r_y1": 255.298, "r_x2": 96.756, "r_y2": 247.84500000000003, "r_x3": 53.798, "r_y3": 247.84500000000003, "coord_origin": "TOPLEFT"}, "text": "categories.", "orig": "categories.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: Distribution of DocLayNet pages across document categories."}, {"label": "text", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 282.234, "r": 294.274, "b": 366.747, "coord_origin": "TOPLEFT"}, "confidence": 0.9866552352905273, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 290.035, "r_x1": 294.046, "r_y1": 290.035, "r_x2": 294.046, "r_y2": 282.234, "r_x3": 53.798, "r_y3": 282.234, "coord_origin": "TOPLEFT"}, "text": "to a minimum, since they introduce difficulties in annotation (see", "orig": "to a minimum, since they introduce difficulties in annotation (see", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 300.994, "r_x1": 294.049, "r_y1": 300.994, "r_x2": 294.049, "r_y2": 293.193, "r_x3": 53.798, "r_y3": 293.193, "coord_origin": "TOPLEFT"}, "text": "Section 4). As a second condition, we focussed on medium to large", "orig": "Section 4). As a second condition, we focussed on medium to large", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 311.953, "r_x1": 98.881, "r_y1": 311.953, "r_x2": 98.881, "r_y2": 304.152, "r_x3": 53.798, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "documents (", "orig": "documents (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 99.071, "r_y0": 310.088, "r_x1": 104.774, "r_y1": 310.088, "r_x2": 104.774, "r_y2": 306.08, "r_x3": 99.071, "r_y3": 306.08, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 107.464, "r_y0": 311.953, "r_x1": 294.262, "r_y1": 311.953, "r_x2": 294.262, "r_y2": 304.152, "r_x3": 107.464, "r_y3": 304.152, "coord_origin": "TOPLEFT"}, "text": "10 pages) with technical content, dense in complex", "orig": "10 pages) with technical content, dense in complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 322.912, "r_x1": 294.047, "r_y1": 322.912, "r_x2": 294.047, "r_y2": 315.111, "r_x3": 53.798, "r_y3": 315.111, "coord_origin": "TOPLEFT"}, "text": "tables, figures, plots and captions. Such documents carry a lot of", "orig": "tables, figures, plots and captions. Such documents carry a lot of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 333.87, "r_x1": 294.274, "r_y1": 333.87, "r_x2": 294.274, "r_y2": 326.069, "r_x3": 53.798, "r_y3": 326.069, "coord_origin": "TOPLEFT"}, "text": "information value, but are often hard to analyse with high accuracy", "orig": "information value, but are often hard to analyse with high accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 344.829, "r_x1": 294.042, "r_y1": 344.829, "r_x2": 294.042, "r_y2": 337.028, "r_x3": 53.798, "r_y3": 337.028, "coord_origin": "TOPLEFT"}, "text": "due to their challenging layouts. Counterexamples of documents", "orig": "due to their challenging layouts. Counterexamples of documents", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 355.788, "r_x1": 294.047, "r_y1": 355.788, "r_x2": 294.047, "r_y2": 347.987, "r_x3": 53.798, "r_y3": 347.987, "coord_origin": "TOPLEFT"}, "text": "not included in the dataset are receipts, invoices, hand-written", "orig": "not included in the dataset are receipts, invoices, hand-written", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 366.747, "r_x1": 251.731, "r_y1": 366.747, "r_x2": 251.731, "r_y2": 358.946, "r_x3": 53.798, "r_y3": 358.946, "coord_origin": "TOPLEFT"}, "text": "documents or photographs showing 'text in the wild\".", "orig": "documents or photographs showing 'text in the wild\".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing 'text in the wild\"."}, {"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.574, "t": 369.905, "r": 295.56, "b": 509.213, "coord_origin": "TOPLEFT"}, "confidence": 0.987700879573822, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 377.706, "r_x1": 295.559, "r_y1": 377.706, "r_x2": 295.559, "r_y2": 369.905, "r_x3": 63.761, "r_y3": 369.905, "coord_origin": "TOPLEFT"}, "text": "The pages in DocLayNet can be grouped into six distinct cate-", "orig": "The pages in DocLayNet can be grouped into six distinct cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 388.665, "r_x1": 105.905, "r_y1": 388.665, "r_x2": 105.905, "r_y2": 380.864, "r_x3": 53.798, 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"b": 0, "a": 255}, "rect": {"r_x0": 215.156, "r_y0": 399.624, "r_x1": 217.119, "r_y1": 399.624, "r_x2": 217.119, "r_y2": 391.823, "r_x3": 215.156, "r_y3": 391.823, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.367, "r_y0": 399.624, "r_x1": 295.557, "r_y1": 399.624, "r_x2": 295.557, "r_y2": 391.823, "r_x3": 219.367, "r_y3": 391.823, "coord_origin": "TOPLEFT"}, "text": "Each document cate-", "orig": "Each document cate-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 410.583, "r_x1": 294.045, "r_y1": 410.583, "r_x2": 294.045, "r_y2": 402.782, "r_x3": 53.798, "r_y3": 402.782, "coord_origin": "TOPLEFT"}, "text": "gory was sourced from various repositories. For example, Financial", "orig": "gory was sourced from various repositories. For example, Financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.542, "r_x1": 132.195, "r_y1": 421.542, "r_x2": 132.195, "r_y2": 413.741, "r_x3": 53.798, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "Reports contain both", "orig": "Reports contain both", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.528, "r_y0": 421.586, "r_x1": 167.773, "r_y1": 421.586, "r_x2": 167.773, "r_y2": 413.678, "r_x3": 134.528, "r_y3": 413.678, "coord_origin": "TOPLEFT"}, "text": "free-style", "orig": "free-style", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.314, "r_y0": 421.542, "r_x1": 255.738, "r_y1": 417.92, "r_x2": 255.738, "r_y2": 411.592, "r_x3": 170.314, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "format annual reports 2", "orig": "format annual reports 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.566, "r_y0": 421.542, "r_x1": 295.56, "r_y1": 421.542, "r_x2": 295.56, "r_y2": 413.741, "r_x3": 258.566, "r_y3": 413.741, "coord_origin": "TOPLEFT"}, "text": "which ex-", "orig": "which ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.501, "r_x1": 294.044, "r_y1": 432.501, "r_x2": 294.044, "r_y2": 424.7, "r_x3": 53.798, "r_y3": 424.7, "coord_origin": "TOPLEFT"}, "text": "pose company-specific, artistic layouts as well as the more formal", "orig": "pose company-specific, artistic layouts as well as the more formal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 443.459, "r_x1": 197.59, "r_y1": 443.459, "r_x2": 197.59, "r_y2": 435.658, "r_x3": 53.798, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "SEC filings. The two largest categories (", "orig": "SEC filings. The two largest categories (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.591, "r_y0": 443.503, "r_x1": 258.028, "r_y1": 443.503, "r_x2": 258.028, "r_y2": 435.595, "r_x3": 197.591, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Financial Reports", "orig": "Financial Reports", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 260.489, "r_y0": 443.459, "r_x1": 273.781, "r_y1": 443.459, "r_x2": 273.781, "r_y2": 435.658, "r_x3": 260.489, "r_y3": 435.658, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 276.032, "r_y0": 443.503, "r_x1": 294.941, "r_y1": 443.503, "r_x2": 294.941, "r_y2": 435.595, "r_x3": 276.032, "r_y3": 435.595, "coord_origin": "TOPLEFT"}, "text": "Man-", "orig": "Man-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 454.462, "r_x1": 68.086, "r_y1": 454.462, "r_x2": 68.086, "r_y2": 446.554, "r_x3": 53.798, "r_y3": 446.554, "coord_origin": "TOPLEFT"}, "text": "uals", "orig": "uals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 68.297, "r_y0": 454.418, "r_x1": 294.046, "r_y1": 454.418, "r_x2": 294.046, "r_y2": 446.617, "r_x3": 68.297, "r_y3": 446.617, "coord_origin": "TOPLEFT"}, "text": ") contain a large amount of free-style layouts in order to obtain", "orig": ") contain a large amount of free-style layouts in order to obtain", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 465.377, "r_x1": 294.042, "r_y1": 465.377, "r_x2": 294.042, "r_y2": 457.576, "r_x3": 53.798, "r_y3": 457.576, "coord_origin": "TOPLEFT"}, "text": "maximum variability. In the other four categories, we boosted the", "orig": "maximum variability. In the other four categories, we boosted the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.574, "r_y0": 476.336, "r_x1": 294.049, "r_y1": 476.336, "r_x2": 294.049, "r_y2": 468.535, "r_x3": 53.574, "r_y3": 468.535, "coord_origin": "TOPLEFT"}, "text": "variability by mixing documents from independent providers, such", "orig": "variability by mixing documents from independent providers, such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 487.295, "r_x1": 294.047, "r_y1": 487.295, "r_x2": 294.047, "r_y2": 479.494, "r_x3": 53.798, "r_y3": 479.494, "coord_origin": "TOPLEFT"}, "text": "as different government websites or publishers. In Figure 2, we", "orig": "as different government websites or publishers. In Figure 2, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 498.254, "r_x1": 294.216, "r_y1": 498.254, "r_x2": 294.216, "r_y2": 490.453, "r_x3": 53.798, "r_y3": 490.453, "coord_origin": "TOPLEFT"}, "text": "show the document categories contained in DocLayNet with their", "orig": "show the document categories contained in DocLayNet with their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 509.213, "r_x1": 112.295, "r_y1": 509.213, "r_x2": 112.295, "r_y2": 501.412, "r_x3": 53.798, "r_y3": 501.412, "coord_origin": "TOPLEFT"}, "text": "respective sizes.", "orig": "respective sizes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals Scientific Articles , , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 512.371, "r": 295.562, "b": 607.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9877581000328064, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 520.172, "r_x1": 295.559, "r_y1": 520.172, "r_x2": 295.559, "r_y2": 512.371, "r_x3": 63.761, "r_y3": 512.371, "coord_origin": "TOPLEFT"}, "text": "We did not control the document selection with regard to lan-", "orig": "We did not control the document selection with regard to lan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 531.131, "r_x1": 294.047, "r_y1": 531.131, "r_x2": 294.047, "r_y2": 523.3299999999999, "r_x3": 53.798, "r_y3": 523.3299999999999, "coord_origin": "TOPLEFT"}, "text": "guage. The vast majority of documents contained in DocLayNet", "orig": "guage. The vast majority of documents contained in DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 542.09, "r_x1": 295.562, "r_y1": 542.09, "r_x2": 295.562, "r_y2": 534.289, "r_x3": 53.529, "r_y3": 534.289, "coord_origin": "TOPLEFT"}, "text": "(close to 95%) are published in English language. However, Do-", "orig": "(close to 95%) are published in English language. However, Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 553.049, "r_x1": 294.041, "r_y1": 553.049, "r_x2": 294.041, "r_y2": 545.248, "r_x3": 53.798, "r_y3": 545.248, "coord_origin": "TOPLEFT"}, "text": "cLayNet also contains a number of documents in other languages", "orig": "cLayNet also contains a number of documents in other languages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 564.0070000000001, "r_x1": 294.047, "r_y1": 564.0070000000001, "r_x2": 294.047, "r_y2": 556.206, "r_x3": 53.798, "r_y3": 556.206, "coord_origin": "TOPLEFT"}, "text": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "orig": "such as German (2.5%), French (1.0%) and Japanese (1.0%). While", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.966, "r_x1": 294.042, "r_y1": 574.966, "r_x2": 294.042, "r_y2": 567.165, "r_x3": 53.798, "r_y3": 567.165, "coord_origin": "TOPLEFT"}, "text": "the document language has negligible impact on the performance", "orig": "the document language has negligible impact on the performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.925, "r_x1": 295.557, "r_y1": 585.925, "r_x2": 295.557, "r_y2": 578.124, "r_x3": 53.798, "r_y3": 578.124, "coord_origin": "TOPLEFT"}, "text": "of computer vision methods such as object detection and segmenta-", "orig": "of computer vision methods such as object detection and segmenta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 596.884, "r_x1": 294.045, "r_y1": 596.884, "r_x2": 294.045, "r_y2": 589.083, "r_x3": 53.798, "r_y3": 589.083, "coord_origin": "TOPLEFT"}, "text": "tion models, it might prove challenging for layout analysis methods", "orig": "tion models, it might prove challenging for layout analysis methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 607.843, "r_x1": 164.399, "r_y1": 607.843, "r_x2": 164.399, "r_y2": 600.042, "r_x3": 53.467, "r_y3": 600.042, "coord_origin": "TOPLEFT"}, "text": "which exploit textual features.", "orig": "which exploit textual features.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 611.001, "r": 295.564, "b": 684.5550000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9880735278129578, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 618.802, "r_x1": 295.564, "r_y1": 618.802, "r_x2": 295.564, "r_y2": 611.001, "r_x3": 63.761, "r_y3": 611.001, "coord_origin": "TOPLEFT"}, "text": "To ensure that future benchmarks in the document-layout analy-", "orig": "To ensure that future benchmarks in the document-layout analy-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 629.761, "r_x1": 294.045, "r_y1": 629.761, "r_x2": 294.045, "r_y2": 621.96, "r_x3": 53.798, "r_y3": 621.96, "coord_origin": "TOPLEFT"}, "text": "sis community can be easily compared, we have split up DocLayNet", "orig": "sis community can be easily compared, we have split up DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 640.72, "r_x1": 294.045, "r_y1": 640.72, "r_x2": 294.045, "r_y2": 632.919, "r_x3": 53.798, "r_y3": 632.919, "coord_origin": "TOPLEFT"}, "text": "into pre-defined train-, test- and validation-sets. In this way, we can", "orig": "into pre-defined train-, test- and validation-sets. In this way, we can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 651.679, "r_x1": 294.043, "r_y1": 651.679, "r_x2": 294.043, "r_y2": 643.8779999999999, "r_x3": 53.798, "r_y3": 643.8779999999999, "coord_origin": "TOPLEFT"}, "text": "avoid spurious variations in the evaluation scores due to random", "orig": "avoid spurious variations in the evaluation scores due to random", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 662.638, "r_x1": 294.047, "r_y1": 662.638, "r_x2": 294.047, "r_y2": 654.837, "r_x3": 53.798, "r_y3": 654.837, "coord_origin": "TOPLEFT"}, "text": "splitting in train-, test- and validation-sets. We also ensured that", "orig": "splitting in train-, test- and validation-sets. We also ensured that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 673.596, "r_x1": 294.043, "r_y1": 673.596, "r_x2": 294.043, "r_y2": 665.795, "r_x3": 53.798, "r_y3": 665.795, "coord_origin": "TOPLEFT"}, "text": "less frequent labels are represented in train and test sets in equal", "orig": "less frequent labels are represented in train and test sets in equal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 684.5550000000001, "r_x1": 98.917, "r_y1": 684.5550000000001, "r_x2": 98.917, "r_y2": 676.754, "r_x3": 53.798, "r_y3": 676.754, "coord_origin": "TOPLEFT"}, "text": "proportions.", "orig": "proportions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions."}, {"label": "footnote", "id": 12, "page_no": 2, "cluster": {"id": 12, "label": "footnote", "bbox": {"l": 53.798, "t": 700.327, "r": 195.79, "b": 708.059, "coord_origin": "TOPLEFT"}, "confidence": 0.9048352241516113, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.094, "r_x1": 56.346, "r_y1": 705.094, "r_x2": 56.346, "r_y2": 700.327, "r_x3": 53.798, "r_y3": 700.327, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 56.844, "r_y0": 708.059, "r_x1": 195.79, "r_y1": 708.059, "r_x2": 195.79, "r_y2": 701.991, "r_x3": 56.844, "r_y3": 701.991, "coord_origin": "TOPLEFT"}, "text": "e.g. AAPL from https://www.annualreports.com/", "orig": "e.g. AAPL from https://www.annualreports.com/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 e.g. AAPL from https://www.annualreports.com/"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 87.79399999999998, "r": 559.192, "b": 161.34799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.986920952796936, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 95.59500000000003, "r_x1": 558.201, "r_y1": 95.59500000000003, "r_x2": 558.201, "r_y2": 87.79399999999998, "r_x3": 327.918, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "Table 1 shows the overall frequency and distribution of the labels", "orig": "Table 1 shows the overall frequency and distribution of the labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.55399999999997, "r_x1": 558.2, "r_y1": 106.55399999999997, "r_x2": 558.2, "r_y2": 98.75300000000004, "r_x3": 317.955, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "among the different sets. Importantly, we ensure that subsets are", "orig": "among the different sets. Importantly, we ensure that subsets are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.51300000000003, "r_x1": 558.201, "r_y1": 117.51300000000003, "r_x2": 558.201, "r_y2": 109.71199999999999, "r_x3": 317.955, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "only split on full-document boundaries. This avoids that pages of", "orig": "only split on full-document boundaries. This avoids that pages of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 128.47199999999998, "r_x1": 559.192, "r_y1": 128.47199999999998, "r_x2": 559.192, "r_y2": 120.67100000000005, "r_x3": 317.955, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "the same document are spread over train, test and validation set,", "orig": "the same document are spread over train, test and validation set,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 139.42999999999995, "r_x1": 558.203, "r_y1": 139.42999999999995, "r_x2": 558.203, "r_y2": 131.62900000000002, "r_x3": 317.623, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "which can give an undesired evaluation advantage to models and", "orig": "which can give an undesired evaluation advantage to models and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 150.389, "r_x1": 558.529, "r_y1": 150.389, "r_x2": 558.529, "r_y2": 142.58799999999997, "r_x3": 317.955, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "lead to overestimation of their prediction accuracy. We will show", "orig": "lead to overestimation of their prediction accuracy. We will show", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 161.34799999999996, "r_x1": 461.642, "r_y1": 161.34799999999996, "r_x2": 461.642, "r_y2": 153.54700000000003, "r_x3": 317.955, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "the impact of this decision in Section 5.", "orig": "the impact of this decision in Section 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5."}, {"label": "text", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.955, "t": 164.50599999999997, "r": 558.438, "b": 270.937, "coord_origin": "TOPLEFT"}, "confidence": 0.9860605001449585, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 172.30700000000002, "r_x1": 558.438, "r_y1": 172.30700000000002, "r_x2": 558.438, "r_y2": 164.50599999999997, "r_x3": 327.918, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "In order to accommodate the different types of models currently", "orig": "In order to accommodate the different types of models currently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 183.26599999999996, "r_x1": 516.822, "r_y1": 183.26599999999996, "r_x2": 516.822, "r_y2": 175.46500000000003, "r_x3": 317.955, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "in use by the community, we provide DocLayNet in an", "orig": "in use by the community, we provide DocLayNet in an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 519.075, "r_y0": 183.30999999999995, "r_x1": 558.201, "r_y1": 183.30999999999995, "r_x2": 558.201, "r_y2": 175.40200000000004, "r_x3": 519.075, "r_y3": 175.40200000000004, "coord_origin": "TOPLEFT"}, "text": "augmented", "orig": "augmented", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 194.22500000000002, "r_x1": 558.203, "r_y1": 194.22500000000002, "r_x2": 558.203, "r_y2": 186.42399999999998, "r_x3": 317.955, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "COCO format [16]. This entails the standard COCO ground-truth", "orig": "COCO format [16]. This entails the standard COCO ground-truth", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 205.18399999999997, "r_x1": 329.579, "r_y1": 205.18399999999997, "r_x2": 329.579, "r_y2": 197.38300000000004, "r_x3": 317.955, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "file", "orig": "file", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 332.753, "r_y0": 205.18399999999997, "r_x1": 558.204, "r_y1": 205.18399999999997, "r_x2": 558.204, "r_y2": 197.38300000000004, "r_x3": 332.753, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "(in JSON format) with the associated page images (in PNG", "orig": "(in JSON format) with the associated page images (in PNG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 216.14300000000003, "r_x1": 364.288, "r_y1": 216.14300000000003, "r_x2": 364.288, "r_y2": 208.34199999999998, "r_x3": 317.955, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "format, 1025", "orig": "format, 1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 364.287, "r_y0": 215.55999999999995, "r_x1": 369.99, "r_y1": 215.55999999999995, "r_x2": 369.99, "r_y2": 207.85799999999995, "r_x3": 364.287, "r_y3": 207.85799999999995, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.99, "r_y0": 216.14300000000003, "r_x1": 558.205, "r_y1": 216.14300000000003, "r_x2": 558.205, "r_y2": 208.34199999999998, "r_x3": 369.99, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "1025 pixels). Furthermore, custom fields have been", "orig": "1025 pixels). Furthermore, custom fields have been", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 227.10199999999998, "r_x1": 558.202, "r_y1": 227.10199999999998, "r_x2": 558.202, "r_y2": 219.30100000000004, "r_x3": 317.955, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "added to each COCO record to specify document category, original", "orig": "added to each COCO record to specify document category, original", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 238.06100000000004, "r_x1": 558.203, "r_y1": 238.06100000000004, "r_x2": 558.203, "r_y2": 230.26, "r_x3": 317.955, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "document filename and page number. In addition, we also provide", "orig": "document filename and page number. In addition, we also provide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 249.019, "r_x1": 558.204, "r_y1": 249.019, "r_x2": 558.204, "r_y2": 241.21799999999996, "r_x3": 317.955, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "the original PDF pages, as well as sidecar files containing parsed", "orig": "the original PDF pages, as well as sidecar files containing parsed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 259.97799999999995, "r_x1": 558.202, "r_y1": 259.97799999999995, "r_x2": 558.202, "r_y2": 252.17700000000002, "r_x3": 317.955, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "PDF text and text-cell coordinates (in JSON). All additional files are", "orig": "PDF text and text-cell coordinates (in JSON). All additional files are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 270.937, "r_x1": 550.364, "r_y1": 270.937, "r_x2": 550.364, "r_y2": 263.13599999999997, "r_x3": 317.955, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "linked to the primary page images by their matching filenames.", "orig": "linked to the primary page images by their matching filenames.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 \u00d7 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames."}, {"label": "text", "id": 7, "page_no": 2, "cluster": {"id": 7, "label": "text", "bbox": {"l": 316.942, "t": 274.095, "r": 559.722, "b": 588.746, "coord_origin": "TOPLEFT"}, "confidence": 0.9853296875953674, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 281.896, "r_x1": 559.185, "r_y1": 281.896, "r_x2": 559.185, "r_y2": 274.095, "r_x3": 327.918, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "Despite being cost-intense and far less scalable than automation,", "orig": "Despite being cost-intense and far less scalable than automation,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 292.855, "r_x1": 559.713, "r_y1": 292.855, "r_x2": 559.713, "r_y2": 285.054, "r_x3": 317.955, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "human annotation has several benefits over automated ground-", "orig": "human annotation has several benefits over automated ground-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 303.814, "r_x1": 558.204, "r_y1": 303.814, "r_x2": 558.204, "r_y2": 296.013, "r_x3": 317.955, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "truth generation. The first and most obvious reason to leverage", "orig": "truth generation. The first and most obvious reason to leverage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 314.773, "r_x1": 559.713, "r_y1": 314.773, "r_x2": 559.713, "r_y2": 306.972, "r_x3": 317.955, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "human annotations is the freedom to annotate any type of doc-", "orig": "human annotations is the freedom to annotate any type of doc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 325.732, "r_x1": 558.414, "r_y1": 325.732, "r_x2": 558.414, "r_y2": 317.931, "r_x3": 317.955, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "ument without requiring a programmatic source. For most PDF", "orig": "ument without requiring a programmatic source. For most PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 336.691, "r_x1": 559.715, "r_y1": 336.691, "r_x2": 559.715, "r_y2": 328.89, "r_x3": 317.955, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "documents, the original source document is not available. The lat-", "orig": "documents, the original source document is not available. The lat-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 347.65, "r_x1": 558.369, "r_y1": 347.65, "r_x2": 558.369, "r_y2": 339.849, "r_x3": 317.955, "r_y3": 339.849, "coord_origin": "TOPLEFT"}, "text": "ter is not a hard constraint with human annotation, but it is for", "orig": "ter is not a hard constraint with human annotation, but it is for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 358.609, "r_x1": 558.201, "r_y1": 358.609, "r_x2": 558.201, "r_y2": 350.808, "r_x3": 317.955, "r_y3": 350.808, "coord_origin": "TOPLEFT"}, "text": "automated methods. A second reason to use human annotations is", "orig": "automated methods. A second reason to use human annotations is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 369.567, "r_x1": 558.202, "r_y1": 369.567, "r_x2": 558.202, "r_y2": 361.766, "r_x3": 317.955, "r_y3": 361.766, "coord_origin": "TOPLEFT"}, "text": "that the latter usually provide a more natural interpretation of the", "orig": "that the latter usually provide a more natural interpretation of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 380.526, "r_x1": 559.714, "r_y1": 380.526, "r_x2": 559.714, "r_y2": 372.725, "r_x3": 317.955, "r_y3": 372.725, "coord_origin": "TOPLEFT"}, "text": "page layout. The human-interpreted layout can significantly devi-", "orig": "page layout. The human-interpreted layout can significantly devi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 391.485, "r_x1": 559.186, "r_y1": 391.485, "r_x2": 559.186, "r_y2": 383.684, "r_x3": 317.955, "r_y3": 383.684, "coord_origin": "TOPLEFT"}, "text": "ate from the programmatic layout used in typesetting. For example,", "orig": "ate from the programmatic layout used in typesetting. For example,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 316.942, "r_y0": 402.444, "r_x1": 558.201, "r_y1": 402.444, "r_x2": 558.201, "r_y2": 394.643, "r_x3": 316.942, "r_y3": 394.643, "coord_origin": "TOPLEFT"}, "text": "'invisible' tables might be used solely for aligning text paragraphs", "orig": "'invisible' tables might be used solely for aligning text paragraphs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 413.403, "r_x1": 559.713, "r_y1": 413.403, "r_x2": 559.713, "r_y2": 405.602, "r_x3": 317.955, "r_y3": 405.602, "coord_origin": "TOPLEFT"}, "text": "on columns. Such typesetting tricks might be interpreted by au-", "orig": "on columns. Such typesetting tricks might be interpreted by au-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 424.362, "r_x1": 558.2, "r_y1": 424.362, "r_x2": 558.2, "r_y2": 416.561, "r_x3": 317.955, "r_y3": 416.561, "coord_origin": "TOPLEFT"}, "text": "tomated methods incorrectly as an actual table, while the human", "orig": "tomated methods incorrectly as an actual table, while the human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 435.321, "r_x1": 464.506, "r_y1": 435.321, "r_x2": 464.506, "r_y2": 427.52, "r_x3": 317.955, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "annotation will interpret it correctly as", "orig": "annotation will interpret it correctly as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 466.982, "r_y0": 435.365, "r_x1": 482.146, "r_y1": 435.365, "r_x2": 482.146, "r_y2": 427.457, "r_x3": 466.982, "r_y3": 427.457, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 485.132, "r_y0": 435.321, "r_x1": 558.197, "r_y1": 435.321, "r_x2": 558.197, "r_y2": 427.52, "r_x3": 485.132, "r_y3": 427.52, "coord_origin": "TOPLEFT"}, "text": "or other styles. The", "orig": "or other styles. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 446.28, "r_x1": 558.202, "r_y1": 446.28, "r_x2": 558.202, "r_y2": 438.479, "r_x3": 317.955, "r_y3": 438.479, "coord_origin": "TOPLEFT"}, "text": "same applies to multi-line text elements, when authors decided to", "orig": "same applies to multi-line text elements, when authors decided to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.239, "r_x1": 558.515, "r_y1": 457.239, "r_x2": 558.515, "r_y2": 449.438, "r_x3": 317.955, "r_y3": 449.438, "coord_origin": "TOPLEFT"}, "text": "space them as 'invisible' list elements without bullet symbols. A", "orig": "space them as 'invisible' list elements without bullet symbols. A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.198, "r_x1": 558.199, "r_y1": 468.198, "r_x2": 558.199, "r_y2": 460.397, "r_x3": 317.955, "r_y3": 460.397, "coord_origin": "TOPLEFT"}, "text": "third reason to gather ground-truth through human annotation is", "orig": "third reason to gather ground-truth through human annotation is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 479.156, "r_x1": 559.582, "r_y1": 479.156, "r_x2": 559.582, "r_y2": 471.355, "r_x3": 317.955, "r_y3": 471.355, "coord_origin": "TOPLEFT"}, "text": "to estimate a 'natural' upper bound on the segmentation accuracy.", "orig": "to estimate a 'natural' upper bound on the segmentation accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 490.115, "r_x1": 558.416, "r_y1": 490.115, "r_x2": 558.416, "r_y2": 482.314, "r_x3": 317.641, "r_y3": 482.314, "coord_origin": "TOPLEFT"}, "text": "As we will show in Section 4, certain documents featuring complex", "orig": "As we will show in Section 4, certain documents featuring complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 501.074, "r_x1": 559.722, "r_y1": 501.074, "r_x2": 559.722, "r_y2": 493.273, "r_x3": 317.955, "r_y3": 493.273, "coord_origin": "TOPLEFT"}, "text": "layouts can have different but equally acceptable layout interpre-", "orig": "layouts can have different but equally acceptable layout interpre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 512.033, "r_x1": 558.199, "r_y1": 512.033, "r_x2": 558.199, "r_y2": 504.232, "r_x3": 317.955, "r_y3": 504.232, "coord_origin": "TOPLEFT"}, "text": "tations. This natural upper bound for segmentation accuracy can", "orig": "tations. This natural upper bound for segmentation accuracy can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 522.992, "r_x1": 558.206, "r_y1": 522.992, "r_x2": 558.206, "r_y2": 515.191, "r_x3": 317.955, "r_y3": 515.191, "coord_origin": "TOPLEFT"}, "text": "be found by annotating the same pages multiple times by different", "orig": "be found by annotating the same pages multiple times by different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 533.951, "r_x1": 559.717, "r_y1": 533.951, "r_x2": 559.717, "r_y2": 526.15, "r_x3": 317.955, "r_y3": 526.15, "coord_origin": "TOPLEFT"}, "text": "people and evaluating the inter-annotator agreement. Such a base-", "orig": "people and evaluating the inter-annotator agreement. Such a base-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 544.91, "r_x1": 558.204, "r_y1": 544.91, "r_x2": 558.204, "r_y2": 537.109, "r_x3": 317.955, "r_y3": 537.109, "coord_origin": "TOPLEFT"}, "text": "line consistency evaluation is very useful to define expectations", "orig": "line consistency evaluation is very useful to define expectations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 555.869, "r_x1": 558.201, "r_y1": 555.869, "r_x2": 558.201, "r_y2": 548.068, "r_x3": 317.955, "r_y3": 548.068, "coord_origin": "TOPLEFT"}, "text": "for a good target accuracy in trained deep neural network models", "orig": "for a good target accuracy in trained deep neural network models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.828, "r_x1": 558.201, "r_y1": 566.828, "r_x2": 558.201, "r_y2": 559.027, "r_x3": 317.955, "r_y3": 559.027, "coord_origin": "TOPLEFT"}, "text": "and avoid overfitting (see Table 1). On the flip side, achieving high", "orig": "and avoid overfitting (see Table 1). On the flip side, achieving high", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.787, "r_x1": 558.204, "r_y1": 577.787, "r_x2": 558.204, "r_y2": 569.986, "r_x3": 317.955, "r_y3": 569.986, "coord_origin": "TOPLEFT"}, "text": "annotation consistency proved to be a key challenge in human", "orig": "annotation consistency proved to be a key challenge in human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 588.746, "r_x1": 457.625, "r_y1": 588.746, "r_x2": 457.625, "r_y2": 580.9449999999999, "r_x3": 317.955, "r_y3": 580.9449999999999, "coord_origin": "TOPLEFT"}, "text": "annotation, as we outline in Section 4.", "orig": "annotation, as we outline in Section 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, 'invisible' tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as 'invisible' list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a 'natural' upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4."}, {"label": "section_header", "id": 10, "page_no": 2, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 317.955, "t": 607.788, "r": 470.213, "b": 616.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9422592520713806, "cells": [{"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.855, "r_x1": 323.562, "r_y1": 616.855, "r_x2": 323.562, "r_y2": 607.788, "r_x3": 317.955, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 616.855, "r_x1": 470.213, "r_y1": 616.855, "r_x2": 470.213, "r_y2": 607.788, "r_x3": 334.471, "r_y3": 607.788, "coord_origin": "TOPLEFT"}, "text": "ANNOTATION CAMPAIGN", "orig": "ANNOTATION CAMPAIGN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4 ANNOTATION CAMPAIGN"}, {"label": "text", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "text", "bbox": {"l": 317.686, "t": 632.404, "r": 559.714, "b": 705.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9863831996917725, "cells": [{"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 640.205, "r_x1": 558.201, "r_y1": 640.205, "r_x2": 558.201, "r_y2": 632.404, "r_x3": 317.686, "r_y3": 632.404, "coord_origin": "TOPLEFT"}, "text": "The annotation campaign was carried out in four phases. In phase", "orig": "The annotation campaign was carried out in four phases. In phase", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.164, "r_x1": 559.585, "r_y1": 651.164, "r_x2": 559.585, "r_y2": 643.363, "r_x3": 317.955, "r_y3": 643.363, "coord_origin": "TOPLEFT"}, "text": "one, we identified and prepared the data sources for annotation.", "orig": "one, we identified and prepared the data sources for annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.123, "r_x1": 558.2, "r_y1": 662.123, "r_x2": 558.2, "r_y2": 654.322, "r_x3": 317.955, "r_y3": 654.322, "coord_origin": "TOPLEFT"}, "text": "In phase two, we determined the class labels and how annotations", "orig": "In phase two, we determined the class labels and how annotations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.082, "r_x1": 559.714, "r_y1": 673.082, "r_x2": 559.714, "r_y2": 665.281, "r_x3": 317.955, "r_y3": 665.281, "coord_origin": "TOPLEFT"}, "text": "should be done on the documents in order to obtain maximum con-", "orig": "should be done on the documents in order to obtain maximum con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.0409999999999, "r_x1": 558.202, "r_y1": 684.0409999999999, "r_x2": 558.202, "r_y2": 676.24, "r_x3": 317.955, "r_y3": 676.24, "coord_origin": "TOPLEFT"}, "text": "sistency. The latter was guided by a detailed requirement analysis", "orig": "sistency. The latter was guided by a detailed requirement analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 695.0, "r_x1": 559.714, "r_y1": 695.0, "r_x2": 559.714, "r_y2": 687.199, "r_x3": 317.955, "r_y3": 687.199, "coord_origin": "TOPLEFT"}, "text": "and exhaustive experiments. In phase three, we trained the annota-", "orig": "and exhaustive experiments. In phase three, we trained the annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.9590000000001, "r_x1": 559.186, "r_y1": 705.9590000000001, "r_x2": 559.186, "r_y2": 698.158, "r_x3": 317.955, "r_y3": 698.158, "coord_origin": "TOPLEFT"}, "text": "tion staff and performed exams for quality assurance. In phase four,", "orig": "tion staff and performed exams for quality assurance. In phase four,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,"}], "headers": [{"label": "page_header", "id": 13, "page_no": 2, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8919971585273743, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 2, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9104517102241516, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 104.13800000000003, "r_x1": 558.203, "r_y1": 104.13800000000003, "r_x2": 558.203, "r_y2": 96.68499999999995, "r_x3": 53.798, "r_y3": 96.68499999999995, "coord_origin": "TOPLEFT"}, "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 115.09699999999998, "r_x1": 469.848, "r_y1": 115.09699999999998, "r_x2": 469.848, "r_y2": 107.644, "r_x3": 53.798, "r_y3": 107.644, "coord_origin": "TOPLEFT"}, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, "r_y0": 148.45500000000004, "r_x1": 270.043, "r_y1": 148.45500000000004, "r_x2": 270.043, "r_y2": 140.654, "r_x3": 233.944, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "% of Total", "orig": "% of Total", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.05, "r_y0": 148.45500000000004, "r_x1": 407.372, "r_y1": 148.45500000000004, "r_x2": 407.372, "r_y2": 140.654, "r_x3": 329.05, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "triple inter-annotator", "orig": "triple inter-annotator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 411.604, "r_y0": 148.45500000000004, "r_x1": 483.398, "r_y1": 148.45500000000004, "r_x2": 483.398, "r_y2": 140.654, "r_x3": 411.604, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "mAP @ 0.5-0.95 (%)", "orig": "mAP @ 0.5-0.95 (%)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 104.825, "r_y0": 159.414, "r_x1": 141.713, "r_y1": 159.414, "r_x2": 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The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"r_x3": 317.955, "r_y3": 575.523, "coord_origin": "TOPLEFT"}, "text": "Critical factors that were considered for the choice of these class", "orig": "Critical factors that were considered for the choice of these class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 594.283, "r_x1": 558.431, "r_y1": 594.283, "r_x2": 558.431, "r_y2": 586.482, "r_x3": 317.955, "r_y3": 586.482, "coord_origin": "TOPLEFT"}, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 229, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 605.242, "r_x1": 558.369, "r_y1": 605.242, "r_x2": 558.369, "r_y2": 597.441, "r_x3": 317.955, "r_y3": 597.441, "coord_origin": "TOPLEFT"}, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 232, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 233, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. 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We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 239, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.954, "r_x1": 447.652, "r_y1": 681.954, "r_x2": 447.652, "r_y2": 674.153, "r_x3": 317.955, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 240, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.86, "r_y0": 681.998, "r_x1": 474.314, "r_y1": 681.998, "r_x2": 474.314, "r_y2": 674.09, "r_x3": 449.86, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 241, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.172, "r_y0": 681.954, "r_x1": 490.397, "r_y1": 681.954, "r_x2": 490.397, "r_y2": 674.153, "r_x3": 477.172, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 242, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.606, "r_y0": 681.998, "r_x1": 528.299, "r_y1": 681.998, "r_x2": 528.299, "r_y2": 674.09, "r_x3": 492.606, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 243, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.299, "r_y0": 681.954, "r_x1": 530.232, "r_y1": 681.954, "r_x2": 530.232, "r_y2": 674.153, "r_x3": 528.299, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 244, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.438, "r_y0": 681.954, "r_x1": 558.201, "r_y1": 681.954, "r_x2": 558.201, "r_y2": 674.153, "r_x3": 532.438, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "as seen", "orig": "as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 245, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 692.913, "r_x1": 558.204, "r_y1": 692.913, "r_x2": 558.204, "r_y2": 685.112, "r_x3": 317.955, "r_y3": 685.112, "coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 246, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 247, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "caption", "bbox": {"l": 53.502, "t": 85.726, "r": 558.49, "b": 115.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 104.13800000000003, "r_x1": 558.203, "r_y1": 104.13800000000003, "r_x2": 558.203, "r_y2": 96.68499999999995, "r_x3": 53.798, "r_y3": 96.68499999999995, "coord_origin": "TOPLEFT"}, "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 115.09699999999998, "r_x1": 469.848, "r_y1": 115.09699999999998, "r_x2": 469.848, "r_y2": 107.644, "r_x3": 53.798, "r_y3": 107.644, "coord_origin": "TOPLEFT"}, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, 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"r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 573.193, "r_x1": 295.649, "r_y1": 573.193, "r_x2": 295.649, "r_y2": 565.74, "r_x3": 53.798, "r_y3": 565.74, "coord_origin": "TOPLEFT"}, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 675.4, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 53.798, "t": 678.558, "r": 295.558, "b": 708.277, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 310.361, "r": 559.185, "b": 375.105, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 378.263, "r": 559.713, "b": 506.612, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 509.77, "r": 559.718, "b": 692.913, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"r_x3": 317.955, "r_y3": 575.523, "coord_origin": "TOPLEFT"}, "text": "Critical factors that were considered for the choice of these class", "orig": "Critical factors that were considered for the choice of these class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 594.283, "r_x1": 558.431, "r_y1": 594.283, "r_x2": 558.431, "r_y2": 586.482, "r_x3": 317.955, "r_y3": 586.482, "coord_origin": "TOPLEFT"}, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 229, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 605.242, "r_x1": 558.369, "r_y1": 605.242, "r_x2": 558.369, "r_y2": 597.441, "r_x3": 317.955, "r_y3": 597.441, "coord_origin": "TOPLEFT"}, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 232, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 233, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. 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"coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "footnote", "bbox": {"l": 317.955, "t": 701.024, "r": 369.246, "b": 708.756, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"index": 246, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 247, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, "r_y0": 148.45500000000004, "r_x1": 270.043, "r_y1": 148.45500000000004, "r_x2": 270.043, "r_y2": 140.654, "r_x3": 233.944, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "% of Total", "orig": "% of Total", 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August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.502, "t": 85.726, "r": 558.49, "b": 115.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 104.13800000000003, "r_x1": 558.203, "r_y1": 104.13800000000003, "r_x2": 558.203, "r_y2": 96.68499999999995, "r_x3": 53.798, "r_y3": 96.68499999999995, "coord_origin": "TOPLEFT"}, "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 115.09699999999998, "r_x1": 469.848, "r_y1": 115.09699999999998, "r_x2": 469.848, "r_y2": 107.644, "r_x3": 53.798, "r_y3": 107.644, "coord_origin": "TOPLEFT"}, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, "r_y0": 148.45500000000004, "r_x1": 270.043, "r_y1": 148.45500000000004, "r_x2": 270.043, "r_y2": 140.654, "r_x3": 233.944, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "% of Total", "orig": "% of Total", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.05, "r_y0": 148.45500000000004, "r_x1": 407.372, "r_y1": 148.45500000000004, "r_x2": 407.372, "r_y2": 140.654, "r_x3": 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"start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 9, "end_col_offset_idx": 10, "text": "86-91", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 457.8, "t": 283.917, "r": 477.509, "b": 291.718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 10, "end_col_offset_idx": 11, "text": "71-76", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 487.47, "t": 283.917, "r": 507.178, "b": 291.718, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 11, "end_col_offset_idx": 12, "text": "68-85", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "caption", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "caption", "bbox": {"l": 53.798, "t": 554.781, "r": 295.649, "b": 606.0699999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.810490071773529, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 573.193, "r_x1": 295.649, "r_y1": 573.193, "r_x2": 295.649, "r_y2": 565.74, "r_x3": 53.798, "r_y3": 565.74, "coord_origin": "TOPLEFT"}, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 675.4, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.798, "t": 678.558, "r": 295.558, "b": 708.277, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 310.361, "r": 559.185, "b": 375.105, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 378.263, "r": 559.713, "b": 506.612, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 509.77, "r": 559.718, "b": 692.913, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"r_x3": 317.955, "r_y3": 575.523, "coord_origin": "TOPLEFT"}, "text": "Critical factors that were considered for the choice of these class", "orig": "Critical factors that were considered for the choice of these class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 594.283, "r_x1": 558.431, "r_y1": 594.283, "r_x2": 558.431, "r_y2": 586.482, "r_x3": 317.955, "r_y3": 586.482, "coord_origin": "TOPLEFT"}, "text": "labels were (1) the overall occurrence of the label, (2) the specificity", "orig": "labels were (1) the overall occurrence of the label, (2) the specificity", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 229, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 605.242, "r_x1": 558.369, "r_y1": 605.242, "r_x2": 558.369, "r_y2": 597.441, "r_x3": 317.955, "r_y3": 597.441, "coord_origin": "TOPLEFT"}, "text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 232, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 233, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 234, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 660.037, "r_x1": 436.906, "r_y1": 660.037, "r_x2": 436.906, "r_y2": 652.236, "r_x3": 317.955, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 235, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.138, "r_y0": 660.081, "r_x1": 469.691, "r_y1": 660.081, "r_x2": 469.691, "r_y2": 652.173, "r_x3": 439.138, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 236, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.429, "r_y0": 660.037, "r_x1": 493.973, "r_y1": 660.037, "r_x2": 493.973, "r_y2": 652.236, "r_x3": 472.429, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 237, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 496.207, "r_y0": 660.081, "r_x1": 558.2, "r_y1": 660.081, "r_x2": 558.2, "r_y2": 652.173, "r_x3": 496.207, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 238, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 670.996, "r_x1": 558.206, "r_y1": 670.996, "r_x2": 558.206, "r_y2": 663.1949999999999, "r_x3": 317.955, "r_y3": 663.1949999999999, "coord_origin": "TOPLEFT"}, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 239, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.954, "r_x1": 447.652, "r_y1": 681.954, "r_x2": 447.652, "r_y2": 674.153, "r_x3": 317.955, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 240, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.86, "r_y0": 681.998, "r_x1": 474.314, "r_y1": 681.998, "r_x2": 474.314, "r_y2": 674.09, "r_x3": 449.86, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 241, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.172, "r_y0": 681.954, "r_x1": 490.397, "r_y1": 681.954, "r_x2": 490.397, "r_y2": 674.153, "r_x3": 477.172, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 242, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.606, "r_y0": 681.998, "r_x1": 528.299, "r_y1": 681.998, "r_x2": 528.299, "r_y2": 674.09, "r_x3": 492.606, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 243, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.299, "r_y0": 681.954, "r_x1": 530.232, "r_y1": 681.954, "r_x2": 530.232, "r_y2": 674.153, "r_x3": 528.299, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 244, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.438, "r_y0": 681.954, "r_x1": 558.201, "r_y1": 681.954, "r_x2": 558.201, "r_y2": 674.153, "r_x3": 532.438, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "as seen", "orig": "as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 245, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 692.913, "r_x1": 558.204, "r_y1": 692.913, "r_x2": 558.204, "r_y2": 685.112, "r_x3": 317.955, "r_y3": 685.112, "coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.955, "t": 701.024, "r": 369.246, "b": 708.756, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"index": 246, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 247, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 53.502, "t": 85.726, "r": 558.49, "b": 115.09699999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8906616568565369, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 93.17899999999997, "r_x1": 558.49, "r_y1": 93.17899999999997, "r_x2": 558.49, "r_y2": 85.726, "r_x3": 53.502, "r_y3": 85.726, "coord_origin": "TOPLEFT"}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "orig": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as %", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 104.13800000000003, "r_x1": 558.203, "r_y1": 104.13800000000003, "r_x2": 558.203, "r_y2": 96.68499999999995, "r_x3": 53.798, "r_y3": 96.68499999999995, "coord_origin": "TOPLEFT"}, "text": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "orig": "of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 115.09699999999998, "r_x1": 469.848, "r_y1": 115.09699999999998, "r_x2": 469.848, "r_y2": 107.644, "r_x3": 53.798, "r_y3": 107.644, "coord_origin": "TOPLEFT"}, "text": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "orig": "between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges."}, {"label": "table", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "table", "bbox": {"l": 98.93103790283203, "t": 137.47547912597656, "r": 512.579833984375, "b": 294.08148193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9860090613365173, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.944, "r_y0": 148.45500000000004, "r_x1": 270.043, "r_y1": 148.45500000000004, "r_x2": 270.043, "r_y2": 140.654, "r_x3": 233.944, "r_y3": 140.654, "coord_origin": "TOPLEFT"}, "text": "% of Total", "orig": "% of Total", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 329.05, "r_y0": 148.45500000000004, "r_x1": 407.372, "r_y1": 148.45500000000004, "r_x2": 407.372, "r_y2": 140.654, "r_x3": 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"TOPLEFT"}, "confidence": 0.810490071773529, "cells": [{"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 562.234, "r_x1": 295.649, "r_y1": 562.234, "r_x2": 295.649, "r_y2": 554.781, "r_x3": 53.798, "r_y3": 554.781, "coord_origin": "TOPLEFT"}, "text": "Figure 3: Corpus Conversion Service annotation user inter-", "orig": "Figure 3: Corpus Conversion Service annotation user inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 573.193, "r_x1": 295.649, "r_y1": 573.193, "r_x2": 295.649, "r_y2": 565.74, "r_x3": 53.798, "r_y3": 565.74, "coord_origin": "TOPLEFT"}, "text": "face. The PDF page is shown in the background, with over-", "orig": "face. The PDF page is shown in the background, with over-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 584.152, "r_x1": 294.044, "r_y1": 584.152, "r_x2": 294.044, "r_y2": 576.6990000000001, "r_x3": 53.798, "r_y3": 576.6990000000001, "coord_origin": "TOPLEFT"}, "text": "laid text-cells (in darker shades). The annotation boxes can", "orig": "laid text-cells (in darker shades). The annotation boxes can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.111, "r_x1": 294.044, "r_y1": 595.111, "r_x2": 294.044, "r_y2": 587.658, "r_x3": 53.798, "r_y3": 587.658, "coord_origin": "TOPLEFT"}, "text": "be drawn by dragging a rectangle over each segment with", "orig": "be drawn by dragging a rectangle over each segment with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.0699999999999, "r_x1": 252.789, "r_y1": 606.0699999999999, "r_x2": 252.789, "r_y2": 598.617, "r_x3": 53.798, "r_y3": 598.617, "coord_origin": "TOPLEFT"}, "text": "the respective label from the palette on the right.", "orig": "the respective label from the palette on the right.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.467, "t": 634.722, "r": 294.047, "b": 675.4, "coord_origin": "TOPLEFT"}, "confidence": 0.980393648147583, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 642.523, "r_x1": 294.047, "r_y1": 642.523, "r_x2": 294.047, "r_y2": 634.722, "r_x3": 53.467, "r_y3": 634.722, "coord_origin": "TOPLEFT"}, "text": "we distributed the annotation workload and performed continuous", "orig": "we distributed the annotation workload and performed continuous", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.482, "r_x1": 294.045, "r_y1": 653.482, "r_x2": 294.045, "r_y2": 645.681, "r_x3": 53.798, "r_y3": 645.681, "coord_origin": "TOPLEFT"}, "text": "quality controls. Phase one and two required a small team of experts", "orig": "quality controls. Phase one and two required a small team of experts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.441, "r_x1": 294.044, "r_y1": 664.441, "r_x2": 294.044, "r_y2": 656.64, "r_x3": 53.798, "r_y3": 656.64, "coord_origin": "TOPLEFT"}, "text": "only. For phases three and four, a group of 40 dedicated annotators", "orig": "only. For phases three and four, a group of 40 dedicated annotators", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 675.4, "r_x1": 170.586, "r_y1": 675.4, "r_x2": 170.586, "r_y2": 667.599, "r_x3": 53.467, "r_y3": 667.599, "coord_origin": "TOPLEFT"}, "text": "were assembled and supervised.", "orig": "were assembled and supervised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised."}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 53.798, "t": 678.558, "r": 295.558, "b": 708.277, "coord_origin": "TOPLEFT"}, "confidence": 0.9748398661613464, "cells": [{"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 686.235, "r_x1": 226.725, "r_y1": 686.235, "r_x2": 226.725, "r_y2": 678.782, "r_x3": 63.761, "r_y3": 678.782, "coord_origin": "TOPLEFT"}, "text": "Phase 1: Data selection and preparation.", "orig": "Phase 1: Data selection and preparation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.069, "r_y0": 686.359, "r_x1": 295.558, "r_y1": 686.359, "r_x2": 295.558, "r_y2": 678.558, "r_x3": 229.069, "r_y3": 678.558, "coord_origin": "TOPLEFT"}, "text": "Our inclusion cri-", "orig": "Our inclusion cri-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.318, "r_x1": 294.045, "r_y1": 697.318, "r_x2": 294.045, "r_y2": 689.517, "r_x3": 53.798, "r_y3": 689.517, "coord_origin": "TOPLEFT"}, "text": "teria for documents were described in Section 3. A large effort went", "orig": "teria for documents were described in Section 3. A large effort went", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.277, "r_x1": 294.046, "r_y1": 708.277, "r_x2": 294.046, "r_y2": 700.476, "r_x3": 53.798, "r_y3": 700.476, "coord_origin": "TOPLEFT"}, "text": "into ensuring that all documents are free to use. The data sources", "orig": "into ensuring that all documents are free to use. The data sources", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources"}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 317.955, "t": 310.361, "r": 559.185, "b": 375.105, "coord_origin": "TOPLEFT"}, "confidence": 0.9818483591079712, "cells": [{"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.311, "r_x1": 481.034, "r_y1": 320.311, "r_x2": 481.034, "r_y2": 312.51, "r_x3": 317.955, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "include publication repositories such as arXiv", "orig": "include publication repositories such as arXiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 481.037, "r_y0": 316.689, "r_x1": 484.419, "r_y1": 316.689, "r_x2": 484.419, "r_y2": 310.361, "r_x3": 481.037, "r_y3": 310.361, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.917, "r_y0": 320.311, "r_x1": 486.85, "r_y1": 320.311, "r_x2": 486.85, "r_y2": 312.51, "r_x3": 484.917, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 488.924, "r_y0": 320.311, "r_x1": 559.185, "r_y1": 320.311, "r_x2": 559.185, "r_y2": 312.51, "r_x3": 488.924, "r_y3": 312.51, "coord_origin": "TOPLEFT"}, "text": "government offices,", "orig": "government offices,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.269, "r_x1": 558.199, "r_y1": 331.269, "r_x2": 558.199, "r_y2": 323.468, "r_x3": 317.955, "r_y3": 323.468, "coord_origin": "TOPLEFT"}, "text": "company websites as well as data directory services for financial", "orig": "company websites as well as data directory services for financial", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.228, "r_x1": 558.37, "r_y1": 342.228, "r_x2": 558.37, "r_y2": 334.427, "r_x3": 317.955, "r_y3": 334.427, "coord_origin": "TOPLEFT"}, "text": "reports and patents. Scanned documents were excluded wherever", "orig": "reports and patents. Scanned documents were excluded wherever", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 353.187, "r_x1": 558.204, "r_y1": 353.187, "r_x2": 558.204, "r_y2": 345.386, "r_x3": 317.955, "r_y3": 345.386, "coord_origin": "TOPLEFT"}, "text": "possible because they can be rotated or skewed. This would not", "orig": "possible because they can be rotated or skewed. This would not", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 364.146, "r_x1": 558.203, "r_y1": 364.146, "r_x2": 558.203, "r_y2": 356.345, "r_x3": 317.955, "r_y3": 356.345, "coord_origin": "TOPLEFT"}, "text": "allow us to perform annotation with rectangular bounding-boxes", "orig": "allow us to perform annotation with rectangular bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 375.105, "r_x1": 496.718, "r_y1": 375.105, "r_x2": 496.718, "r_y2": 367.304, "r_x3": 317.955, "r_y3": 367.304, "coord_origin": "TOPLEFT"}, "text": "and therefore complicate the annotation process.", "orig": "and therefore complicate the annotation process.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 317.955, "t": 378.263, "r": 559.713, "b": 506.612, "coord_origin": "TOPLEFT"}, "confidence": 0.9866719841957092, "cells": [{"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 386.064, "r_x1": 558.206, "r_y1": 386.064, "r_x2": 558.206, "r_y2": 378.263, "r_x3": 327.918, "r_y3": 378.263, "coord_origin": "TOPLEFT"}, "text": "Preparation work included uploading and parsing the sourced", "orig": "Preparation work included uploading and parsing the sourced", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 397.023, "r_x1": 558.202, "r_y1": 397.023, "r_x2": 558.202, "r_y2": 389.222, "r_x3": 317.955, "r_y3": 389.222, "coord_origin": "TOPLEFT"}, "text": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "orig": "PDF documents in the Corpus Conversion Service (CCS) [22], a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 407.982, "r_x1": 558.202, "r_y1": 407.982, "r_x2": 558.202, "r_y2": 400.181, "r_x3": 317.955, "r_y3": 400.181, "coord_origin": "TOPLEFT"}, "text": "cloud-native platform which provides a visual annotation interface", "orig": "cloud-native platform which provides a visual annotation interface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 418.941, "r_x1": 559.713, "r_y1": 418.941, "r_x2": 559.713, "r_y2": 411.14, "r_x3": 317.955, "r_y3": 411.14, "coord_origin": "TOPLEFT"}, "text": "and allows for dataset inspection and analysis. The annotation in-", "orig": "and allows for dataset inspection and analysis. The annotation in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 429.9, "r_x1": 558.201, "r_y1": 429.9, "r_x2": 558.201, "r_y2": 422.099, "r_x3": 317.955, "r_y3": 422.099, "coord_origin": "TOPLEFT"}, "text": "terface of CCS is shown in Figure 3. The desired balance of pages", "orig": "terface of CCS is shown in Figure 3. The desired balance of pages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 440.859, "r_x1": 559.713, "r_y1": 440.859, "r_x2": 559.713, "r_y2": 433.058, "r_x3": 317.955, "r_y3": 433.058, "coord_origin": "TOPLEFT"}, "text": "between the different document categories was achieved by se-", "orig": "between the different document categories was achieved by se-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 451.817, "r_x1": 558.369, "r_y1": 451.817, "r_x2": 558.369, "r_y2": 444.016, "r_x3": 317.955, "r_y3": 444.016, "coord_origin": "TOPLEFT"}, "text": "lective subsampling of pages with certain desired properties. For", "orig": "lective subsampling of pages with certain desired properties. For", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 462.776, "r_x1": 558.204, "r_y1": 462.776, "r_x2": 558.204, "r_y2": 454.975, "r_x3": 317.955, "r_y3": 454.975, "coord_origin": "TOPLEFT"}, "text": "example, we made sure to include the title page of each document", "orig": "example, we made sure to include the title page of each document", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 473.735, "r_x1": 558.369, "r_y1": 473.735, "r_x2": 558.369, "r_y2": 465.934, "r_x3": 317.955, "r_y3": 465.934, "coord_origin": "TOPLEFT"}, "text": "and bias the remaining page selection to those with figures or", "orig": "and bias the remaining page selection to those with figures or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 484.694, "r_x1": 558.204, "r_y1": 484.694, "r_x2": 558.204, "r_y2": 476.893, "r_x3": 317.955, "r_y3": 476.893, "coord_origin": "TOPLEFT"}, "text": "tables. The latter was achieved by leveraging pre-trained object", "orig": "tables. The latter was achieved by leveraging pre-trained object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 495.653, "r_x1": 558.531, "r_y1": 495.653, "r_x2": 558.531, "r_y2": 487.852, "r_x3": 317.955, "r_y3": 487.852, "coord_origin": "TOPLEFT"}, "text": "detection models from PubLayNet, which helped us estimate how", "orig": "detection models from PubLayNet, which helped us estimate how", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.612, "r_x1": 488.469, "r_y1": 506.612, "r_x2": 488.469, "r_y2": 498.811, "r_x3": 317.955, "r_y3": 498.811, "coord_origin": "TOPLEFT"}, "text": "many figures and tables a given page contains.", "orig": "many figures and tables a given page contains.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains."}, {"label": "text", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 509.77, "r": 559.718, "b": 692.913, "coord_origin": "TOPLEFT"}, "confidence": 0.9847556948661804, "cells": [{"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 517.447, "r_x1": 482.418, "r_y1": 517.447, "r_x2": 482.418, "r_y2": 509.994, "r_x3": 327.918, "r_y3": 509.994, "coord_origin": "TOPLEFT"}, "text": "Phase 2: Label selection and guideline.", "orig": "Phase 2: Label selection and guideline.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.474, "r_y0": 517.571, "r_x1": 559.718, "r_y1": 517.571, "r_x2": 559.718, "r_y2": 509.77, "r_x3": 484.474, "r_y3": 509.77, "coord_origin": "TOPLEFT"}, "text": "We reviewed the col-", "orig": "We reviewed the col-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 528.53, "r_x1": 559.716, "r_y1": 528.53, "r_x2": 559.716, "r_y2": 520.729, "r_x3": 317.955, "r_y3": 520.729, "coord_origin": "TOPLEFT"}, "text": "lected documents and identified the most common structural fea-", "orig": "lected documents and identified the most common structural fea-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 539.489, "r_x1": 558.202, "r_y1": 539.489, "r_x2": 558.202, "r_y2": 531.688, "r_x3": 317.955, "r_y3": 531.688, "coord_origin": "TOPLEFT"}, "text": "tures they exhibit. This was achieved by identifying recurrent layout", "orig": "tures they exhibit. This was achieved by identifying recurrent layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 550.448, "r_x1": 559.585, "r_y1": 550.448, "r_x2": 559.585, "r_y2": 542.6469999999999, "r_x3": 317.955, "r_y3": 542.6469999999999, "coord_origin": "TOPLEFT"}, "text": "elements and lead us to the definition of 11 distinct class labels.", "orig": "elements and lead us to the definition of 11 distinct class labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 561.406, "r_x1": 404.142, "r_y1": 561.406, "r_x2": 404.142, "r_y2": 553.605, "r_x3": 317.686, "r_y3": 553.605, "coord_origin": "TOPLEFT"}, "text": "These 11 class labels are", "orig": "These 11 class labels are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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"text": "of the label, (3) recognisability on a single page (i.e. no need for", "orig": "of the label, (3) recognisability on a single page (i.e. no need for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 616.201, "r_x1": 558.201, "r_y1": 616.201, "r_x2": 558.201, "r_y2": 608.4, "r_x3": 317.955, "r_y3": 608.4, "coord_origin": "TOPLEFT"}, "text": "context from previous or next page) and (4) overall coverage of the", "orig": "context from previous or next page) and (4) overall coverage of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 627.16, "r_x1": 559.187, "r_y1": 627.16, "r_x2": 559.187, "r_y2": 619.359, "r_x3": 317.955, "r_y3": 619.359, "coord_origin": "TOPLEFT"}, "text": "page. Specificity ensures that the choice of label is not ambiguous,", "orig": "page. Specificity ensures that the choice of label is not ambiguous,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 232, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 638.119, "r_x1": 558.201, "r_y1": 638.119, "r_x2": 558.201, "r_y2": 630.318, "r_x3": 317.623, "r_y3": 630.318, "coord_origin": "TOPLEFT"}, "text": "while coverage ensures that all meaningful items on a page can", "orig": "while coverage ensures that all meaningful items on a page can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 233, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 649.078, "r_x1": 558.202, "r_y1": 649.078, "r_x2": 558.202, "r_y2": 641.277, "r_x3": 317.955, "r_y3": 641.277, "coord_origin": "TOPLEFT"}, "text": "be annotated. We refrained from class labels that are very specific", "orig": "be annotated. We refrained from class labels that are very specific", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 234, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 660.037, "r_x1": 436.906, "r_y1": 660.037, "r_x2": 436.906, "r_y2": 652.236, "r_x3": 317.955, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "to a document category, such as", "orig": "to a document category, such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 235, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 439.138, "r_y0": 660.081, "r_x1": 469.691, "r_y1": 660.081, "r_x2": 469.691, "r_y2": 652.173, "r_x3": 439.138, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Abstract", "orig": "Abstract", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 236, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 472.429, "r_y0": 660.037, "r_x1": 493.973, "r_y1": 660.037, "r_x2": 493.973, "r_y2": 652.236, "r_x3": 472.429, "r_y3": 652.236, "coord_origin": "TOPLEFT"}, "text": "in the", "orig": "in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 237, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 496.207, "r_y0": 660.081, "r_x1": 558.2, "r_y1": 660.081, "r_x2": 558.2, "r_y2": 652.173, "r_x3": 496.207, "r_y3": 652.173, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 238, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 670.996, "r_x1": 558.206, "r_y1": 670.996, "r_x2": 558.206, "r_y2": 663.1949999999999, "r_x3": 317.955, "r_y3": 663.1949999999999, "coord_origin": "TOPLEFT"}, "text": "category. We also avoided class labels that are tightly linked to the", "orig": "category. We also avoided class labels that are tightly linked to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 239, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.954, "r_x1": 447.652, "r_y1": 681.954, "r_x2": 447.652, "r_y2": 674.153, "r_x3": 317.955, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "semantics of the text. Labels such as", "orig": "semantics of the text. Labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 240, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 449.86, "r_y0": 681.998, "r_x1": 474.314, "r_y1": 681.998, "r_x2": 474.314, "r_y2": 674.09, "r_x3": 449.86, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Author", "orig": "Author", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 241, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.172, "r_y0": 681.954, "r_x1": 490.397, "r_y1": 681.954, "r_x2": 490.397, "r_y2": 674.153, "r_x3": 477.172, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 242, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 492.606, "r_y0": 681.998, "r_x1": 528.299, "r_y1": 681.998, "r_x2": 528.299, "r_y2": 674.09, "r_x3": 492.606, "r_y3": 674.09, "coord_origin": "TOPLEFT"}, "text": "Affiliation", "orig": "Affiliation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 243, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.299, "r_y0": 681.954, "r_x1": 530.232, "r_y1": 681.954, "r_x2": 530.232, "r_y2": 674.153, "r_x3": 528.299, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 244, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 532.438, "r_y0": 681.954, "r_x1": 558.201, "r_y1": 681.954, "r_x2": 558.201, "r_y2": 674.153, "r_x3": 532.438, "r_y3": 674.153, "coord_origin": "TOPLEFT"}, "text": "as seen", "orig": "as seen", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 245, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 692.913, "r_x1": 558.204, "r_y1": 692.913, "r_x2": 558.204, "r_y2": 685.112, "r_x3": 317.955, "r_y3": 685.112, "coord_origin": "TOPLEFT"}, "text": "in DocBank, are often only distinguishable by discriminating on", "orig": "in DocBank, are often only distinguishable by discriminating on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula List-item , , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on"}, {"label": "footnote", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "footnote", "bbox": {"l": 317.955, "t": 701.024, "r": 369.246, "b": 708.756, "coord_origin": "TOPLEFT"}, "confidence": 0.9080352783203125, "cells": [{"index": 246, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 705.7909999999999, "r_x1": 320.503, "r_y1": 705.7909999999999, "r_x2": 320.503, "r_y2": 701.024, "r_x3": 317.955, "r_y3": 701.024, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 247, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.001, "r_y0": 708.756, "r_x1": 369.246, "r_y1": 708.756, "r_x2": 369.246, "r_y2": 702.688, "r_x3": 321.001, "r_y3": 702.688, "coord_origin": "TOPLEFT"}, "text": "https://arxiv.org/", "orig": "https://arxiv.org/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 https://arxiv.org/"}, {"label": "picture", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 53.05912780761719, "t": 310.7912902832031, "r": 295.8506164550781, "b": 540.864013671875, "coord_origin": "TOPLEFT"}, "confidence": 0.9847871661186218, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7625049948692322, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.691, "r_x1": 67.283, "r_y1": 336.691, "r_x2": 67.283, "r_y2": 328.89, "r_x3": 53.798, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 356.744, "r_x1": 74.221, "r_y1": 356.744, "r_x2": 74.221, "r_y2": 348.943, "r_x3": 64.708, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 356.744, "r_x1": 294.046, "r_y1": 356.744, "r_x2": 294.046, "r_y2": 348.943, "r_x3": 78.207, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 367.703, "r_x1": 95.73, "r_y1": 367.703, "r_x2": 95.73, "r_y2": 359.902, "r_x3": 78.207, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.977, "r_y0": 367.747, "r_x1": 130.171, "r_y1": 367.747, "r_x2": 130.171, "r_y2": 359.839, "r_x3": 97.977, "r_y3": 359.839, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 389.621, "r_x1": 91.594, "r_y1": 389.621, "r_x2": 91.594, "r_y2": 381.82, "r_x3": 78.207, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.835, "r_y0": 389.665, "r_x1": 106.881, "r_y1": 389.665, "r_x2": 106.881, "r_y2": 381.757, "r_x3": 93.835, "r_y3": 381.757, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 109.629, "r_y0": 389.621, "r_x1": 133.722, "r_y1": 389.621, "r_x2": 133.722, "r_y2": 381.82, "r_x3": 109.629, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 400.58, "r_x1": 74.221, "r_y1": 400.58, "r_x2": 74.221, "r_y2": 392.779, "r_x3": 64.708, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 400.58, "r_x1": 84.351, "r_y1": 400.58, "r_x2": 84.351, "r_y2": 392.779, "r_x3": 78.207, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 86.584, "r_y0": 400.624, "r_x1": 118.013, "r_y1": 400.624, "r_x2": 118.013, "r_y2": 392.716, "r_x3": 86.584, "r_y3": 392.716, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.269, "r_y0": 400.58, "r_x1": 295.557, "r_y1": 400.58, "r_x2": 295.557, "r_y2": 392.779, "r_x3": 120.269, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 433.457, "r_x1": 192.009, "r_y1": 433.457, "r_x2": 192.009, "r_y2": 425.656, "r_x3": 78.207, "r_y3": 425.656, "coord_origin": "TOPLEFT"}, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 444.416, "r_x1": 74.221, "r_y1": 444.416, "r_x2": 74.221, "r_y2": 436.615, "r_x3": 64.708, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 444.416, "r_x1": 112.616, "r_y1": 444.416, "r_x2": 112.616, "r_y2": 436.615, "r_x3": 78.207, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 114.861, "r_y0": 444.46, "r_x1": 142.612, "r_y1": 444.46, "r_x2": 142.612, "r_y2": 436.552, "r_x3": 114.861, "r_y3": 436.552, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.612, "r_y0": 444.416, "r_x1": 144.557, "r_y1": 444.416, "r_x2": 144.557, "r_y2": 436.615, "r_x3": 142.612, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.794, "r_y0": 444.416, "r_x1": 294.047, "r_y1": 444.416, "r_x2": 294.047, "r_y2": 436.615, "r_x3": 146.794, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "there must be exactly one corresponding", "orig": "there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 455.419, "r_x1": 102.793, "r_y1": 455.419, "r_x2": 102.793, "r_y2": 447.511, "r_x3": 78.207, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 105.245, "r_y0": 455.375, "r_x1": 113.1, "r_y1": 455.375, "r_x2": 113.1, "r_y2": 447.574, "r_x3": 105.245, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.341, "r_y0": 455.419, "r_x1": 134.404, "r_y1": 455.419, "r_x2": 134.404, "r_y2": 447.511, "r_x3": 115.341, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.403, "r_y0": 455.375, "r_x1": 136.376, "r_y1": 455.375, "r_x2": 136.376, "r_y2": 447.574, "r_x3": 134.403, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 466.333, "r_x1": 74.221, "r_y1": 466.333, "r_x2": 74.221, "r_y2": 458.532, "r_x3": 64.708, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 466.333, "r_x1": 267.468, "r_y1": 466.333, "r_x2": 267.468, "r_y2": 458.532, "r_x3": 78.207, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 269.706, "r_y0": 466.377, "r_x1": 294.046, "r_y1": 466.377, "r_x2": 294.046, "r_y2": 458.469, "r_x3": 269.706, "r_y3": 458.469, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 477.292, "r_x1": 102.3, "r_y1": 477.292, "r_x2": 102.3, "r_y2": 469.491, "r_x3": 78.207, "r_y3": 469.491, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 488.251, "r_x1": 74.221, "r_y1": 488.251, "r_x2": 74.221, "r_y2": 480.45, "r_x3": 64.708, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.202, "r_y0": 488.251, "r_x1": 206.135, "r_y1": 488.251, "r_x2": 206.135, "r_y2": 480.45, "r_x3": 78.202, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.38, "r_y0": 488.295, "r_x1": 238.122, "r_y1": 488.295, "r_x2": 238.122, "r_y2": 480.387, "r_x3": 208.38, "r_y3": 480.387, "coord_origin": "TOPLEFT"}, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.413, "r_y0": 488.251, "r_x1": 264.506, "r_y1": 488.251, "r_x2": 264.506, "r_y2": 480.45, "r_x3": 240.413, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 499.21, "r_x1": 294.046, "r_y1": 499.21, "r_x2": 294.046, "r_y2": 491.409, "r_x3": 78.207, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 510.169, "r_x1": 200.348, "r_y1": 510.169, "r_x2": 200.348, "r_y2": 502.368, "r_x3": 78.207, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.667, "r_y0": 510.213, "r_x1": 256.575, "r_y1": 510.213, "r_x2": 256.575, "r_y2": 502.305, "r_x3": 203.667, "r_y3": 502.305, "coord_origin": "TOPLEFT"}, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 256.574, "r_y0": 510.169, "r_x1": 258.586, "r_y1": 510.169, "r_x2": 258.586, "r_y2": 502.368, "r_x3": 256.574, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.906, "r_y0": 510.169, "r_x1": 294.044, "r_y1": 510.169, "r_x2": 294.044, "r_y2": 502.368, "r_x3": 261.906, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "unless it", "orig": "unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 521.1279999999999, "r_x1": 208.13, "r_y1": 521.1279999999999, "r_x2": 208.13, "r_y2": 513.327, "r_x3": 78.207, "r_y3": 513.327, "coord_origin": "TOPLEFT"}, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.14, "r_x1": 295.563, "r_y1": 552.14, "r_x2": 295.563, "r_y2": 544.3389999999999, "r_x3": 53.798, "r_y3": 544.3389999999999, "coord_origin": "TOPLEFT"}, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 181.83299999999997, "r_x1": 416.179, "r_y1": 181.83299999999997, "r_x2": 416.179, "r_y2": 180.015, "r_x3": 340.002, "r_y3": 180.015, "coord_origin": "TOPLEFT"}, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.383, "r_y0": 92.00300000000004, "r_x1": 417.395, "r_y1": 92.00300000000004, "r_x2": 417.395, "r_y2": 86.39099999999996, "r_x3": 339.383, "r_y3": 86.39099999999996, "coord_origin": "TOPLEFT"}, "text": "Compliant with guidelines", "orig": "Compliant with guidelines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.428, "r_y0": 92.00300000000004, "r_x1": 545.834, "r_y1": 92.00300000000004, "r_x2": 545.834, "r_y2": 86.39099999999996, "r_x3": 451.428, "r_y3": 86.39099999999996, "coord_origin": "TOPLEFT"}, "text": "Plausible but invalid alternative", "orig": "Plausible but invalid alternative", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.337, "r_y0": 371.669, "r_x1": 513.216, "r_y1": 371.669, "r_x2": 513.216, "r_y2": 366.057, "r_x3": 350.337, "r_y3": 366.057, "coord_origin": "TOPLEFT"}, "text": "Borderline case: Two guideline-compliant alternatives", "orig": "Borderline case: Two guideline-compliant alternatives", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 247.11400000000003, "r_x1": 416.179, 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"from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 322.194, "r_y0": 259.21900000000005, "r_x1": 326.015, "r_y1": 259.21900000000005, "r_x2": 326.015, "r_y2": 254.54200000000003, "r_x3": 322.194, "r_y3": 254.54200000000003, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 322.194, "r_y0": 372.762, "r_x1": 326.015, "r_y1": 372.762, "r_x2": 326.015, "r_y2": 368.085, "r_x3": 322.194, "r_y3": 368.085, "coord_origin": "TOPLEFT"}, "text": "D", "orig": "D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 481.718, "r_x1": 558.389, "r_y1": 481.718, "r_x2": 558.389, "r_y2": 474.265, "r_x3": 317.955, "r_y3": 474.265, "coord_origin": "TOPLEFT"}, "text": "Figure 4: Examples of plausible annotation alternatives for", "orig": "Figure 4: Examples of plausible annotation alternatives for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 492.677, "r_x1": 559.806, "r_y1": 492.677, "r_x2": 559.806, "r_y2": 485.224, "r_x3": 317.955, "r_y3": 485.224, "coord_origin": "TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 53.798, "t": 87.79399999999998, "r": 294.045, "b": 106.59799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 109.71199999999999, "r": 295.559, "b": 249.019, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.798, "t": 252.17700000000002, "r": 295.56, "b": 336.691, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.691, "r_x1": 67.283, "r_y1": 336.691, "r_x2": 67.283, "r_y2": 328.89, "r_x3": 53.798, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.943, "r": 294.046, "b": 389.665, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 356.744, "r_x1": 74.221, "r_y1": 356.744, "r_x2": 74.221, "r_y2": 348.943, "r_x3": 64.708, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, 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Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. 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Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 577.216, "r": 295.562, "b": 705.565, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. 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"TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.623, "t": 525.928, "r": 558.204, "b": 544.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.623, "t": 547.846, "r": 559.715, "b": 709.072, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, 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"bbox": {"l": 53.798, "t": 87.79399999999998, "r": 294.045, "b": 106.59799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 109.71199999999999, "r": 295.559, "b": 249.019, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.798, "t": 252.17700000000002, "r": 295.56, "b": 336.691, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.691, "r_x1": 67.283, "r_y1": 336.691, "r_x2": 67.283, "r_y2": 328.89, "r_x3": 53.798, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.943, "r": 294.046, "b": 389.665, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 356.744, "r_x1": 74.221, "r_y1": 356.744, "r_x2": 74.221, "r_y2": 348.943, "r_x3": 64.708, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 356.744, "r_x1": 294.046, "r_y1": 356.744, "r_x2": 294.046, "r_y2": 348.943, "r_x3": 78.207, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 367.703, "r_x1": 95.73, "r_y1": 367.703, "r_x2": 95.73, "r_y2": 359.902, "r_x3": 78.207, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.977, "r_y0": 367.747, "r_x1": 130.171, "r_y1": 367.747, "r_x2": 130.171, "r_y2": 359.839, "r_x3": 97.977, "r_y3": 359.839, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.171, "r_y0": 367.703, "r_x1": 132.163, "r_y1": 367.703, "r_x2": 132.163, "r_y2": 359.902, "r_x3": 130.171, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.409, "r_y0": 367.703, "r_x1": 294.04, "r_y1": 367.703, "r_x2": 294.04, "r_y2": 359.902, "r_x3": 134.409, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "This definition is different from PubLayNet", "orig": "This definition is different from PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 378.662, "r_x1": 294.044, "r_y1": 378.662, "r_x2": 294.044, "r_y2": 370.861, "r_x3": 78.207, "r_y3": 370.861, "coord_origin": "TOPLEFT"}, "text": "and DocBank, where all list-items are grouped together into", "orig": "and DocBank, where all list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 389.621, "r_x1": 91.594, "r_y1": 389.621, "r_x2": 91.594, "r_y2": 381.82, "r_x3": 78.207, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.835, "r_y0": 389.665, "r_x1": 106.881, "r_y1": 389.665, "r_x2": 106.881, "r_y2": 381.757, "r_x3": 93.835, "r_y3": 381.757, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 109.629, "r_y0": 389.621, "r_x1": 133.722, "r_y1": 389.621, "r_x2": 133.722, "r_y2": 381.82, "r_x3": 109.629, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.708, "t": 392.716, "r": 295.564, "b": 433.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 400.58, "r_x1": 74.221, "r_y1": 400.58, "r_x2": 74.221, "r_y2": 392.779, "r_x3": 64.708, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 400.58, "r_x1": 84.351, "r_y1": 400.58, "r_x2": 84.351, "r_y2": 392.779, "r_x3": 78.207, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 86.584, "r_y0": 400.624, "r_x1": 118.013, "r_y1": 400.624, "r_x2": 118.013, "r_y2": 392.716, "r_x3": 86.584, "r_y3": 392.716, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.269, "r_y0": 400.58, "r_x1": 295.557, "r_y1": 400.58, "r_x2": 295.557, "r_y2": 392.779, "r_x3": 120.269, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 433.457, "r_x1": 192.009, "r_y1": 433.457, "r_x2": 192.009, "r_y2": 425.656, "r_x3": 78.207, "r_y3": 425.656, "coord_origin": "TOPLEFT"}, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.552, "r": 294.047, "b": 455.419, "coord_origin": "TOPLEFT"}, "confidence": 0.9413429498672485, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 444.416, "r_x1": 74.221, "r_y1": 444.416, "r_x2": 74.221, "r_y2": 436.615, "r_x3": 64.708, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 444.416, "r_x1": 112.616, "r_y1": 444.416, "r_x2": 112.616, "r_y2": 436.615, "r_x3": 78.207, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 114.861, "r_y0": 444.46, "r_x1": 142.612, "r_y1": 444.46, "r_x2": 142.612, "r_y2": 436.552, "r_x3": 114.861, "r_y3": 436.552, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.612, "r_y0": 444.416, "r_x1": 144.557, "r_y1": 444.416, "r_x2": 144.557, "r_y2": 436.615, "r_x3": 142.612, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.794, "r_y0": 444.416, "r_x1": 294.047, "r_y1": 444.416, "r_x2": 294.047, "r_y2": 436.615, "r_x3": 146.794, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "there must be exactly one corresponding", "orig": "there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 455.419, "r_x1": 102.793, "r_y1": 455.419, "r_x2": 102.793, "r_y2": 447.511, "r_x3": 78.207, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 105.245, "r_y0": 455.375, "r_x1": 113.1, "r_y1": 455.375, "r_x2": 113.1, "r_y2": 447.574, "r_x3": 105.245, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.341, "r_y0": 455.419, "r_x1": 134.404, "r_y1": 455.419, "r_x2": 134.404, "r_y2": 447.511, "r_x3": 115.341, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.403, "r_y0": 455.375, "r_x1": 136.376, "r_y1": 455.375, "r_x2": 136.376, "r_y2": 447.574, "r_x3": 134.403, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.469, "r": 294.046, "b": 477.292, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 466.333, "r_x1": 74.221, "r_y1": 466.333, "r_x2": 74.221, "r_y2": 458.532, "r_x3": 64.708, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 466.333, "r_x1": 267.468, "r_y1": 466.333, "r_x2": 267.468, "r_y2": 458.532, "r_x3": 78.207, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 269.706, "r_y0": 466.377, "r_x1": 294.046, "r_y1": 466.377, "r_x2": 294.046, "r_y2": 458.469, "r_x3": 269.706, "r_y3": 458.469, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 477.292, "r_x1": 102.3, "r_y1": 477.292, "r_x2": 102.3, "r_y2": 469.491, "r_x3": 78.207, "r_y3": 469.491, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.387, "r": 264.506, "b": 488.295, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 488.251, "r_x1": 74.221, "r_y1": 488.251, "r_x2": 74.221, "r_y2": 480.45, "r_x3": 64.708, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.202, "r_y0": 488.251, "r_x1": 206.135, "r_y1": 488.251, "r_x2": 206.135, "r_y2": 480.45, "r_x3": 78.202, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.38, "r_y0": 488.295, "r_x1": 238.122, "r_y1": 488.295, "r_x2": 238.122, "r_y2": 480.387, "r_x3": 208.38, "r_y3": 480.387, "coord_origin": "TOPLEFT"}, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.413, "r_y0": 488.251, "r_x1": 264.506, "r_y1": 488.251, "r_x2": 264.506, "r_y2": 480.45, "r_x3": 240.413, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708, "t": 491.409, "r": 294.046, "b": 521.1279999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9544308185577393, "cells": [{"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 499.21, "r_x1": 294.046, "r_y1": 499.21, "r_x2": 294.046, "r_y2": 491.409, "r_x3": 78.207, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 510.169, "r_x1": 200.348, "r_y1": 510.169, "r_x2": 200.348, "r_y2": 502.368, "r_x3": 78.207, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.667, "r_y0": 510.213, "r_x1": 256.575, "r_y1": 510.213, "r_x2": 256.575, "r_y2": 502.305, "r_x3": 203.667, "r_y3": 502.305, "coord_origin": "TOPLEFT"}, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 256.574, "r_y0": 510.169, "r_x1": 258.586, "r_y1": 510.169, "r_x2": 258.586, "r_y2": 502.368, "r_x3": 256.574, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.906, "r_y0": 510.169, "r_x1": 294.044, "r_y1": 510.169, "r_x2": 294.044, "r_y2": 502.368, "r_x3": 261.906, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "unless it", "orig": "unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 521.1279999999999, "r_x1": 208.13, "r_y1": 521.1279999999999, "r_x2": 208.13, "r_y2": 513.327, "r_x3": 78.207, "r_y3": 513.327, "coord_origin": "TOPLEFT"}, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.529, "t": 533.381, "r": 295.563, "b": 574.058, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.14, "r_x1": 295.563, "r_y1": 552.14, "r_x2": 295.563, "r_y2": 544.3389999999999, "r_x3": 53.798, "r_y3": 544.3389999999999, "coord_origin": "TOPLEFT"}, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 577.216, "r": 295.562, "b": 705.565, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. 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false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 492.677, "r_x1": 559.806, "r_y1": 492.677, "r_x2": 559.806, "r_y2": 485.224, "r_x3": 317.955, "r_y3": 485.224, "coord_origin": "TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.623, "t": 525.928, "r": 558.204, "b": 544.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.623, "t": 547.846, "r": 559.715, "b": 709.072, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "body": [{"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 53.798, "t": 87.79399999999998, "r": 294.045, "b": 106.59799999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9022762179374695, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 95.59500000000003, "r_x1": 294.045, "r_y1": 95.59500000000003, "r_x2": 294.045, "r_y2": 87.79399999999998, "r_x3": 53.798, "r_y3": 87.79399999999998, "coord_origin": "TOPLEFT"}, "text": "the textual content of an element, which goes beyond visual layout", "orig": "the textual content of an element, which goes beyond visual layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.55399999999997, "r_x1": 188.133, "r_y1": 106.55399999999997, "r_x2": 188.133, "r_y2": 98.75300000000004, "r_x3": 53.798, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "recognition, in particular outside the", "orig": "recognition, in particular outside the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.374, "r_y0": 106.59799999999996, "r_x1": 251.256, "r_y1": 106.59799999999996, "r_x2": 251.256, "r_y2": 98.69000000000005, "r_x3": 190.374, "r_y3": 98.69000000000005, "coord_origin": "TOPLEFT"}, "text": "Scientific Articles", "orig": "Scientific Articles", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.708, "r_y0": 106.55399999999997, "r_x1": 287.018, "r_y1": 106.55399999999997, "r_x2": 287.018, "r_y2": 98.75300000000004, "r_x3": 253.708, "r_y3": 98.75300000000004, "coord_origin": "TOPLEFT"}, "text": "category.", "orig": "category.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category."}, {"label": "text", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.798, "t": 109.71199999999999, "r": 295.559, "b": 249.019, "coord_origin": "TOPLEFT"}, "confidence": 0.9711344838142395, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 117.51300000000003, "r_x1": 294.043, "r_y1": 117.51300000000003, "r_x2": 294.043, "r_y2": 109.71199999999999, "r_x3": 63.761, "r_y3": 109.71199999999999, "coord_origin": "TOPLEFT"}, "text": "At first sight, the task of visual document-layout interpretation", "orig": "At first sight, the task of visual document-layout interpretation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 128.47199999999998, "r_x1": 294.043, "r_y1": 128.47199999999998, "r_x2": 294.043, "r_y2": 120.67100000000005, "r_x3": 53.798, "r_y3": 120.67100000000005, "coord_origin": "TOPLEFT"}, "text": "appears intuitive enough to obtain plausible annotations in most", "orig": "appears intuitive enough to obtain plausible annotations in most", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 139.42999999999995, "r_x1": 295.556, "r_y1": 139.42999999999995, "r_x2": 295.556, "r_y2": 131.62900000000002, "r_x3": 53.798, "r_y3": 131.62900000000002, "coord_origin": "TOPLEFT"}, "text": "cases. However, during early trial-runs in the core team, we ob-", "orig": "cases. However, during early trial-runs in the core team, we ob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 150.389, "r_x1": 294.047, "r_y1": 150.389, "r_x2": 294.047, "r_y2": 142.58799999999997, "r_x3": 53.798, "r_y3": 142.58799999999997, "coord_origin": "TOPLEFT"}, "text": "served many cases in which annotators use different annotation", "orig": "served many cases in which annotators use different annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 161.34799999999996, "r_x1": 295.559, "r_y1": 161.34799999999996, "r_x2": 295.559, "r_y2": 153.54700000000003, "r_x3": 53.798, "r_y3": 153.54700000000003, "coord_origin": "TOPLEFT"}, "text": "styles, especially for documents with challenging layouts. For ex-", "orig": "styles, especially for documents with challenging layouts. For ex-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 172.30700000000002, "r_x1": 294.045, "r_y1": 172.30700000000002, "r_x2": 294.045, "r_y2": 164.50599999999997, "r_x3": 53.798, "r_y3": 164.50599999999997, "coord_origin": "TOPLEFT"}, "text": "ample, if a figure is presented with subfigures, one annotator might", "orig": "ample, if a figure is presented with subfigures, one annotator might", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 183.26599999999996, "r_x1": 294.048, "r_y1": 183.26599999999996, "r_x2": 294.048, "r_y2": 175.46500000000003, "r_x3": 53.798, "r_y3": 175.46500000000003, "coord_origin": "TOPLEFT"}, "text": "draw a single figure bounding-box, while another might annotate", "orig": "draw a single figure bounding-box, while another might annotate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 194.22500000000002, "r_x1": 294.047, "r_y1": 194.22500000000002, "r_x2": 294.047, "r_y2": 186.42399999999998, "r_x3": 53.798, "r_y3": 186.42399999999998, "coord_origin": "TOPLEFT"}, "text": "each subfigure separately. The same applies for lists, where one", "orig": "each subfigure separately. The same applies for lists, where one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 205.18399999999997, "r_x1": 295.556, "r_y1": 205.18399999999997, "r_x2": 295.556, "r_y2": 197.38300000000004, "r_x3": 53.798, "r_y3": 197.38300000000004, "coord_origin": "TOPLEFT"}, "text": "might annotate all list items in one block or each list item sep-", "orig": "might annotate all list items in one block or each list item sep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 216.14300000000003, "r_x1": 294.047, "r_y1": 216.14300000000003, "r_x2": 294.047, "r_y2": 208.34199999999998, "r_x3": 53.798, "r_y3": 208.34199999999998, "coord_origin": "TOPLEFT"}, "text": "arately. In essence, we observed that challenging layouts would", "orig": "arately. In essence, we observed that challenging layouts would", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 227.10199999999998, "r_x1": 294.045, "r_y1": 227.10199999999998, "r_x2": 294.045, "r_y2": 219.30100000000004, "r_x3": 53.798, "r_y3": 219.30100000000004, "coord_origin": "TOPLEFT"}, "text": "be annotated in different but plausible ways. To illustrate this, we", "orig": "be annotated in different but plausible ways. To illustrate this, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 238.06100000000004, "r_x1": 294.047, "r_y1": 238.06100000000004, "r_x2": 294.047, "r_y2": 230.26, "r_x3": 53.798, "r_y3": 230.26, "coord_origin": "TOPLEFT"}, "text": "show in Figure 4 multiple examples of plausible but inconsistent", "orig": "show in Figure 4 multiple examples of plausible but inconsistent", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 249.019, "r_x1": 168.613, "r_y1": 249.019, "r_x2": 168.613, "r_y2": 241.21799999999996, "r_x3": 53.798, "r_y3": 241.21799999999996, "coord_origin": "TOPLEFT"}, "text": "annotations on the same pages.", "orig": "annotations on the same pages.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.798, "t": 252.17700000000002, "r": 295.56, "b": 336.691, "coord_origin": "TOPLEFT"}, "confidence": 0.985905110836029, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 259.97799999999995, "r_x1": 294.219, "r_y1": 259.97799999999995, "r_x2": 294.219, "r_y2": 252.17700000000002, "r_x3": 63.761, "r_y3": 252.17700000000002, "coord_origin": "TOPLEFT"}, "text": "Obviously, this inconsistency in annotations is not desirable for", "orig": "Obviously, this inconsistency in annotations is not desirable for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 270.937, "r_x1": 295.56, "r_y1": 270.937, "r_x2": 295.56, "r_y2": 263.13599999999997, "r_x3": 53.798, "r_y3": 263.13599999999997, "coord_origin": "TOPLEFT"}, "text": "datasets which are intended to be used for model training. To min-", "orig": "datasets which are intended to be used for model training. To min-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 281.896, "r_x1": 295.557, "r_y1": 281.896, "r_x2": 295.557, "r_y2": 274.095, "r_x3": 53.798, "r_y3": 274.095, "coord_origin": "TOPLEFT"}, "text": "imise these inconsistencies, we created a detailed annotation guide-", "orig": "imise these inconsistencies, we created a detailed annotation guide-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 292.855, "r_x1": 294.049, "r_y1": 292.855, "r_x2": 294.049, "r_y2": 285.054, "r_x3": 53.798, "r_y3": 285.054, "coord_origin": "TOPLEFT"}, "text": "line. While perfect consistency across 40 annotation staff members", "orig": "line. While perfect consistency across 40 annotation staff members", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 303.814, "r_x1": 294.043, "r_y1": 303.814, "r_x2": 294.043, "r_y2": 296.013, "r_x3": 53.798, "r_y3": 296.013, "coord_origin": "TOPLEFT"}, "text": "is clearly not possible to achieve, we saw a huge improvement in", "orig": "is clearly not possible to achieve, we saw a huge improvement in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 314.773, "r_x1": 294.047, "r_y1": 314.773, "r_x2": 294.047, "r_y2": 306.972, "r_x3": 53.798, "r_y3": 306.972, "coord_origin": "TOPLEFT"}, "text": "annotation consistency after the introduction of our annotation", "orig": "annotation consistency after the introduction of our annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 325.732, "r_x1": 294.047, "r_y1": 325.732, "r_x2": 294.047, "r_y2": 317.931, "r_x3": 53.798, "r_y3": 317.931, "coord_origin": "TOPLEFT"}, "text": "guideline. A few selected, non-trivial highlights of the guideline", "orig": "guideline. A few selected, non-trivial highlights of the guideline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 336.691, "r_x1": 67.283, "r_y1": 336.691, "r_x2": 67.283, "r_y2": 328.89, "r_x3": 53.798, "r_y3": 328.89, "coord_origin": "TOPLEFT"}, "text": "are:", "orig": "are:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:"}, {"label": "list_item", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 64.708, "t": 348.943, "r": 294.046, "b": 389.665, "coord_origin": "TOPLEFT"}, "confidence": 0.9691526293754578, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 356.744, "r_x1": 74.221, "r_y1": 356.744, "r_x2": 74.221, "r_y2": 348.943, "r_x3": 64.708, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "(1)", "orig": "(1)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 356.744, "r_x1": 294.046, "r_y1": 356.744, "r_x2": 294.046, "r_y2": 348.943, "r_x3": 78.207, "r_y3": 348.943, "coord_origin": "TOPLEFT"}, "text": "Every list-item is an individual object instance with class", "orig": "Every list-item is an individual object instance with class", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 367.703, "r_x1": 95.73, "r_y1": 367.703, "r_x2": 95.73, "r_y2": 359.902, "r_x3": 78.207, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "label", "orig": "label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 97.977, "r_y0": 367.747, "r_x1": 130.171, "r_y1": 367.747, "r_x2": 130.171, "r_y2": 359.839, "r_x3": 97.977, "r_y3": 359.839, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 130.171, "r_y0": 367.703, "r_x1": 132.163, "r_y1": 367.703, "r_x2": 132.163, "r_y2": 359.902, "r_x3": 130.171, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.409, "r_y0": 367.703, "r_x1": 294.04, "r_y1": 367.703, "r_x2": 294.04, "r_y2": 359.902, "r_x3": 134.409, "r_y3": 359.902, "coord_origin": "TOPLEFT"}, "text": "This definition is different from PubLayNet", "orig": "This definition is different from PubLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 378.662, "r_x1": 294.044, "r_y1": 378.662, "r_x2": 294.044, "r_y2": 370.861, "r_x3": 78.207, "r_y3": 370.861, "coord_origin": "TOPLEFT"}, "text": "and DocBank, where all list-items are grouped together into", "orig": "and DocBank, where all list-items are grouped together into", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 389.621, "r_x1": 91.594, "r_y1": 389.621, "r_x2": 91.594, "r_y2": 381.82, "r_x3": 78.207, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "one", "orig": "one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.835, "r_y0": 389.665, "r_x1": 106.881, "r_y1": 389.665, "r_x2": 106.881, "r_y2": 381.757, "r_x3": 93.835, "r_y3": 381.757, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 109.629, "r_y0": 389.621, "r_x1": 133.722, "r_y1": 389.621, "r_x2": 133.722, "r_y2": 381.82, "r_x3": 109.629, "r_y3": 381.82, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object."}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 64.708, "t": 392.716, "r": 295.564, "b": 433.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9591284990310669, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 400.58, "r_x1": 74.221, "r_y1": 400.58, "r_x2": 74.221, "r_y2": 392.779, "r_x3": 64.708, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "(2)", "orig": "(2)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 400.58, "r_x1": 84.351, "r_y1": 400.58, "r_x2": 84.351, "r_y2": 392.779, "r_x3": 78.207, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 86.584, "r_y0": 400.624, "r_x1": 118.013, "r_y1": 400.624, "r_x2": 118.013, "r_y2": 392.716, "r_x3": 86.584, "r_y3": 392.716, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 120.269, "r_y0": 400.58, "r_x1": 295.557, "r_y1": 400.58, "r_x2": 295.557, "r_y2": 392.779, "r_x3": 120.269, "r_y3": 392.779, "coord_origin": "TOPLEFT"}, "text": "is a paragraph with hanging indentation. Single-", "orig": "is a paragraph with hanging indentation. Single-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 411.539, "r_x1": 181.81, "r_y1": 411.539, "r_x2": 181.81, "r_y2": 403.738, "r_x3": 78.207, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "line elements can qualify as", "orig": "line elements can qualify as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 184.175, "r_y0": 411.583, "r_x1": 216.688, "r_y1": 411.583, "r_x2": 216.688, "r_y2": 403.675, "r_x3": 184.175, "r_y3": 403.675, "coord_origin": "TOPLEFT"}, "text": "List-item", "orig": "List-item", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.078, "r_y0": 411.539, "r_x1": 295.564, "r_y1": 411.539, "r_x2": 295.564, "r_y2": 403.738, "r_x3": 219.078, "r_y3": 403.738, "coord_origin": "TOPLEFT"}, "text": "if the neighbour ele-", "orig": "if the neighbour ele-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 422.498, "r_x1": 294.046, "r_y1": 422.498, "r_x2": 294.046, "r_y2": 414.697, "r_x3": 78.207, "r_y3": 414.697, "coord_origin": "TOPLEFT"}, "text": "ments expose hanging indentation. Bullet or enumeration", "orig": "ments expose hanging indentation. Bullet or enumeration", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 433.457, "r_x1": 192.009, "r_y1": 433.457, "r_x2": 192.009, "r_y2": 425.656, "r_x3": 78.207, "r_y3": 425.656, "coord_origin": "TOPLEFT"}, "text": "symbols are not a requirement.", "orig": "symbols are not a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement."}, {"label": "list_item", "id": 10, "page_no": 4, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 64.708, "t": 436.552, "r": 294.047, "b": 455.419, "coord_origin": "TOPLEFT"}, "confidence": 0.9413429498672485, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 444.416, "r_x1": 74.221, "r_y1": 444.416, "r_x2": 74.221, "r_y2": 436.615, "r_x3": 64.708, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "(3)", "orig": "(3)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 444.416, "r_x1": 112.616, "r_y1": 444.416, "r_x2": 112.616, "r_y2": 436.615, "r_x3": 78.207, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "For every", "orig": "For every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 114.861, "r_y0": 444.46, "r_x1": 142.612, "r_y1": 444.46, "r_x2": 142.612, "r_y2": 436.552, "r_x3": 114.861, "r_y3": 436.552, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.612, "r_y0": 444.416, "r_x1": 144.557, "r_y1": 444.416, "r_x2": 144.557, "r_y2": 436.615, "r_x3": 142.612, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 146.794, "r_y0": 444.416, "r_x1": 294.047, "r_y1": 444.416, "r_x2": 294.047, "r_y2": 436.615, "r_x3": 146.794, "r_y3": 436.615, "coord_origin": "TOPLEFT"}, "text": "there must be exactly one corresponding", "orig": "there must be exactly one corresponding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 455.419, "r_x1": 102.793, "r_y1": 455.419, "r_x2": 102.793, "r_y2": 447.511, "r_x3": 78.207, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 105.245, "r_y0": 455.375, "r_x1": 113.1, "r_y1": 455.375, "r_x2": 113.1, "r_y2": 447.574, "r_x3": 105.245, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": "or", "orig": "or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 115.341, "r_y0": 455.419, "r_x1": 134.404, "r_y1": 455.419, "r_x2": 134.404, "r_y2": 447.511, "r_x3": 115.341, "r_y3": 447.511, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.403, "r_y0": 455.375, "r_x1": 136.376, "r_y1": 455.375, "r_x2": 136.376, "r_y2": 447.574, "r_x3": 134.403, "r_y3": 447.574, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(3) For every Caption , there must be exactly one corresponding Picture or Table ."}, {"label": "list_item", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 64.708, "t": 458.469, "r": 294.046, "b": 477.292, "coord_origin": "TOPLEFT"}, "confidence": 0.9426738023757935, "cells": [{"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 466.333, "r_x1": 74.221, "r_y1": 466.333, "r_x2": 74.221, "r_y2": 458.532, "r_x3": 64.708, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "(4)", "orig": "(4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 466.333, "r_x1": 267.468, "r_y1": 466.333, "r_x2": 267.468, "r_y2": 458.532, "r_x3": 78.207, "r_y3": 458.532, "coord_origin": "TOPLEFT"}, "text": "Connected sub-pictures are grouped together in one", "orig": "Connected sub-pictures are grouped together in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 269.706, "r_y0": 466.377, "r_x1": 294.046, "r_y1": 466.377, "r_x2": 294.046, "r_y2": 458.469, "r_x3": 269.706, "r_y3": 458.469, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 477.292, "r_x1": 102.3, "r_y1": 477.292, "r_x2": 102.3, "r_y2": 469.491, "r_x3": 78.207, "r_y3": 469.491, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(4) Connected sub-pictures are grouped together in one Picture object."}, {"label": "list_item", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 64.708, "t": 480.387, "r": 264.506, "b": 488.295, "coord_origin": "TOPLEFT"}, "confidence": 0.9147457480430603, "cells": [{"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 488.251, "r_x1": 74.221, "r_y1": 488.251, "r_x2": 74.221, "r_y2": 480.45, "r_x3": 64.708, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "(5)", "orig": "(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.202, "r_y0": 488.251, "r_x1": 206.135, "r_y1": 488.251, "r_x2": 206.135, "r_y2": 480.45, "r_x3": 78.202, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "Formula numbers are included in a", "orig": "Formula numbers are included in a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 208.38, "r_y0": 488.295, "r_x1": 238.122, "r_y1": 488.295, "r_x2": 238.122, "r_y2": 480.387, "r_x3": 208.38, "r_y3": 480.387, "coord_origin": "TOPLEFT"}, "text": "Formula", "orig": "Formula", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.413, "r_y0": 488.251, "r_x1": 264.506, "r_y1": 488.251, "r_x2": 264.506, "r_y2": 480.45, "r_x3": 240.413, "r_y3": 480.45, "coord_origin": "TOPLEFT"}, "text": "object.", "orig": "object.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(5) Formula numbers are included in a Formula object."}, {"label": "list_item", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 64.708, "t": 491.409, "r": 294.046, "b": 521.1279999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9544308185577393, "cells": [{"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.708, "r_y0": 499.21, "r_x1": 74.221, "r_y1": 499.21, "r_x2": 74.221, "r_y2": 491.409, "r_x3": 64.708, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "(6)", "orig": "(6)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 499.21, "r_x1": 294.046, "r_y1": 499.21, "r_x2": 294.046, "r_y2": 491.409, "r_x3": 78.207, "r_y3": 491.409, "coord_origin": "TOPLEFT"}, "text": "Emphasised text (e.g. in italic or bold) at the beginning of", "orig": "Emphasised text (e.g. in italic or bold) at the beginning of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 510.169, "r_x1": 200.348, "r_y1": 510.169, "r_x2": 200.348, "r_y2": 502.368, "r_x3": 78.207, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "a paragraph is not considered a", "orig": "a paragraph is not considered a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.667, "r_y0": 510.213, "r_x1": 256.575, "r_y1": 510.213, "r_x2": 256.575, "r_y2": 502.305, "r_x3": 203.667, "r_y3": 502.305, "coord_origin": "TOPLEFT"}, "text": "Section-header", "orig": "Section-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 256.574, "r_y0": 510.169, "r_x1": 258.586, "r_y1": 510.169, "r_x2": 258.586, "r_y2": 502.368, "r_x3": 256.574, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 261.906, "r_y0": 510.169, "r_x1": 294.044, "r_y1": 510.169, "r_x2": 294.044, "r_y2": 502.368, "r_x3": 261.906, "r_y3": 502.368, "coord_origin": "TOPLEFT"}, "text": "unless it", "orig": "unless it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.207, "r_y0": 521.1279999999999, "r_x1": 208.13, "r_y1": 521.1279999999999, "r_x2": 208.13, "r_y2": 513.327, "r_x3": 78.207, "r_y3": 513.327, "coord_origin": "TOPLEFT"}, "text": "appears exclusively on its own line.", "orig": "appears exclusively on its own line.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 53.529, "t": 533.381, "r": 295.563, "b": 574.058, "coord_origin": "TOPLEFT"}, "confidence": 0.9811431169509888, "cells": [{"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 541.182, "r_x1": 294.043, "r_y1": 541.182, "r_x2": 294.043, "r_y2": 533.381, "r_x3": 53.529, "r_y3": 533.381, "coord_origin": "TOPLEFT"}, "text": "The complete annotation guideline is over 100 pages long and a", "orig": "The complete annotation guideline is over 100 pages long and a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.14, "r_x1": 295.563, "r_y1": 552.14, "r_x2": 295.563, "r_y2": 544.3389999999999, "r_x3": 53.798, "r_y3": 544.3389999999999, "coord_origin": "TOPLEFT"}, "text": "detailed description is obviously out of scope for this paper. Never-", "orig": "detailed description is obviously out of scope for this paper. Never-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.0989999999999, "r_x1": 294.045, "r_y1": 563.0989999999999, "r_x2": 294.045, "r_y2": 555.298, "r_x3": 53.798, "r_y3": 555.298, "coord_origin": "TOPLEFT"}, "text": "theless, it will be made publicly available alongside with DocLayNet", "orig": "theless, it will be made publicly available alongside with DocLayNet", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.058, "r_x1": 127.242, "r_y1": 574.058, "r_x2": 127.242, "r_y2": 566.2570000000001, "r_x3": 53.798, "r_y3": 566.2570000000001, "coord_origin": "TOPLEFT"}, "text": "for future reference.", "orig": "for future reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference."}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 53.798, "t": 577.216, "r": 295.562, "b": 705.565, "coord_origin": "TOPLEFT"}, "confidence": 0.9864389896392822, "cells": [{"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 584.893, "r_x1": 136.774, "r_y1": 584.893, "r_x2": 136.774, "r_y2": 577.44, "r_x3": 63.761, "r_y3": 577.44, "coord_origin": "TOPLEFT"}, "text": "Phase 3: Training.", "orig": "Phase 3: Training.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.008, "r_y0": 585.017, "r_x1": 295.562, "r_y1": 585.017, "r_x2": 295.562, "r_y2": 577.216, "r_x3": 139.008, "r_y3": 577.216, "coord_origin": "TOPLEFT"}, "text": "After a first trial with a small group of peo-", "orig": "After a first trial with a small group of peo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 595.976, "r_x1": 294.045, "r_y1": 595.976, "r_x2": 294.045, "r_y2": 588.175, "r_x3": 53.798, "r_y3": 588.175, "coord_origin": "TOPLEFT"}, "text": "ple, we realised that providing the annotation guideline and a set of", "orig": "ple, we realised that providing the annotation guideline and a set of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 606.935, "r_x1": 295.557, "r_y1": 606.935, "r_x2": 295.557, "r_y2": 599.134, "r_x3": 53.798, "r_y3": 599.134, "coord_origin": "TOPLEFT"}, "text": "random practice pages did not yield the desired quality level for lay-", "orig": "random practice pages did not yield the desired quality level for lay-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 617.894, "r_x1": 294.046, "r_y1": 617.894, "r_x2": 294.046, "r_y2": 610.093, "r_x3": 53.798, "r_y3": 610.093, "coord_origin": "TOPLEFT"}, "text": "out annotation. Therefore we prepared a subset of pages with two", "orig": "out annotation. Therefore we prepared a subset of pages with two", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 628.8530000000001, "r_x1": 295.424, "r_y1": 628.8530000000001, "r_x2": 295.424, "r_y2": 621.052, "r_x3": 53.798, "r_y3": 621.052, "coord_origin": "TOPLEFT"}, "text": "different complexity levels, each with a practice and an exam part.", "orig": "different complexity levels, each with a practice and an exam part.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.812, "r_x1": 294.047, "r_y1": 639.812, "r_x2": 294.047, "r_y2": 632.011, "r_x3": 53.798, "r_y3": 632.011, "coord_origin": "TOPLEFT"}, "text": "974 pages were reference-annotated by one proficient core team", "orig": "974 pages were reference-annotated by one proficient core team", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.771, "r_x1": 294.049, "r_y1": 650.771, "r_x2": 294.049, "r_y2": 642.97, "r_x3": 53.798, "r_y3": 642.97, "coord_origin": "TOPLEFT"}, "text": "member. Annotation staff were then given the task to annotate the", "orig": "member. Annotation staff were then given the task to annotate the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.73, "r_x1": 295.556, "r_y1": 661.73, "r_x2": 295.556, "r_y2": 653.929, "r_x3": 53.798, "r_y3": 653.929, "coord_origin": "TOPLEFT"}, "text": "same subsets (blinded from the reference). By comparing the an-", "orig": "same subsets (blinded from the reference). By comparing the an-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.688, "r_x1": 294.049, "r_y1": 672.688, "r_x2": 294.049, "r_y2": 664.887, "r_x3": 53.798, "r_y3": 664.887, "coord_origin": "TOPLEFT"}, "text": "notations of each staff member with the reference annotations, we", "orig": "notations of each staff member with the reference annotations, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.647, "r_x1": 295.425, "r_y1": 683.647, "r_x2": 295.425, "r_y2": 675.846, "r_x3": 53.798, "r_y3": 675.846, "coord_origin": "TOPLEFT"}, "text": "could quantify how closely their annotations matched the reference.", "orig": "could quantify how closely their annotations matched the reference.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.606, "r_x1": 295.027, "r_y1": 694.606, "r_x2": 295.027, "r_y2": 686.8050000000001, "r_x3": 53.798, "r_y3": 686.8050000000001, "coord_origin": "TOPLEFT"}, "text": "Only after passing two exam levels with high annotation quality,", "orig": "Only after passing two exam levels with high annotation quality,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.565, "r_x1": 294.041, "r_y1": 705.565, "r_x2": 294.041, "r_y2": 697.764, "r_x3": 53.798, "r_y3": 697.764, "coord_origin": "TOPLEFT"}, "text": "staff were admitted into the production phase. Practice iterations", "orig": "staff were admitted into the production phase. Practice iterations", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations"}, {"label": "picture", "id": 16, "page_no": 4, "cluster": {"id": 16, "label": "picture", "bbox": {"l": 315.960205078125, "t": 85.33881378173828, "r": 559.396484375, "b": 459.68084716796875, "coord_origin": "TOPLEFT"}, "confidence": 0.7264115214347839, "cells": [{"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 340.002, "r_y0": 181.83299999999997, "r_x1": 416.179, "r_y1": 181.83299999999997, "r_x2": 416.179, "r_y2": 180.015, "r_x3": 340.002, "r_y3": 180.015, "coord_origin": "TOPLEFT"}, "text": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "orig": "1ef23f5e6d7f10d393f9947e8208285dce9ae87250ac483ac4b4a59d51b4e037", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 339.383, "r_y0": 92.00300000000004, "r_x1": 417.395, "r_y1": 92.00300000000004, "r_x2": 417.395, "r_y2": 86.39099999999996, "r_x3": 339.383, "r_y3": 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false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 492.677, "r_x1": 559.806, "r_y1": 492.677, "r_x2": 559.806, "r_y2": 485.224, "r_x3": 317.955, "r_y3": 485.224, "coord_origin": "TOPLEFT"}, "text": "the same page. Criteria in our annotation guideline can re-", "orig": "the same page. Criteria in our annotation guideline can re-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 503.636, "r_x1": 544.141, "r_y1": 503.636, "r_x2": 544.141, "r_y2": 496.183, "r_x3": 317.955, "r_y3": 496.183, "coord_origin": "TOPLEFT"}, "text": "solve cases A to C, while the case D remains ambiguous.", "orig": "solve cases A to C, while the case D remains ambiguous.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous."}, {"label": "text", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.623, "t": 525.928, "r": 558.204, "b": 544.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9611718654632568, "cells": [{"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 533.729, "r_x1": 558.204, "r_y1": 533.729, "r_x2": 558.204, "r_y2": 525.928, "r_x3": 317.623, "r_y3": 525.928, "coord_origin": "TOPLEFT"}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the", "orig": "were carried out over a timeframe of 12 weeks, after which 8 of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 544.688, "r_x1": 509.119, "r_y1": 544.688, "r_x2": 509.119, "r_y2": 536.887, "r_x3": 317.749, "r_y3": 536.887, "coord_origin": "TOPLEFT"}, "text": "40 initially allocated annotators did not pass the bar.", "orig": "40 initially allocated annotators did not pass the bar.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.623, "t": 547.846, "r": 559.715, "b": 709.072, "coord_origin": "TOPLEFT"}, "confidence": 0.9829543232917786, "cells": [{"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 555.523, "r_x1": 456.801, "r_y1": 555.523, "r_x2": 456.801, "r_y2": 548.0699999999999, "r_x3": 327.918, "r_y3": 548.0699999999999, "coord_origin": "TOPLEFT"}, "text": "Phase 4: Production annotation.", "orig": "Phase 4: Production annotation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 458.712, "r_y0": 555.6469999999999, "r_x1": 558.489, "r_y1": 555.6469999999999, "r_x2": 558.489, "r_y2": 547.846, "r_x3": 458.712, "r_y3": 547.846, "coord_origin": "TOPLEFT"}, "text": "The previously selected 80K", "orig": "The previously selected 80K", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 566.606, "r_x1": 559.714, "r_y1": 566.606, "r_x2": 559.714, "r_y2": 558.8050000000001, "r_x3": 317.955, "r_y3": 558.8050000000001, "coord_origin": "TOPLEFT"}, "text": "pages were annotated with the defined 11 class labels by 32 annota-", "orig": "pages were annotated with the defined 11 class labels by 32 annota-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 577.565, "r_x1": 559.581, "r_y1": 577.565, "r_x2": 559.581, "r_y2": 569.764, "r_x3": 317.955, "r_y3": 569.764, "coord_origin": "TOPLEFT"}, "text": "tors. This production phase took around three months to complete.", "orig": "tors. This production phase took around three months to complete.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 588.524, "r_x1": 558.204, "r_y1": 588.524, "r_x2": 558.204, "r_y2": 580.723, "r_x3": 317.641, "r_y3": 580.723, "coord_origin": "TOPLEFT"}, "text": "All annotations were created online through CCS, which visualises", "orig": "All annotations were created online through CCS, which visualises", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 599.483, "r_x1": 558.202, "r_y1": 599.483, "r_x2": 558.202, "r_y2": 591.682, "r_x3": 317.955, "r_y3": 591.682, "coord_origin": "TOPLEFT"}, "text": "the programmatic PDF text-cells as an overlay on the page. The page", "orig": "the programmatic PDF text-cells as an overlay on the page. The page", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 610.441, "r_x1": 559.185, "r_y1": 610.441, "r_x2": 559.185, "r_y2": 602.64, "r_x3": 317.955, "r_y3": 602.64, "coord_origin": "TOPLEFT"}, "text": "annotation are obtained by drawing rectangular bounding-boxes,", "orig": "annotation are obtained by drawing rectangular bounding-boxes,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 621.4, "r_x1": 558.202, "r_y1": 621.4, "r_x2": 558.202, "r_y2": 613.5989999999999, "r_x3": 317.955, "r_y3": 613.5989999999999, "coord_origin": "TOPLEFT"}, "text": "as shown in Figure 3. With regard to the annotation practices, we", "orig": "as shown in Figure 3. With regard to the annotation practices, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 632.359, "r_x1": 559.582, "r_y1": 632.359, "r_x2": 559.582, "r_y2": 624.558, "r_x3": 317.955, "r_y3": 624.558, "coord_origin": "TOPLEFT"}, "text": "implemented a few constraints and capabilities on the tooling level.", "orig": "implemented a few constraints and capabilities on the tooling level.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 643.318, "r_x1": 559.714, "r_y1": 643.318, "r_x2": 559.714, "r_y2": 635.517, "r_x3": 317.955, "r_y3": 635.517, "coord_origin": "TOPLEFT"}, "text": "First, we only allow non-overlapping, vertically oriented, rectangu-", "orig": "First, we only allow non-overlapping, vertically oriented, rectangu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 654.277, "r_x1": 558.206, "r_y1": 654.277, "r_x2": 558.206, "r_y2": 646.476, "r_x3": 317.955, "r_y3": 646.476, "coord_origin": "TOPLEFT"}, "text": "lar boxes. For the large majority of documents, this constraint was", "orig": "lar boxes. For the large majority of documents, this constraint was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 665.236, "r_x1": 559.715, "r_y1": 665.236, "r_x2": 559.715, "r_y2": 657.435, "r_x3": 317.955, "r_y3": 657.435, "coord_origin": "TOPLEFT"}, "text": "sufficient and it speeds up the annotation considerably in compar-", "orig": "sufficient and it speeds up the annotation considerably in compar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 676.1949999999999, "r_x1": 558.199, "r_y1": 676.1949999999999, "r_x2": 558.199, "r_y2": 668.394, "r_x3": 317.955, "r_y3": 668.394, "coord_origin": "TOPLEFT"}, "text": "ison with arbitrary segmentation shapes. Second, annotator staff", "orig": "ison with arbitrary segmentation shapes. Second, annotator staff", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 687.154, "r_x1": 558.433, "r_y1": 687.154, "r_x2": 558.433, "r_y2": 679.353, "r_x3": 317.623, "r_y3": 679.353, "coord_origin": "TOPLEFT"}, "text": "were not able to see each other's annotations. This was enforced by", "orig": "were not able to see each other's annotations. This was enforced by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 698.113, "r_x1": 558.198, "r_y1": 698.113, "r_x2": 558.198, "r_y2": 690.312, "r_x3": 317.955, "r_y3": 690.312, "coord_origin": "TOPLEFT"}, "text": "design to avoid any bias in the annotation, which could skew the", "orig": "design to avoid any bias in the annotation, which could skew the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 709.072, "r_x1": 558.202, "r_y1": 709.072, "r_x2": 558.202, "r_y2": 701.271, "r_x3": 317.955, "r_y3": 701.271, "coord_origin": "TOPLEFT"}, "text": "numbers of the inter-annotator agreement (see Table 1). We wanted", "orig": "numbers of the inter-annotator agreement (see Table 1). We wanted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted"}], "headers": [{"label": "page_header", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8877827525138855, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 15, "page_no": 4, "cluster": {"id": 15, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8534670472145081, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 91.52, "r_y1": 106.05600000000004, "r_x2": 91.52, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "detection", "orig": "detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.976, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 95.976, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "networks on DocLayNet test set. The MRCNN", "orig": "networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.52, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 202.434, "r_y1": 138.933, "r_x2": 202.434, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.085, "r_y0": 138.95899999999995, "r_x1": 247.142, "r_y1": 138.95899999999995, "r_x2": 247.142, "r_y2": 131.48000000000002, "r_x3": 206.085, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.95, "r_y0": 138.933, "r_x1": 294.043, "r_y1": 138.933, "r_x2": 294.043, "r_y2": 131.48000000000002, "r_x3": 250.95, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 149.89200000000005, "r_x1": 294.044, "r_y1": 149.89200000000005, "r_x2": 294.044, "r_y2": 142.43899999999996, "r_x3": 53.52, "r_y3": 142.43899999999996, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 160.851, "r_x1": 294.044, "r_y1": 160.851, "r_x2": 294.044, "r_y2": 153.39800000000002, "r_x3": 53.798, "r_y3": 153.39800000000002, "coord_origin": "TOPLEFT"}, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 171.80999999999995, "r_x1": 295.649, "r_y1": 171.80999999999995, "r_x2": 295.649, "r_y2": 164.35699999999997, "r_x3": 53.798, "r_y3": 164.35699999999997, "coord_origin": "TOPLEFT"}, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 182.76800000000003, "r_x1": 268.624, "r_y1": 182.76800000000003, "r_x2": 268.624, "r_y2": 175.31500000000005, "r_x3": 53.798, "r_y3": 175.31500000000005, "coord_origin": "TOPLEFT"}, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 132.365, "r_y0": 206.20500000000004, "r_x1": 157.991, "r_y1": 206.20500000000004, "r_x2": 157.991, "r_y2": 198.404, "r_x3": 132.365, "r_y3": 198.404, "coord_origin": "TOPLEFT"}, "text": "human", "orig": "human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 401.077, "r_x1": 140.362, "r_y1": 401.077, "r_x2": 140.362, "r_y2": 393.276, "r_x3": 53.798, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.99, "r_y0": 401.121, "r_x1": 175.969, "r_y1": 401.121, "r_x2": 175.969, "r_y2": 393.213, "r_x3": 142.99, "r_y3": 393.213, "coord_origin": "TOPLEFT"}, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.951, "r_y0": 401.077, "r_x1": 294.041, "r_y1": 401.077, "r_x2": 294.041, "r_y2": 393.276, "r_x3": 178.951, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 412.036, "r_x1": 295.425, "r_y1": 412.036, "r_x2": 295.425, "r_y2": 404.235, "r_x3": 53.798, "r_y3": 404.235, "coord_origin": "TOPLEFT"}, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 422.995, "r_x1": 294.043, "r_y1": 422.995, "r_x2": 294.043, "r_y2": 415.194, "r_x3": 53.529, "r_y3": 415.194, "coord_origin": "TOPLEFT"}, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool 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"coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 385.937, "r_y0": 252.75400000000002, "r_x1": 392.569, "r_y1": 252.75400000000002, "r_x2": 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"coord_origin": "TOPLEFT"}, "text": "ing fractions of the DocLayNet dataset. The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, 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295.556, "b": 576.42, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 379.159, "r_x1": 294.047, "r_y1": 379.159, "r_x2": 294.047, "r_y2": 371.358, "r_x3": 53.798, "r_y3": 371.358, "coord_origin": "TOPLEFT"}, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 390.118, "r_x1": 295.556, "r_y1": 390.118, "r_x2": 295.556, "r_y2": 382.317, "r_x3": 53.798, "r_y3": 382.317, "coord_origin": "TOPLEFT"}, "text": "numbers for human document-layout annotation. Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 401.077, "r_x1": 140.362, "r_y1": 401.077, "r_x2": 140.362, "r_y2": 393.276, "r_x3": 53.798, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.99, "r_y0": 401.121, "r_x1": 175.969, "r_y1": 401.121, "r_x2": 175.969, "r_y2": 393.213, "r_x3": 142.99, "r_y3": 393.213, "coord_origin": "TOPLEFT"}, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.951, "r_y0": 401.077, "r_x1": 294.041, "r_y1": 401.077, "r_x2": 294.041, "r_y2": 393.276, "r_x3": 178.951, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 412.036, "r_x1": 295.425, "r_y1": 412.036, "r_x2": 295.425, "r_y2": 404.235, "r_x3": 53.798, "r_y3": 404.235, "coord_origin": "TOPLEFT"}, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 422.995, "r_x1": 294.043, "r_y1": 422.995, "r_x2": 294.043, "r_y2": 415.194, "r_x3": 53.529, "r_y3": 415.194, "coord_origin": "TOPLEFT"}, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 433.954, "r_x1": 294.048, "r_y1": 433.954, "r_x2": 294.048, "r_y2": 426.153, "r_x3": 53.798, "r_y3": 426.153, "coord_origin": "TOPLEFT"}, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 444.913, "r_x1": 256.806, "r_y1": 444.913, "r_x2": 256.806, "r_y2": 437.112, "r_x3": 53.798, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": 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1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.877, "r_y0": 455.872, "r_x1": 80.889, "r_y1": 455.872, "r_x2": 80.889, "r_y2": 448.071, "r_x3": 78.877, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.139, "r_y0": 455.872, "r_x1": 294.049, "r_y1": 455.872, "r_x2": 294.049, "r_y2": 448.071, "r_x3": 83.139, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": "For the latter, we instructed annotation staff to minimise", "orig": "For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 466.831, "r_x1": 294.046, "r_y1": 466.831, "r_x2": 294.046, "r_y2": 459.03, "r_x3": 53.798, "r_y3": 459.03, "coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 589.068, "r": 147.485, "b": 598.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.484, "t": 613.684, "r": 295.428, "b": 709.156, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", 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The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 384.442, "r": 558.204, "b": 403.202, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.641, "t": 406.36, "r": 558.437, "b": 479.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 497.76, "r": 466.853, "b": 506.827, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 317.749, "t": 512.4549999999999, "r": 558.431, "b": 706.557, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, 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Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 182.76800000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9782760143280029, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 91.52, "r_y1": 106.05600000000004, "r_x2": 91.52, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "detection", "orig": "detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.976, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 95.976, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "networks on DocLayNet test set. The MRCNN", "orig": "networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.52, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 202.434, "r_y1": 138.933, "r_x2": 202.434, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.085, "r_y0": 138.95899999999995, "r_x1": 247.142, "r_y1": 138.95899999999995, "r_x2": 247.142, "r_y2": 131.48000000000002, "r_x3": 206.085, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.95, "r_y0": 138.933, "r_x1": 294.043, "r_y1": 138.933, "r_x2": 294.043, "r_y2": 131.48000000000002, "r_x3": 250.95, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 149.89200000000005, "r_x1": 294.044, "r_y1": 149.89200000000005, "r_x2": 294.044, "r_y2": 142.43899999999996, "r_x3": 53.52, "r_y3": 142.43899999999996, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 160.851, "r_x1": 294.044, "r_y1": 160.851, "r_x2": 294.044, "r_y2": 153.39800000000002, "r_x3": 53.798, "r_y3": 153.39800000000002, "coord_origin": "TOPLEFT"}, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 171.80999999999995, "r_x1": 295.649, "r_y1": 171.80999999999995, "r_x2": 295.649, "r_y2": 164.35699999999997, "r_x3": 53.798, "r_y3": 164.35699999999997, "coord_origin": "TOPLEFT"}, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 182.76800000000003, "r_x1": 268.624, "r_y1": 182.76800000000003, "r_x2": 268.624, "r_y2": 175.31500000000005, "r_x3": 53.798, "r_y3": 175.31500000000005, "coord_origin": "TOPLEFT"}, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. 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"start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "76.8", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "text", "bbox": {"l": 53.529, "t": 371.358, "r": 295.556, "b": 576.42, "coord_origin": "TOPLEFT"}, "confidence": 0.9866557121276855, "cells": [{"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 379.159, "r_x1": 294.047, "r_y1": 379.159, "r_x2": 294.047, "r_y2": 371.358, "r_x3": 53.798, "r_y3": 371.358, "coord_origin": "TOPLEFT"}, "text": "to avoid this at any cost in order to have clear, unbiased baseline", "orig": "to avoid this at any cost in order to have clear, unbiased baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 390.118, "r_x1": 295.556, "r_y1": 390.118, "r_x2": 295.556, "r_y2": 382.317, "r_x3": 53.798, "r_y3": 382.317, "coord_origin": "TOPLEFT"}, "text": "numbers for human document-layout annotation. Third, we in-", "orig": "numbers for human document-layout annotation. Third, we in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 401.077, "r_x1": 140.362, "r_y1": 401.077, "r_x2": 140.362, "r_y2": 393.276, "r_x3": 53.798, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "troduced the feature of", "orig": "troduced the feature of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.99, "r_y0": 401.121, "r_x1": 175.969, "r_y1": 401.121, "r_x2": 175.969, "r_y2": 393.213, "r_x3": 142.99, "r_y3": 393.213, "coord_origin": "TOPLEFT"}, "text": "snapping", "orig": "snapping", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.951, "r_y0": 401.077, "r_x1": 294.041, "r_y1": 401.077, "r_x2": 294.041, "r_y2": 393.276, "r_x3": 178.951, "r_y3": 393.276, "coord_origin": "TOPLEFT"}, "text": "boxes around text segments to", "orig": "boxes around text segments to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 412.036, "r_x1": 295.425, "r_y1": 412.036, "r_x2": 295.425, "r_y2": 404.235, "r_x3": 53.798, "r_y3": 404.235, "coord_origin": "TOPLEFT"}, "text": "obtain a pixel-accurate annotation and again reduce time and effort.", "orig": "obtain a pixel-accurate annotation and again reduce time and effort.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 422.995, "r_x1": 294.043, "r_y1": 422.995, "r_x2": 294.043, "r_y2": 415.194, "r_x3": 53.529, "r_y3": 415.194, "coord_origin": "TOPLEFT"}, "text": "The CCS annotation tool automatically shrinks every user-drawn", "orig": "The CCS annotation tool automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 433.954, "r_x1": 294.048, "r_y1": 433.954, "r_x2": 294.048, "r_y2": 426.153, "r_x3": 53.798, "r_y3": 426.153, "coord_origin": "TOPLEFT"}, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 444.913, "r_x1": 256.806, "r_y1": 444.913, "r_x2": 256.806, "r_y2": 437.112, "r_x3": 53.798, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 259.042, "r_y0": 444.957, "r_x1": 278.105, "r_y1": 444.957, "r_x2": 278.105, "r_y2": 437.049, "r_x3": 259.042, "r_y3": 437.049, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.55, "r_y0": 444.913, "r_x1": 294.044, "r_y1": 444.913, "r_x2": 294.044, "r_y2": 437.112, "r_x3": 280.55, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 455.916, "r_x1": 78.876, "r_y1": 455.916, "r_x2": 78.876, "r_y2": 448.008, "r_x3": 53.798, "r_y3": 448.008, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.877, "r_y0": 455.872, "r_x1": 80.889, "r_y1": 455.872, "r_x2": 80.889, "r_y2": 448.071, "r_x3": 78.877, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.139, "r_y0": 455.872, "r_x1": 294.049, "r_y1": 455.872, "r_x2": 294.049, "r_y2": 448.071, "r_x3": 83.139, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": "For the latter, we instructed annotation staff to minimise", "orig": "For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 466.831, "r_x1": 294.046, "r_y1": 466.831, "r_x2": 294.046, "r_y2": 459.03, "r_x3": 53.798, "r_y3": 459.03, "coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 589.068, "r": 147.485, "b": 598.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484, "t": 613.684, "r": 295.428, "b": 709.156, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 385.937, "r_y0": 252.75400000000002, "r_x1": 392.569, "r_y1": 252.75400000000002, "r_x2": 392.569, "r_y2": 246.67999999999995, "r_x3": 385.937, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "20", 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The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 384.442, "r": 558.204, "b": 403.202, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.641, "t": 406.36, "r": 558.437, "b": 479.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 497.76, "r": 466.853, "b": 506.827, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.749, "t": 512.4549999999999, "r": 558.431, "b": 706.557, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, 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"TOPLEFT"}, "text": "the most visually distinctive in a document.", "orig": "the most visually distinctive in a document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "body": [{"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 182.76800000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9782760143280029, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "orig": "Table 2: Prediction performance (mAP@0.5-0.95) of object", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 91.52, "r_y1": 106.05600000000004, "r_x2": 91.52, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "detection", "orig": "detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 95.976, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 95.976, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "networks on DocLayNet test set. The MRCNN", "orig": "networks on DocLayNet test set. The MRCNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.52, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "orig": "(Mask R-CNN) and FRCNN (Faster R-CNN) models with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ResNet-50 or ResNet-101 backbone were trained based on", "orig": "ResNet-50 or ResNet-101 backbone were trained based on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 202.434, "r_y1": 138.933, "r_x2": 202.434, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "the network architectures from the", "orig": "the network architectures from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.085, "r_y0": 138.95899999999995, "r_x1": 247.142, "r_y1": 138.95899999999995, "r_x2": 247.142, "r_y2": 131.48000000000002, "r_x3": 206.085, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.95, "r_y0": 138.933, "r_x1": 294.043, "r_y1": 138.933, "r_x2": 294.043, "r_y2": 131.48000000000002, "r_x3": 250.95, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "model zoo", "orig": "model zoo", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.52, "r_y0": 149.89200000000005, "r_x1": 294.044, "r_y1": 149.89200000000005, "r_x2": 294.044, "r_y2": 142.43899999999996, "r_x3": 53.52, "r_y3": 142.43899999999996, "coord_origin": "TOPLEFT"}, "text": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "orig": "(Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 160.851, "r_x1": 294.044, "r_y1": 160.851, "r_x2": 294.044, "r_y2": 153.39800000000002, "r_x3": 53.798, "r_y3": 153.39800000000002, "coord_origin": "TOPLEFT"}, "text": "3x), with default configurations. The YOLO implementation", "orig": "3x), with default configurations. The YOLO implementation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 171.80999999999995, "r_x1": 295.649, "r_y1": 171.80999999999995, "r_x2": 295.649, "r_y2": 164.35699999999997, "r_x3": 53.798, "r_y3": 164.35699999999997, "coord_origin": "TOPLEFT"}, "text": "utilized was YOLOv5x6 [13]. All models were initialised us-", "orig": "utilized was YOLOv5x6 [13]. All models were initialised us-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 182.76800000000003, "r_x1": 268.624, "r_y1": 182.76800000000003, "r_x2": 268.624, "r_y2": 175.31500000000005, "r_x3": 53.798, "r_y3": 175.31500000000005, "coord_origin": "TOPLEFT"}, "text": "ing pre-trained weights from the COCO 2017 dataset.", "orig": "ing pre-trained weights from the COCO 2017 dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. 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automatically shrinks every user-drawn", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 433.954, "r_x1": 294.048, "r_y1": 433.954, "r_x2": 294.048, "r_y2": 426.153, "r_x3": 53.798, "r_y3": 426.153, "coord_origin": "TOPLEFT"}, "text": "box to the minimum bounding-box around the enclosed text-cells", "orig": "box to the minimum bounding-box around the enclosed text-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 444.913, "r_x1": 256.806, "r_y1": 444.913, "r_x2": 256.806, "r_y2": 437.112, "r_x3": 53.798, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "for all purely text-based segments, which excludes only", "orig": "for all purely text-based segments, which excludes only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 259.042, "r_y0": 444.957, "r_x1": 278.105, "r_y1": 444.957, "r_x2": 278.105, "r_y2": 437.049, "r_x3": 259.042, "r_y3": 437.049, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.55, "r_y0": 444.913, "r_x1": 294.044, "r_y1": 444.913, "r_x2": 294.044, "r_y2": 437.112, "r_x3": 280.55, "r_y3": 437.112, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 455.916, "r_x1": 78.876, "r_y1": 455.916, "r_x2": 78.876, "r_y2": 448.008, "r_x3": 53.798, "r_y3": 448.008, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 78.877, "r_y0": 455.872, "r_x1": 80.889, "r_y1": 455.872, "r_x2": 80.889, "r_y2": 448.071, "r_x3": 78.877, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.139, "r_y0": 455.872, "r_x1": 294.049, "r_y1": 455.872, "r_x2": 294.049, "r_y2": 448.071, "r_x3": 83.139, "r_y3": 448.071, "coord_origin": "TOPLEFT"}, "text": "For the latter, we instructed annotation staff to minimise", "orig": "For the latter, we instructed annotation staff to minimise", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 466.831, "r_x1": 294.046, "r_y1": 466.831, "r_x2": 294.046, "r_y2": 459.03, "r_x3": 53.798, "r_y3": 459.03, "coord_origin": "TOPLEFT"}, "text": "inclusion of surrounding whitespace while including all graphical", "orig": "inclusion of surrounding whitespace while including all graphical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 477.79, "r_x1": 294.042, "r_y1": 477.79, "r_x2": 294.042, "r_y2": 469.989, "r_x3": 53.798, "r_y3": 469.989, "coord_origin": "TOPLEFT"}, "text": "lines. A downside of snapping boxes to enclosed text cells is that", "orig": "lines. A downside of snapping boxes to enclosed text cells is that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 488.748, "r_x1": 294.045, "r_y1": 488.748, "r_x2": 294.045, "r_y2": 480.947, "r_x3": 53.798, "r_y3": 480.947, "coord_origin": "TOPLEFT"}, "text": "some wrongly parsed PDF pages cannot be annotated correctly and", "orig": "some wrongly parsed PDF pages cannot be annotated correctly and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 499.707, "r_x1": 294.043, "r_y1": 499.707, "r_x2": 294.043, "r_y2": 491.906, "r_x3": 53.798, "r_y3": 491.906, "coord_origin": "TOPLEFT"}, "text": "need to be skipped. Fourth, we established a way to flag pages as", "orig": "need to be skipped. Fourth, we established a way to flag pages as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 510.71, "r_x1": 80.598, "r_y1": 510.71, "r_x2": 80.598, "r_y2": 502.802, "r_x3": 53.798, "r_y3": 502.802, "coord_origin": "TOPLEFT"}, "text": "rejected", "orig": "rejected", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 83.367, "r_y0": 510.666, "r_x1": 294.045, "r_y1": 510.666, "r_x2": 294.045, "r_y2": 502.865, "r_x3": 83.367, "r_y3": 502.865, "coord_origin": "TOPLEFT"}, "text": "for cases where no valid annotation according to the label", "orig": "for cases where no valid annotation according to the label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 521.625, "r_x1": 294.258, "r_y1": 521.625, "r_x2": 294.258, "r_y2": 513.8240000000001, "r_x3": 53.798, "r_y3": 513.8240000000001, "coord_origin": "TOPLEFT"}, "text": "guidelines could be achieved. Example cases for this would be PDF", "orig": "guidelines could be achieved. Example cases for this would be PDF", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 532.5840000000001, "r_x1": 294.045, "r_y1": 532.5840000000001, "r_x2": 294.045, "r_y2": 524.783, "r_x3": 53.798, "r_y3": 524.783, "coord_origin": "TOPLEFT"}, "text": "pages that render incorrectly or contain layouts that are impossible", "orig": "pages that render incorrectly or contain layouts that are impossible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 543.543, "r_x1": 294.045, "r_y1": 543.543, "r_x2": 294.045, "r_y2": 535.742, "r_x3": 53.798, "r_y3": 535.742, "coord_origin": "TOPLEFT"}, "text": "to capture with non-overlapping rectangles. Such rejected pages are", "orig": "to capture with non-overlapping rectangles. Such rejected pages are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 554.502, "r_x1": 295.028, "r_y1": 554.502, "r_x2": 295.028, "r_y2": 546.701, "r_x3": 53.798, "r_y3": 546.701, "coord_origin": "TOPLEFT"}, "text": "not contained in the final dataset. With all these measures in place,", "orig": "not contained in the final dataset. With all these measures in place,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 565.461, "r_x1": 294.049, "r_y1": 565.461, "r_x2": 294.049, "r_y2": 557.66, "r_x3": 53.798, "r_y3": 557.66, "coord_origin": "TOPLEFT"}, "text": "experienced annotation staff managed to annotate a single page in", "orig": "experienced annotation staff managed to annotate a single page in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.42, "r_x1": 281.805, "r_y1": 576.42, "r_x2": 281.805, "r_y2": 568.619, "r_x3": 53.798, "r_y3": 568.619, "coord_origin": "TOPLEFT"}, "text": "a typical timeframe of 20s to 60s, depending on its complexity.", "orig": "a typical timeframe of 20s to 60s, depending on its complexity.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity."}, {"label": "section_header", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 589.068, "r": 147.485, "b": 598.135, "coord_origin": "TOPLEFT"}, "confidence": 0.9419495463371277, "cells": [{"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.135, "r_x1": 59.405, "r_y1": 598.135, "r_x2": 59.405, "r_y2": 589.068, "r_x3": 53.798, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.314, "r_y0": 598.135, "r_x1": 147.485, "r_y1": 598.135, "r_x2": 147.485, "r_y2": 589.068, "r_x3": 70.314, "r_y3": 589.068, "coord_origin": "TOPLEFT"}, "text": "EXPERIMENTS", "orig": "EXPERIMENTS", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5 EXPERIMENTS"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.484, "t": 613.684, "r": 295.428, "b": 709.156, "coord_origin": "TOPLEFT"}, "confidence": 0.987605094909668, "cells": [{"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 621.485, "r_x1": 294.049, "r_y1": 621.485, "r_x2": 294.049, "r_y2": 613.684, "r_x3": 53.529, "r_y3": 613.684, "coord_origin": "TOPLEFT"}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models", "orig": "The primary goal of DocLayNet is to obtain high-quality ML models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 632.444, "r_x1": 294.276, "r_y1": 632.444, "r_x2": 294.276, "r_y2": 624.643, "r_x3": 53.798, "r_y3": 624.643, "coord_origin": "TOPLEFT"}, "text": "capable of accurate document-layout analysis on a wide variety", "orig": "capable of accurate document-layout analysis on a wide variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 643.403, "r_x1": 294.041, "r_y1": 643.403, "r_x2": 294.041, "r_y2": 635.602, "r_x3": 53.798, "r_y3": 635.602, "coord_origin": "TOPLEFT"}, "text": "of challenging layouts. As discussed in Section 2, object detection", "orig": "of challenging layouts. As discussed in Section 2, object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 654.362, "r_x1": 294.042, "r_y1": 654.362, "r_x2": 294.042, "r_y2": 646.561, "r_x3": 53.798, "r_y3": 646.561, "coord_origin": "TOPLEFT"}, "text": "models are currently the easiest to use, due to the standardisation", "orig": "models are currently the easiest to use, due to the standardisation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 665.3199999999999, "r_x1": 294.041, "r_y1": 665.3199999999999, "r_x2": 294.041, "r_y2": 657.519, "r_x3": 53.798, "r_y3": 657.519, "coord_origin": "TOPLEFT"}, "text": "of ground-truth data in COCO format [16] and the availability of", "orig": "of ground-truth data in COCO format [16] and the availability of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 676.279, "r_x1": 155.011, "r_y1": 676.279, "r_x2": 155.011, "r_y2": 668.478, "r_x3": 53.798, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "general frameworks such as", "orig": "general frameworks such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.236, "r_y0": 676.323, "r_x1": 193.267, "r_y1": 676.323, "r_x2": 193.267, "r_y2": 668.415, "r_x3": 157.236, "r_y3": 668.415, "coord_origin": "TOPLEFT"}, "text": "detectron2", "orig": "detectron2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.867, "r_y0": 676.279, "r_x1": 294.047, "r_y1": 676.279, "r_x2": 294.047, "r_y2": 668.478, "r_x3": 195.867, "r_y3": 668.478, "coord_origin": "TOPLEFT"}, "text": "[17]. Furthermore, baseline", "orig": "[17]. Furthermore, baseline", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 687.238, "r_x1": 294.045, "r_y1": 687.238, "r_x2": 294.045, "r_y2": 679.437, "r_x3": 53.798, "r_y3": 679.437, "coord_origin": "TOPLEFT"}, "text": "numbers in PubLayNet and DocBank were obtained using standard", "orig": "numbers in PubLayNet and DocBank were obtained using standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 698.197, "r_x1": 295.428, "r_y1": 698.197, "r_x2": 295.428, "r_y2": 690.396, "r_x3": 53.798, "r_y3": 690.396, "coord_origin": "TOPLEFT"}, "text": "object detection models such as Mask R-CNN and Faster R-CNN.", "orig": "object detection models such as Mask R-CNN and Faster R-CNN.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 709.156, "r_x1": 294.044, "r_y1": 709.156, "r_x2": 294.044, "r_y2": 701.355, "r_x3": 53.484, "r_y3": 701.355, "coord_origin": "TOPLEFT"}, "text": "As such, we will relate to these object detection methods in this", "orig": "As such, we will relate to these object detection methods in this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this"}, {"label": "picture", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 323.48431396484375, "t": 89.88603210449219, "r": 553.5411376953125, "b": 260.0107727050781, "coord_origin": "TOPLEFT"}, "confidence": 0.9782691597938538, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 349.166, "r_y0": 252.75400000000002, "r_x1": 352.482, "r_y1": 252.75400000000002, "r_x2": 352.482, "r_y2": 246.67999999999995, "r_x3": 349.166, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "0", "orig": "0", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 385.937, "r_y0": 252.75400000000002, "r_x1": 392.569, "r_y1": 252.75400000000002, "r_x2": 392.569, "r_y2": 246.67999999999995, "r_x3": 385.937, "r_y3": 246.67999999999995, "coord_origin": "TOPLEFT"}, "text": "20", 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null, "predicted_class": null, "confidence": null}, {"label": "caption", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 317.955, "t": 279.78700000000003, "r": 559.806, "b": 342.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9639935493469238, "cells": [{"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 287.24, "r_x1": 558.479, "r_y1": 287.24, "r_x2": 558.479, "r_y2": 279.78700000000003, "r_x3": 317.955, "r_y3": 279.78700000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "orig": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 298.199, "r_x1": 559.806, "r_y1": 298.199, "r_x2": 559.806, "r_y2": 290.746, "r_x3": 317.955, "r_y3": 290.746, "coord_origin": "TOPLEFT"}, "text": "R-CNNnetworkwithResNet50backbonetrainedonincreas-", "orig": "R-CNNnetworkwithResNet50backbonetrainedonincreas-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 309.158, "r_x1": 558.201, "r_y1": 309.158, "r_x2": 558.201, "r_y2": 301.705, "r_x3": 317.955, "r_y3": 301.705, "coord_origin": "TOPLEFT"}, "text": "ing fractions of the DocLayNet dataset. The learning curve", "orig": "ing fractions of the DocLayNet dataset. The learning curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 320.117, "r_x1": 558.201, "r_y1": 320.117, "r_x2": 558.201, "r_y2": 312.664, "r_x3": 317.955, "r_y3": 312.664, "coord_origin": "TOPLEFT"}, "text": "flattens around the 80% mark, indicating that increasing the", "orig": "flattens around the 80% mark, indicating that increasing the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 157, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 331.076, "r_x1": 558.201, "r_y1": 331.076, "r_x2": 558.201, "r_y2": 323.623, "r_x3": 317.955, "r_y3": 323.623, "coord_origin": "TOPLEFT"}, "text": "size of the DocLayNet dataset with similar data will not yield", "orig": "size of the DocLayNet dataset with similar data will not yield", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 158, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 342.035, "r_x1": 445.242, "r_y1": 342.035, "r_x2": 445.242, "r_y2": 334.582, "r_x3": 317.955, "r_y3": 334.582, "coord_origin": "TOPLEFT"}, "text": "significantly better predictions.", "orig": "significantly better predictions.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNNnetworkwithResNet50backbonetrainedonincreasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 317.955, "t": 384.442, "r": 558.204, "b": 403.202, "coord_origin": "TOPLEFT"}, "confidence": 0.9594221711158752, "cells": [{"index": 159, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 392.243, "r_x1": 558.204, "r_y1": 392.243, "r_x2": 558.204, "r_y2": 384.442, "r_x3": 317.955, "r_y3": 384.442, "coord_origin": "TOPLEFT"}, "text": "paper and leave the detailed evaluation of more recent methods", "orig": "paper and leave the detailed evaluation of more recent methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 160, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 403.202, "r_x1": 463.049, "r_y1": 403.202, "r_x2": 463.049, "r_y2": 395.401, "r_x3": 317.955, "r_y3": 395.401, "coord_origin": "TOPLEFT"}, "text": "mentioned in Section 2 for future work.", "orig": "mentioned in Section 2 for future work.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.641, "t": 406.36, "r": 558.437, "b": 479.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9872915148735046, "cells": [{"index": 161, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 414.161, "r_x1": 558.198, "r_y1": 414.161, "r_x2": 558.198, "r_y2": 406.36, "r_x3": 327.918, "r_y3": 406.36, "coord_origin": "TOPLEFT"}, "text": "In this section, we will present several aspects related to the", "orig": "In this section, we will present several aspects related to the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 162, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 425.12, "r_x1": 558.437, "r_y1": 425.12, "r_x2": 558.437, "r_y2": 417.319, "r_x3": 317.955, "r_y3": 417.319, "coord_origin": "TOPLEFT"}, "text": "performance of object detection models on DocLayNet. Similarly", "orig": "performance of object detection models on DocLayNet. Similarly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 163, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 436.078, "r_x1": 558.206, "r_y1": 436.078, "r_x2": 558.206, "r_y2": 428.277, "r_x3": 317.955, "r_y3": 428.277, "coord_origin": "TOPLEFT"}, "text": "as in PubLayNet, we will evaluate the quality of their predictions", "orig": "as in PubLayNet, we will evaluate the quality of their predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 164, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 447.037, "r_x1": 558.2, "r_y1": 447.037, "r_x2": 558.2, "r_y2": 439.236, "r_x3": 317.955, "r_y3": 439.236, "coord_origin": "TOPLEFT"}, "text": "using mean average precision (mAP) with 10 overlaps that range", "orig": "using mean average precision (mAP) with 10 overlaps that range", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 165, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 457.996, "r_x1": 558.199, "r_y1": 457.996, "r_x2": 558.199, "r_y2": 450.195, "r_x3": 317.955, "r_y3": 450.195, "coord_origin": "TOPLEFT"}, "text": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "orig": "from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 166, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 468.955, "r_x1": 558.202, "r_y1": 468.955, "r_x2": 558.202, "r_y2": 461.154, "r_x3": 317.955, "r_y3": 461.154, "coord_origin": "TOPLEFT"}, "text": "computed by leveraging the evaluation code provided by the COCO", "orig": "computed by leveraging the evaluation code provided by the COCO", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 167, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.641, "r_y0": 479.914, "r_x1": 350.324, "r_y1": 479.914, "r_x2": 350.324, "r_y2": 472.113, "r_x3": 317.641, "r_y3": 472.113, "coord_origin": "TOPLEFT"}, "text": "API [16].", "orig": "API [16].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]."}, {"label": "section_header", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 497.76, "r": 466.853, "b": 506.827, "coord_origin": "TOPLEFT"}, "confidence": 0.9579005241394043, "cells": [{"index": 168, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 506.827, "r_x1": 466.853, "r_y1": 506.827, "r_x2": 466.853, "r_y2": 497.76, "r_x3": 317.955, "r_y3": 497.76, "coord_origin": "TOPLEFT"}, "text": "Baselines for Object Detection", "orig": "Baselines for Object Detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Baselines for Object Detection"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 317.749, "t": 512.4549999999999, "r": 558.431, "b": 706.557, "coord_origin": "TOPLEFT"}, "confidence": 0.987722635269165, "cells": [{"index": 169, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 520.256, "r_x1": 558.431, "r_y1": 520.256, "r_x2": 558.431, "r_y2": 512.4549999999999, "r_x3": 317.955, "r_y3": 512.4549999999999, "coord_origin": "TOPLEFT"}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask", "orig": "In Table 2, we present baseline experiments (given in mAP) on Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 170, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 531.2149999999999, "r_x1": 558.201, "r_y1": 531.2149999999999, "r_x2": 558.201, "r_y2": 523.414, "r_x3": 317.955, "r_y3": 523.414, "coord_origin": "TOPLEFT"}, "text": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "orig": "R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 171, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 542.174, "r_x1": 558.202, "r_y1": 542.174, "r_x2": 558.202, "r_y2": 534.373, "r_x3": 317.955, "r_y3": 534.373, "coord_origin": "TOPLEFT"}, "text": "and evaluation were performed on RGB images with dimensions of", "orig": "and evaluation were performed on RGB images with dimensions of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.749, "r_y0": 553.133, "r_x1": 334.093, "r_y1": 553.133, "r_x2": 334.093, "r_y2": 545.332, "r_x3": 317.749, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025", "orig": "1025", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.812, "r_y0": 552.55, "r_x1": 340.515, "r_y1": 552.55, "r_x2": 340.515, "r_y2": 544.848, "r_x3": 334.812, "r_y3": 544.848, "coord_origin": "TOPLEFT"}, "text": "\u00d7", "orig": "\u00d7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 341.233, "r_y0": 553.133, "r_x1": 558.201, "r_y1": 553.133, "r_x2": 558.201, "r_y2": 545.332, "r_x3": 341.233, "r_y3": 545.332, "coord_origin": "TOPLEFT"}, "text": "1025 pixels. For training, we only used one annotation in case", "orig": "1025 pixels. For training, we only used one annotation in case", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 564.091, "r_x1": 558.204, "r_y1": 564.091, "r_x2": 558.204, "r_y2": 556.29, "r_x3": 317.955, "r_y3": 556.29, "coord_origin": "TOPLEFT"}, "text": "of redundantly annotated pages. As one can observe, the variation", "orig": "of redundantly annotated pages. As one can observe, the variation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 575.05, "r_x1": 558.204, "r_y1": 575.05, "r_x2": 558.204, "r_y2": 567.249, "r_x3": 317.955, "r_y3": 567.249, "coord_origin": "TOPLEFT"}, "text": "in mAP between the models is rather low, but overall between 6", "orig": "in mAP between the models is rather low, but overall between 6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 586.009, "r_x1": 558.205, "r_y1": 586.009, "r_x2": 558.205, "r_y2": 578.208, "r_x3": 317.955, "r_y3": 578.208, "coord_origin": "TOPLEFT"}, "text": "and 10% lower than the mAP computed from the pairwise human", "orig": "and 10% lower than the mAP computed from the pairwise human", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 596.968, "r_x1": 558.202, "r_y1": 596.968, "r_x2": 558.202, "r_y2": 589.167, "r_x3": 317.955, "r_y3": 589.167, "coord_origin": "TOPLEFT"}, "text": "annotations on triple-annotated pages. This gives a good indication", "orig": "annotations on triple-annotated pages. This gives a good indication", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 607.927, "r_x1": 558.198, "r_y1": 607.927, "r_x2": 558.198, "r_y2": 600.126, "r_x3": 317.955, "r_y3": 600.126, "coord_origin": "TOPLEFT"}, "text": "that the DocLayNet dataset poses a worthwhile challenge for the", "orig": "that the DocLayNet dataset poses a worthwhile challenge for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.886, "r_x1": 558.205, "r_y1": 618.886, "r_x2": 558.205, "r_y2": 611.085, "r_x3": 317.955, "r_y3": 611.085, "coord_origin": "TOPLEFT"}, "text": "research community to close the gap between human recognition", "orig": "research community to close the gap between human recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 629.845, "r_x1": 558.203, "r_y1": 629.845, "r_x2": 558.203, "r_y2": 622.044, "r_x3": 317.955, "r_y3": 622.044, "coord_origin": "TOPLEFT"}, "text": "and ML approaches. It is interesting to see that Mask R-CNN and", "orig": "and ML approaches. It is interesting to see that Mask R-CNN and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 640.804, "r_x1": 558.204, "r_y1": 640.804, "r_x2": 558.204, "r_y2": 633.0029999999999, "r_x3": 317.955, "r_y3": 633.0029999999999, "coord_origin": "TOPLEFT"}, "text": "Faster R-CNN produce very comparable mAP scores, indicating", "orig": "Faster R-CNN produce very comparable mAP scores, indicating", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 651.763, "r_x1": 558.202, "r_y1": 651.763, "r_x2": 558.202, "r_y2": 643.962, "r_x3": 317.955, "r_y3": 643.962, "coord_origin": "TOPLEFT"}, "text": "that pixel-based image segmentation derived from bounding-boxes", "orig": "that pixel-based image segmentation derived from bounding-boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 662.722, "r_x1": 558.198, "r_y1": 662.722, "r_x2": 558.198, "r_y2": 654.921, "r_x3": 317.955, "r_y3": 654.921, "coord_origin": "TOPLEFT"}, "text": "does not help to obtain better predictions. On the other hand, the", "orig": "does not help to obtain better predictions. On the other hand, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 673.6800000000001, "r_x1": 558.204, "r_y1": 673.6800000000001, "r_x2": 558.204, "r_y2": 665.879, "r_x3": 317.955, "r_y3": 665.879, "coord_origin": "TOPLEFT"}, "text": "more recent Yolov5x model does very well and even out-performs", "orig": "more recent Yolov5x model does very well and even out-performs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 684.639, "r_x1": 444.541, "r_y1": 684.639, "r_x2": 444.541, "r_y2": 676.838, "r_x3": 317.955, "r_y3": 676.838, "coord_origin": "TOPLEFT"}, "text": "humans on selected labels such as", "orig": "humans on selected labels such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, 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"TOPLEFT"}, "text": "the most visually distinctive in a document.", "orig": "the most visually distinctive in a document.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 \u00d7 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document."}], "headers": [{"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8662876486778259, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. 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"orig": "78", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.275, "r_y0": 293.636, "r_x1": 253.614, "r_y1": 293.636, "r_x2": 253.614, "r_y2": 285.835, "r_x3": 245.275, "r_y3": 285.835, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 329.575, "r_x1": 131.056, "r_y1": 329.575, "r_x2": 131.056, "r_y2": 320.508, "r_x3": 53.798, "r_y3": 320.508, "coord_origin": "TOPLEFT"}, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", 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The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.488, "r_x1": 531.077, "r_y1": 708.488, "r_x2": 531.077, "r_y2": 700.687, "r_x3": 317.955, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 533.162, "r_y0": 708.532, "r_x1": 557.256, "r_y1": 708.532, "r_x2": 557.256, "r_y2": 700.624, "r_x3": 533.162, "r_y3": 700.624, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 557.255, "r_y0": 708.488, "r_x1": 559.188, "r_y1": 708.488, "r_x2": 559.188, "r_y2": 700.687, "r_x3": 557.255, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 12, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "orig": "mAP@0.5-0.95 scores trained on DocLayNet with different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 295.649, "r_y1": 117.01499999999999, "r_x2": 295.649, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "class label sets. The reduced label sets were obtained by ei-", "orig": "class label sets. 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"left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 335.202, "r": 295.558, "b": 529.3050000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 558.142, "r": 295.557, "b": 708.409, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 559.807, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.01499999999999, "r_x1": 455.114, "r_y1": 117.01499999999999, "r_x2": 455.114, "r_y2": 109.56200000000001, "r_x3": 317.955, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "Naive page-wise split will result", "orig": "Naive page-wise split will result", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 459.31, "r_y0": 117.01499999999999, "r_x1": 467.721, "r_y1": 117.01499999999999, "r_x2": 467.721, "r_y2": 109.56200000000001, "r_x3": 459.31, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.909, "r_y0": 116.19299999999998, "r_x1": 477.539, "r_y1": 116.19299999999998, "r_x2": 477.539, "r_y2": 104.81500000000005, "r_x3": 471.909, "r_y3": 104.81500000000005, "coord_origin": "TOPLEFT"}, "text": "/tildelow", "orig": "/tildelow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.54, "r_y0": 117.01499999999999, "r_x1": 559.807, "r_y1": 117.01499999999999, "r_x2": 559.807, "r_y2": 109.56200000000001, "r_x3": 477.54, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 127.97400000000005, "r_x1": 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in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 350.594, "r_x1": 558.2, "r_y1": 350.594, "r_x2": 558.2, "r_y2": 342.793, "r_x3": 317.955, "r_y3": 342.793, "coord_origin": "TOPLEFT"}, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "orig": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 361.553, "r_x1": 393.16, "r_y1": 361.553, "r_x2": 393.16, "r_y2": 353.752, "r_x3": 317.955, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "assumption that the", "orig": "assumption that the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.842, "r_y0": 361.597, "r_x1": 409.149, "r_y1": 361.597, "r_x2": 409.149, "r_y2": 353.689, "r_x3": 395.842, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 412.333, "r_y0": 361.553, "r_x1": 485.023, "r_y1": 361.553, "r_x2": 485.023, "r_y2": 353.752, "r_x3": 412.333, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "is down-mapped to", "orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.704, "r_y0": 361.597, "r_x1": 502.868, "r_y1": 361.597, "r_x2": 502.868, "r_y2": 353.689, "r_x3": 487.704, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 506.055, "r_y0": 361.553, "r_x1": 559.585, "r_y1": 361.553, "r_x2": 559.585, "r_y2": 353.752, "r_x3": 506.055, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 372.512, "r_x1": 558.2, "r_y1": 372.512, "r_x2": 558.2, "r_y2": 364.711, "r_x3": 317.686, "r_y3": 364.711, "coord_origin": "TOPLEFT"}, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 383.47, "r_x1": 558.369, "r_y1": 383.47, "r_x2": 558.369, "r_y2": 375.669, "r_x3": 317.955, "r_y3": 375.669, "coord_origin": "TOPLEFT"}, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 394.429, "r_x1": 558.2, "r_y1": 394.429, "r_x2": 558.2, "r_y2": 386.628, "r_x3": 317.955, "r_y3": 386.628, "coord_origin": "TOPLEFT"}, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 430.333, "r": 549.861, "b": 439.4, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 445.027, "r": 559.714, "b": 595.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 559.188, "b": 708.532, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. 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293.636, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "77", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 320.508, "r": 131.056, "b": 329.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9555686116218567, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 329.575, "r_x1": 131.056, "r_y1": 329.575, "r_x2": 131.056, "r_y2": 320.508, "r_x3": 53.798, "r_y3": 320.508, "coord_origin": "TOPLEFT"}, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 335.202, "r": 295.558, "b": 529.3050000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 558.142, "r": 295.557, "b": 708.409, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 559.807, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.01499999999999, "r_x1": 455.114, "r_y1": 117.01499999999999, "r_x2": 455.114, "r_y2": 109.56200000000001, "r_x3": 317.955, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "Naive page-wise split will result", "orig": "Naive page-wise split will result", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 459.31, "r_y0": 117.01499999999999, "r_x1": 467.721, "r_y1": 117.01499999999999, "r_x2": 467.721, "r_y2": 109.56200000000001, "r_x3": 459.31, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.909, "r_y0": 116.19299999999998, "r_x1": 477.539, "r_y1": 116.19299999999998, "r_x2": 477.539, "r_y2": 104.81500000000005, "r_x3": 471.909, "r_y3": 104.81500000000005, "coord_origin": "TOPLEFT"}, "text": "/tildelow", "orig": "/tildelow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.54, "r_y0": 117.01499999999999, "r_x1": 559.807, "r_y1": 117.01499999999999, "r_x2": 559.807, "r_y2": 109.56200000000001, "r_x3": 477.54, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 127.97400000000005, "r_x1": 341.375, "r_y1": 127.97400000000005, "r_x2": 341.375, "r_y2": 120.52099999999996, "r_x3": 317.955, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ment.", "orig": "ment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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"end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Title", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 426.524, "t": 285.436, "r": 434.863, "b": 293.237, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "77", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 452.562, "t": 285.436, "r": 460.901, "b": 293.237, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "81", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 358.639, "t": 296.793, "r": 369.605, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "All", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 426.524, "t": 296.793, "r": 434.863, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "72", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 452.562, "t": 296.793, "r": 460.901, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "84", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 478.594, "t": 296.793, "r": 486.933, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "78", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 504.632, "t": 296.793, "r": 512.971, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.834, "r": 559.585, "b": 416.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 350.594, "r_x1": 558.2, "r_y1": 350.594, "r_x2": 558.2, "r_y2": 342.793, "r_x3": 317.955, "r_y3": 342.793, "coord_origin": "TOPLEFT"}, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "orig": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 361.553, "r_x1": 393.16, "r_y1": 361.553, "r_x2": 393.16, "r_y2": 353.752, "r_x3": 317.955, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "assumption that the", "orig": "assumption that the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.842, "r_y0": 361.597, "r_x1": 409.149, "r_y1": 361.597, "r_x2": 409.149, "r_y2": 353.689, "r_x3": 395.842, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 412.333, "r_y0": 361.553, "r_x1": 485.023, "r_y1": 361.553, "r_x2": 485.023, "r_y2": 353.752, "r_x3": 412.333, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "is down-mapped to", "orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.704, "r_y0": 361.597, "r_x1": 502.868, "r_y1": 361.597, "r_x2": 502.868, "r_y2": 353.689, "r_x3": 487.704, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 506.055, "r_y0": 361.553, "r_x1": 559.585, "r_y1": 361.553, "r_x2": 559.585, "r_y2": 353.752, "r_x3": 506.055, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 372.512, "r_x1": 558.2, "r_y1": 372.512, "r_x2": 558.2, "r_y2": 364.711, "r_x3": 317.686, "r_y3": 364.711, "coord_origin": "TOPLEFT"}, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 383.47, "r_x1": 558.369, "r_y1": 383.47, "r_x2": 558.369, "r_y2": 375.669, "r_x3": 317.955, "r_y3": 375.669, "coord_origin": "TOPLEFT"}, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 394.429, "r_x1": 558.2, "r_y1": 394.429, "r_x2": 558.2, "r_y2": 386.628, "r_x3": 317.955, "r_y3": 386.628, "coord_origin": "TOPLEFT"}, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 430.333, "r": 549.861, "b": 439.4, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 445.027, "r": 559.714, "b": 595.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 559.188, "b": 708.532, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.488, "r_x1": 531.077, "r_y1": 708.488, "r_x2": 531.077, "r_y2": 700.687, "r_x3": 317.955, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 533.162, "r_y0": 708.532, "r_x1": 557.256, "r_y1": 708.532, "r_x2": 557.256, "r_y2": 700.624, "r_x3": 533.162, "r_y3": 700.624, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 557.255, "r_y0": 708.488, "r_x1": 559.188, "r_y1": 708.488, "r_x2": 559.188, "r_y2": 700.687, "r_x3": 557.255, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "body": [{"label": "text", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8296934366226196, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.044, "r_y1": 95.09699999999998, "r_x2": 294.044, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 3: Performance of a Mask R-CNN R50 network in", "orig": "Table 3: Performance of a Mask R-CNN R50 network in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 294.044, "r_y1": 106.05600000000004, "r_x2": 294.044, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "mAP@0.5-0.95 scores trained on DocLayNet with different", "orig": "mAP@0.5-0.95 scores trained on DocLayNet with different", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 295.649, "r_y1": 117.01499999999999, "r_x2": 295.649, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "class label sets. The reduced label sets were obtained by ei-", "orig": "class label sets. The reduced label sets were obtained by ei-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 213.239, "r_y1": 127.97400000000005, "r_x2": 213.239, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ther down-mapping or dropping labels.", "orig": "ther down-mapping or dropping labels.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. 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293.636, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "77", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "section_header", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "section_header", "bbox": {"l": 53.798, "t": 320.508, "r": 131.056, "b": 329.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9555686116218567, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 329.575, "r_x1": 131.056, "r_y1": 329.575, "r_x2": 131.056, "r_y2": 320.508, "r_x3": 53.798, "r_y3": 320.508, "coord_origin": "TOPLEFT"}, "text": "Learning Curve", "orig": "Learning Curve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Learning Curve"}, {"label": "text", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "text", "bbox": {"l": 52.785, "t": 335.202, "r": 295.558, "b": 529.3050000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9857739210128784, "cells": [{"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 343.003, "r_x1": 294.042, "r_y1": 343.003, "r_x2": 294.042, "r_y2": 335.202, "r_x3": 53.798, "r_y3": 335.202, "coord_origin": "TOPLEFT"}, "text": "One of the fundamental questions related to any dataset is if it is", "orig": "One of the fundamental questions related to any dataset is if it is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 52.785, "r_y0": 353.962, "r_x1": 295.558, "r_y1": 353.962, "r_x2": 295.558, "r_y2": 346.161, "r_x3": 52.785, "r_y3": 346.161, "coord_origin": "TOPLEFT"}, "text": "'large enough'. To answer this question for DocLayNet, we per-", "orig": "'large enough'. To answer this question for DocLayNet, we per-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 364.921, "r_x1": 294.045, "r_y1": 364.921, "r_x2": 294.045, "r_y2": 357.12, "r_x3": 53.798, "r_y3": 357.12, "coord_origin": "TOPLEFT"}, "text": "formed a data ablation study in which we evaluated a Mask R-CNN", "orig": "formed a data ablation study in which we evaluated a Mask R-CNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 375.88, "r_x1": 295.428, "r_y1": 375.88, "r_x2": 295.428, "r_y2": 368.079, "r_x3": 53.798, "r_y3": 368.079, "coord_origin": "TOPLEFT"}, "text": "model trained on increasing fractions of the DocLayNet dataset.", "orig": "model trained on increasing fractions of the DocLayNet dataset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.484, "r_y0": 386.839, "r_x1": 295.557, "r_y1": 386.839, "r_x2": 295.557, "r_y2": 379.038, "r_x3": 53.484, "r_y3": 379.038, "coord_origin": "TOPLEFT"}, "text": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "orig": "As can be seen in Figure 5, the mAP score rises sharply in the be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 397.798, "r_x1": 294.049, "r_y1": 397.798, "r_x2": 294.049, "r_y2": 389.997, "r_x3": 53.798, "r_y3": 389.997, "coord_origin": "TOPLEFT"}, "text": "ginning and eventually levels out. To estimate the error-bar on the", "orig": "ginning and eventually levels out. To estimate the error-bar on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 408.757, "r_x1": 294.044, "r_y1": 408.757, "r_x2": 294.044, "r_y2": 400.956, "r_x3": 53.798, "r_y3": 400.956, "coord_origin": "TOPLEFT"}, "text": "metrics, we ran the training five times on the entire data-set. This", "orig": "metrics, we ran the training five times on the entire data-set. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 419.716, "r_x1": 295.425, "r_y1": 419.716, "r_x2": 295.425, "r_y2": 411.915, "r_x3": 53.798, "r_y3": 411.915, "coord_origin": "TOPLEFT"}, "text": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "orig": "resulted in a 1% error-bar, depicted by the shaded area in Figure 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 430.675, "r_x1": 294.047, "r_y1": 430.675, "r_x2": 294.047, "r_y2": 422.874, "r_x3": 53.798, "r_y3": 422.874, "coord_origin": "TOPLEFT"}, "text": "In the inset of Figure 5, we show the exact same data-points, but", "orig": "In the inset of Figure 5, we show the exact same data-points, but", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 441.634, "r_x1": 294.045, "r_y1": 441.634, "r_x2": 294.045, "r_y2": 433.833, "r_x3": 53.467, "r_y3": 433.833, "coord_origin": "TOPLEFT"}, "text": "with a logarithmic scale on the x-axis. As is expected, the mAP", "orig": "with a logarithmic scale on the x-axis. As is expected, the mAP", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 452.592, "r_x1": 295.429, "r_y1": 452.592, "r_x2": 295.429, "r_y2": 444.791, "r_x3": 53.798, "r_y3": 444.791, "coord_origin": "TOPLEFT"}, "text": "score increases linearly as a function of the data-size in the inset.", "orig": "score increases linearly as a function of the data-size in the inset.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 463.551, "r_x1": 295.031, "r_y1": 463.551, "r_x2": 295.031, "r_y2": 455.75, "r_x3": 53.529, "r_y3": 455.75, "coord_origin": "TOPLEFT"}, "text": "The curve ultimately flattens out between the 80% and 100% mark,", "orig": "The curve ultimately flattens out between the 80% and 100% mark,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 474.51, "r_x1": 295.422, "r_y1": 474.51, "r_x2": 295.422, "r_y2": 466.709, "r_x3": 53.467, "r_y3": 466.709, "coord_origin": "TOPLEFT"}, "text": "with the 80% mark falling within the error-bars of the 100% mark.", "orig": "with the 80% mark falling within the error-bars of the 100% mark.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 485.469, "r_x1": 294.046, "r_y1": 485.469, "r_x2": 294.046, "r_y2": 477.668, "r_x3": 53.529, "r_y3": 477.668, "coord_origin": "TOPLEFT"}, "text": "This provides a good indication that the model would not improve", "orig": "This provides a good indication that the model would not improve", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 496.428, "r_x1": 295.556, "r_y1": 496.428, "r_x2": 295.556, "r_y2": 488.627, "r_x3": 53.798, "r_y3": 488.627, "coord_origin": "TOPLEFT"}, "text": "significantly by yet increasing the data size. Rather, it would prob-", "orig": "significantly by yet increasing the data size. Rather, it would prob-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 507.387, "r_x1": 294.047, "r_y1": 507.387, "r_x2": 294.047, "r_y2": 499.586, "r_x3": 53.798, "r_y3": 499.586, "coord_origin": "TOPLEFT"}, "text": "ably benefit more from improved data consistency (as discussed", "orig": "ably benefit more from improved data consistency (as discussed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 518.346, "r_x1": 294.042, "r_y1": 518.346, "r_x2": 294.042, "r_y2": 510.545, "r_x3": 53.798, "r_y3": 510.545, "coord_origin": "TOPLEFT"}, "text": "in Section 3), data augmentation methods [23], or the addition of", "orig": "in Section 3), data augmentation methods [23], or the addition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 529.3050000000001, "r_x1": 191.477, "r_y1": 529.3050000000001, "r_x2": 191.477, "r_y2": 521.504, "r_x3": 53.798, "r_y3": 521.504, "coord_origin": "TOPLEFT"}, "text": "more document categories and styles.", "orig": "more document categories and styles.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "One of the fundamental questions related to any dataset is if it is 'large enough'. To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles."}, {"label": "section_header", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 543.448, "r": 164.329, "b": 552.515, "coord_origin": "TOPLEFT"}, "confidence": 0.9572026133537292, "cells": [{"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.515, "r_x1": 164.329, "r_y1": 552.515, "r_x2": 164.329, "r_y2": 543.448, "r_x3": 53.798, "r_y3": 543.448, "coord_origin": "TOPLEFT"}, "text": "Impact of Class Labels", "orig": "Impact of Class Labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Class Labels"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 558.142, "r": 295.557, "b": 708.409, "coord_origin": "TOPLEFT"}, "confidence": 0.9876301884651184, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 565.943, "r_x1": 294.043, "r_y1": 565.943, "r_x2": 294.043, "r_y2": 558.142, "r_x3": 53.529, "r_y3": 558.142, "coord_origin": "TOPLEFT"}, "text": "The choice and number of labels can have a significant effect on", "orig": "The choice and number of labels can have a significant effect on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 576.902, "r_x1": 294.047, "r_y1": 576.902, "r_x2": 294.047, "r_y2": 569.101, "r_x3": 53.798, "r_y3": 569.101, "coord_origin": "TOPLEFT"}, "text": "the overall model performance. Since PubLayNet, DocBank and", "orig": "the overall model performance. Since PubLayNet, DocBank and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 587.861, "r_x1": 294.045, "r_y1": 587.861, "r_x2": 294.045, "r_y2": 580.06, "r_x3": 53.798, "r_y3": 580.06, "coord_origin": "TOPLEFT"}, "text": "DocLayNet all have different label sets, it is of particular interest to", "orig": "DocLayNet all have different label sets, it is of particular interest to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 598.8199999999999, "r_x1": 294.045, "r_y1": 598.8199999999999, "r_x2": 294.045, "r_y2": 591.019, "r_x3": 53.798, "r_y3": 591.019, "coord_origin": "TOPLEFT"}, "text": "understand and quantify this influence of the label set on the model", "orig": "understand and quantify this influence of the label set on the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 609.779, "r_x1": 294.043, "r_y1": 609.779, "r_x2": 294.043, "r_y2": 601.9780000000001, "r_x3": 53.798, "r_y3": 601.9780000000001, "coord_origin": "TOPLEFT"}, "text": "performance. We investigate this by either down-mapping labels", "orig": "performance. We investigate this by either down-mapping labels", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 620.738, "r_x1": 163.592, "r_y1": 620.738, "r_x2": 163.592, "r_y2": 612.937, "r_x3": 53.798, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": "into more common ones (e.g.", "orig": "into more common ones (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.264, "r_y0": 620.782, "r_x1": 194.972, "r_y1": 620.782, "r_x2": 194.972, "r_y2": 612.874, "r_x3": 166.264, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Caption", "orig": "Caption", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.994, "r_y0": 620.155, "r_x1": 204.176, "r_y1": 620.155, "r_x2": 204.176, "r_y2": 612.453, "r_x3": 194.994, "r_y3": 612.453, "coord_origin": "TOPLEFT"}, "text": "\u2192", "orig": "\u2192", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.176, "r_y0": 620.782, "r_x1": 219.34, "r_y1": 620.782, "r_x2": 219.34, "r_y2": 612.874, "r_x3": 204.176, "r_y3": 612.874, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 219.849, "r_y0": 620.738, "r_x1": 294.048, "r_y1": 620.738, "r_x2": 294.048, "r_y2": 612.937, "r_x3": 219.849, "r_y3": 612.937, "coord_origin": "TOPLEFT"}, "text": ") or excluding them", "orig": ") or excluding them", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 631.697, "r_x1": 294.047, "r_y1": 631.697, "r_x2": 294.047, "r_y2": 623.896, "r_x3": 53.798, "r_y3": 623.896, "coord_origin": "TOPLEFT"}, "text": "from the annotations entirely. Furthermore, it must be stressed", "orig": "from the annotations entirely. Furthermore, it must be stressed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 642.655, "r_x1": 295.557, "r_y1": 642.655, "r_x2": 295.557, "r_y2": 634.854, "r_x3": 53.798, "r_y3": 634.854, "coord_origin": "TOPLEFT"}, "text": "that all mappings and exclusions were performed on the data be-", "orig": "that all mappings and exclusions were performed on the data be-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 653.614, "r_x1": 294.047, "r_y1": 653.614, "r_x2": 294.047, "r_y2": 645.813, "r_x3": 53.798, "r_y3": 645.813, "coord_origin": "TOPLEFT"}, "text": "fore model training. In Table 3, we present the mAP scores for a", "orig": "fore model training. In Table 3, we present the mAP scores for a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 664.573, "r_x1": 294.047, "r_y1": 664.573, "r_x2": 294.047, "r_y2": 656.7719999999999, "r_x3": 53.798, "r_y3": 656.7719999999999, "coord_origin": "TOPLEFT"}, "text": "Mask R-CNN R50 network on different label sets. Where a label", "orig": "Mask R-CNN R50 network on different label sets. Where a label", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 675.532, "r_x1": 294.047, "r_y1": 675.532, "r_x2": 294.047, "r_y2": 667.731, "r_x3": 53.798, "r_y3": 667.731, "coord_origin": "TOPLEFT"}, "text": "is down-mapped, we show its corresponding label, otherwise it", "orig": "is down-mapped, we show its corresponding label, otherwise it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 686.491, "r_x1": 294.252, "r_y1": 686.491, "r_x2": 294.252, "r_y2": 678.69, "r_x3": 53.467, "r_y3": 678.69, "coord_origin": "TOPLEFT"}, "text": "was excluded. We present three different label sets, with 6, 5 and 4", "orig": "was excluded. We present three different label sets, with 6, 5 and 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 697.45, "r_x1": 294.046, "r_y1": 697.45, "r_x2": 294.046, "r_y2": 689.649, "r_x3": 53.798, "r_y3": 689.649, "coord_origin": "TOPLEFT"}, "text": "different labels respectively. The set of 5 labels contains the same", "orig": "different labels respectively. The set of 5 labels contains the same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 708.409, "r_x1": 294.047, "r_y1": 708.409, "r_x2": 294.047, "r_y2": 700.608, "r_x3": 53.798, "r_y3": 700.608, "coord_origin": "TOPLEFT"}, "text": "labels as PubLayNet. However, due to the different definition of", "orig": "labels as PubLayNet. However, due to the different definition of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption \u2192 Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of"}, {"label": "text", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "text", "bbox": {"l": 317.659, "t": 87.644, "r": 559.807, "b": 127.97400000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.8736215829849243, "cells": [{"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.659, "r_y0": 95.09699999999998, "r_x1": 558.201, "r_y1": 95.09699999999998, "r_x2": 558.201, "r_y2": 87.644, "r_x3": 317.659, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 4: Performance of a Mask R-CNN R50 network with", "orig": "Table 4: Performance of a Mask R-CNN R50 network with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 106.05600000000004, "r_x1": 559.734, "r_y1": 106.05600000000004, "r_x2": 559.734, "r_y2": 98.60299999999995, "r_x3": 317.955, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "document-wise and page-wise split for different label sets.", "orig": "document-wise and page-wise split for different label sets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 117.01499999999999, "r_x1": 455.114, "r_y1": 117.01499999999999, "r_x2": 455.114, "r_y2": 109.56200000000001, "r_x3": 317.955, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "Naive page-wise split will result", "orig": "Naive page-wise split will result", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 459.31, "r_y0": 117.01499999999999, "r_x1": 467.721, "r_y1": 117.01499999999999, "r_x2": 467.721, "r_y2": 109.56200000000001, "r_x3": 459.31, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.909, "r_y0": 116.19299999999998, "r_x1": 477.539, "r_y1": 116.19299999999998, "r_x2": 477.539, "r_y2": 104.81500000000005, "r_x3": 471.909, "r_y3": 104.81500000000005, "coord_origin": "TOPLEFT"}, "text": "/tildelow", "orig": "/tildelow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 477.54, "r_y0": 117.01499999999999, "r_x1": 559.807, "r_y1": 117.01499999999999, "r_x2": 559.807, "r_y2": 109.56200000000001, "r_x3": 477.54, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "10% point improve-", "orig": "10% point improve-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 127.97400000000005, "r_x1": 341.375, "r_y1": 127.97400000000005, "r_x2": 341.375, "r_y2": 120.52099999999996, "r_x3": 317.955, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "ment.", "orig": "ment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. 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"row_header": false, "row_section": false}, {"bbox": {"l": 504.632, "t": 296.793, "r": 512.971, "b": 304.594, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "87", "column_header": false, "row_header": false, "row_section": false}]}, {"label": "text", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 331.834, "r": 559.585, "b": 416.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9740146994590759, "cells": [{"index": 172, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 339.635, "r_x1": 558.202, "r_y1": 339.635, "r_x2": 558.202, "r_y2": 331.834, "r_x3": 317.955, "r_y3": 331.834, "coord_origin": "TOPLEFT"}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "orig": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 173, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 350.594, "r_x1": 558.2, "r_y1": 350.594, "r_x2": 558.2, "r_y2": 342.793, "r_x3": 317.955, "r_y3": 342.793, "coord_origin": "TOPLEFT"}, "text": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "orig": "list-items), the label set of size 4 is the closest to PubLayNet, in the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 174, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 361.553, "r_x1": 393.16, "r_y1": 361.553, "r_x2": 393.16, "r_y2": 353.752, "r_x3": 317.955, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "assumption that the", "orig": "assumption that the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 175, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.842, "r_y0": 361.597, "r_x1": 409.149, "r_y1": 361.597, "r_x2": 409.149, "r_y2": 353.689, "r_x3": 395.842, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 176, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 412.333, "r_y0": 361.553, "r_x1": 485.023, "r_y1": 361.553, "r_x2": 485.023, "r_y2": 353.752, "r_x3": 412.333, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "is down-mapped to", "orig": "is down-mapped to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 177, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 487.704, "r_y0": 361.597, "r_x1": 502.868, "r_y1": 361.597, "r_x2": 502.868, "r_y2": 353.689, "r_x3": 487.704, "r_y3": 353.689, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 178, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 506.055, "r_y0": 361.553, "r_x1": 559.585, "r_y1": 361.553, "r_x2": 559.585, "r_y2": 353.752, "r_x3": 506.055, "r_y3": 353.752, "coord_origin": "TOPLEFT"}, "text": "in PubLayNet.", "orig": "in PubLayNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 179, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 372.512, "r_x1": 558.2, "r_y1": 372.512, "r_x2": 558.2, "r_y2": 364.711, "r_x3": 317.686, "r_y3": 364.711, "coord_origin": "TOPLEFT"}, "text": "The results in Table 3 show that the prediction accuracy on the", "orig": "The results in Table 3 show that the prediction accuracy on the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 180, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 383.47, "r_x1": 558.369, "r_y1": 383.47, "r_x2": 558.369, "r_y2": 375.669, "r_x3": 317.955, "r_y3": 375.669, "coord_origin": "TOPLEFT"}, "text": "remaining class labels does not change significantly when other", "orig": "remaining class labels does not change significantly when other", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 181, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 394.429, "r_x1": 558.2, "r_y1": 394.429, "r_x2": 558.2, "r_y2": 386.628, "r_x3": 317.955, "r_y3": 386.628, "coord_origin": "TOPLEFT"}, "text": "classes are merged into them. The overall macro-average improves", "orig": "classes are merged into them. The overall macro-average improves", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 182, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 405.388, "r_x1": 439.495, "r_y1": 405.388, "r_x2": 439.495, "r_y2": 397.587, "r_x3": 317.955, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "by around 5%, in particular when", "orig": "by around 5%, in particular when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 183, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.728, "r_y0": 405.432, "r_x1": 481.862, "r_y1": 405.432, "r_x2": 481.862, "r_y2": 397.524, "r_x3": 441.728, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-footer", "orig": "Page-footer", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 184, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 484.743, "r_y0": 405.388, "r_x1": 498.237, "r_y1": 405.388, "r_x2": 498.237, "r_y2": 397.587, "r_x3": 484.743, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 185, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.473, "r_y0": 405.432, "r_x1": 543.951, "r_y1": 405.432, "r_x2": 543.951, "r_y2": 397.524, "r_x3": 500.473, "r_y3": 397.524, "coord_origin": "TOPLEFT"}, "text": "Page-header", "orig": "Page-header", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 186, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 546.832, "r_y0": 405.388, "r_x1": 558.201, "r_y1": 405.388, "r_x2": 558.201, "r_y2": 397.587, "r_x3": 546.832, "r_y3": 397.587, "coord_origin": "TOPLEFT"}, "text": "are", "orig": "are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 187, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 416.347, "r_x1": 352.377, "r_y1": 416.347, "r_x2": 352.377, "r_y2": 408.546, "r_x3": 317.955, "r_y3": 408.546, "coord_origin": "TOPLEFT"}, "text": "excluded.", "orig": "excluded.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded."}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 317.955, "t": 430.333, "r": 549.861, "b": 439.4, "coord_origin": "TOPLEFT"}, "confidence": 0.9562516808509827, "cells": [{"index": 188, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 439.4, "r_x1": 549.861, "r_y1": 439.4, "r_x2": 549.861, "r_y2": 430.333, "r_x3": 317.955, "r_y3": 430.333, "coord_origin": "TOPLEFT"}, "text": "Impact of Document Split in Train and Test Set", "orig": "Impact of Document Split in Train and Test Set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Impact of Document Split in Train and Test Set"}, {"label": "text", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.623, "t": 445.027, "r": 559.714, "b": 595.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9869834184646606, "cells": [{"index": 189, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 452.828, "r_x1": 558.369, "r_y1": 452.828, "r_x2": 558.369, "r_y2": 445.027, "r_x3": 317.955, "r_y3": 445.027, "coord_origin": "TOPLEFT"}, "text": "Many documents in DocLayNet have a unique styling. In order", "orig": "Many documents in DocLayNet have a unique styling. In order", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 190, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 463.787, "r_x1": 559.192, "r_y1": 463.787, "r_x2": 559.192, "r_y2": 455.986, "r_x3": 317.955, "r_y3": 455.986, "coord_origin": "TOPLEFT"}, "text": "to avoid overfitting on a particular style, we have split the train-,", "orig": "to avoid overfitting on a particular style, we have split the train-,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 191, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 474.746, "r_x1": 559.582, "r_y1": 474.746, "r_x2": 559.582, "r_y2": 466.945, "r_x3": 317.955, "r_y3": 466.945, "coord_origin": "TOPLEFT"}, "text": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "orig": "test- and validation-sets of DocLayNet on document boundaries, i.e.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 192, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 485.705, "r_x1": 558.206, "r_y1": 485.705, "r_x2": 558.206, "r_y2": 477.904, "r_x3": 317.955, "r_y3": 477.904, "coord_origin": "TOPLEFT"}, "text": "every document contributes pages to only one set. To the best of", "orig": "every document contributes pages to only one set. To the best of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 193, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 496.664, "r_x1": 559.582, "r_y1": 496.664, "r_x2": 559.582, "r_y2": 488.863, "r_x3": 317.955, "r_y3": 488.863, "coord_origin": "TOPLEFT"}, "text": "our knowledge, this was not considered in PubLayNet or DocBank.", "orig": "our knowledge, this was not considered in PubLayNet or DocBank.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 194, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 507.623, "r_x1": 558.2, "r_y1": 507.623, "r_x2": 558.2, "r_y2": 499.822, "r_x3": 317.686, "r_y3": 499.822, "coord_origin": "TOPLEFT"}, "text": "To quantify how this affects model performance, we trained and", "orig": "To quantify how this affects model performance, we trained and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 195, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 518.582, "r_x1": 559.585, "r_y1": 518.582, "r_x2": 559.585, "r_y2": 510.781, "r_x3": 317.955, "r_y3": 510.781, "coord_origin": "TOPLEFT"}, "text": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "orig": "evaluated a Mask R-CNN R50 model on a modified dataset version.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 196, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 529.5409999999999, "r_x1": 559.714, "r_y1": 529.5409999999999, "r_x2": 559.714, "r_y2": 521.74, "r_x3": 317.955, "r_y3": 521.74, "coord_origin": "TOPLEFT"}, "text": "Here, the train-, test- and validation-sets were obtained by a ran-", "orig": "Here, the train-, test- and validation-sets were obtained by a ran-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 197, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 540.5, "r_x1": 559.187, "r_y1": 540.5, "r_x2": 559.187, "r_y2": 532.6990000000001, "r_x3": 317.955, "r_y3": 532.6990000000001, "coord_origin": "TOPLEFT"}, "text": "domised draw over the individual pages. As can be seen in Table 4,", "orig": "domised draw over the individual pages. As can be seen in Table 4,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 198, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 551.458, "r_x1": 559.713, "r_y1": 551.458, "r_x2": 559.713, "r_y2": 543.657, "r_x3": 317.955, "r_y3": 543.657, "coord_origin": "TOPLEFT"}, "text": "the difference in model performance is surprisingly large: page-", "orig": "the difference in model performance is surprisingly large: page-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 199, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 562.417, "r_x1": 559.582, "r_y1": 562.417, "r_x2": 559.582, "r_y2": 554.616, "r_x3": 317.623, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "orig": "wise splitting gains \u02dc 0% in mAP over the document-wise splitting.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 200, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.59, "r_y0": 562.417, "r_x1": 394.751, "r_y1": 562.417, "r_x2": 394.751, "r_y2": 554.616, "r_x3": 390.59, "r_y3": 554.616, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 201, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 573.376, "r_x1": 558.2, "r_y1": 573.376, "r_x2": 558.2, "r_y2": 565.575, "r_x3": 317.686, "r_y3": 565.575, "coord_origin": "TOPLEFT"}, "text": "Thus, random page-wise splitting of DocLayNet can easily lead", "orig": "Thus, random page-wise splitting of DocLayNet can easily lead", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 202, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 584.335, "r_x1": 558.205, "r_y1": 584.335, "r_x2": 558.205, "r_y2": 576.534, "r_x3": 317.955, "r_y3": 576.534, "coord_origin": "TOPLEFT"}, "text": "to accidental overestimation of model performance and should be", "orig": "to accidental overestimation of model performance and should be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 203, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 595.294, "r_x1": 348.504, "r_y1": 595.294, "r_x2": 348.504, "r_y2": 587.4929999999999, "r_x3": 317.955, "r_y3": 587.4929999999999, "coord_origin": "TOPLEFT"}, "text": "avoided.", "orig": "avoided.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains \u02dc 0% in mAP over the document-wise splitting. 1 Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided."}, {"label": "section_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 317.955, "t": 609.28, "r": 418.548, "b": 618.347, "coord_origin": "TOPLEFT"}, "confidence": 0.9589217305183411, "cells": [{"index": 204, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 618.347, "r_x1": 418.548, "r_y1": 618.347, "r_x2": 418.548, "r_y2": 609.28, "r_x3": 317.955, "r_y3": 609.28, "coord_origin": "TOPLEFT"}, "text": "Dataset Comparison", "orig": "Dataset Comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Dataset Comparison"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 317.686, "t": 623.9739999999999, "r": 559.188, "b": 708.532, "coord_origin": "TOPLEFT"}, "confidence": 0.9872549772262573, "cells": [{"index": 205, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 631.775, "r_x1": 558.206, "r_y1": 631.775, "r_x2": 558.206, "r_y2": 623.9739999999999, "r_x3": 317.686, "r_y3": 623.9739999999999, "coord_origin": "TOPLEFT"}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of", "orig": "Throughout this paper, we claim that DocLayNet's wider variety of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 206, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 642.734, "r_x1": 558.206, "r_y1": 642.734, "r_x2": 558.206, "r_y2": 634.933, "r_x3": 317.955, "r_y3": 634.933, "coord_origin": "TOPLEFT"}, "text": "document layouts leads to more robust layout detection models. In", "orig": "document layouts leads to more robust layout detection models. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 207, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 653.693, "r_x1": 558.203, "r_y1": 653.693, "r_x2": 558.203, "r_y2": 645.892, "r_x3": 317.686, "r_y3": 645.892, "coord_origin": "TOPLEFT"}, "text": "Table 5, we provide evidence for that. We trained models on each", "orig": "Table 5, we provide evidence for that. We trained models on each", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 208, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 664.652, "r_x1": 558.744, "r_y1": 664.652, "r_x2": 558.744, "r_y2": 656.851, "r_x3": 317.955, "r_y3": 656.851, "coord_origin": "TOPLEFT"}, "text": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "orig": "of the available datasets (PubLayNet, DocBank and DocLayNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 209, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 675.611, "r_x1": 558.204, "r_y1": 675.611, "r_x2": 558.204, "r_y2": 667.81, "r_x3": 317.955, "r_y3": 667.81, "coord_origin": "TOPLEFT"}, "text": "and evaluated them on the test sets of the other datasets. Due to", "orig": "and evaluated them on the test sets of the other datasets. Due to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 210, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 686.5699999999999, "r_x1": 558.201, "r_y1": 686.5699999999999, "r_x2": 558.201, "r_y2": 678.769, "r_x3": 317.955, "r_y3": 678.769, "coord_origin": "TOPLEFT"}, "text": "the different label sets and annotation styles, a direct comparison", "orig": "the different label sets and annotation styles, a direct comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 211, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 697.529, "r_x1": 558.203, "r_y1": 697.529, "r_x2": 558.203, "r_y2": 689.728, "r_x3": 317.955, "r_y3": 689.728, "coord_origin": "TOPLEFT"}, "text": "is not possible. Hence, we focussed on the common labels among", "orig": "is not possible. Hence, we focussed on the common labels among", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 212, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 708.488, "r_x1": 531.077, "r_y1": 708.488, "r_x2": 531.077, "r_y2": 700.687, "r_x3": 317.955, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": "the datasets. Between PubLayNet and DocLayNet, these are", "orig": "the datasets. Between PubLayNet and DocLayNet, these are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 213, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 533.162, "r_y0": 708.532, "r_x1": 557.256, "r_y1": 708.532, "r_x2": 557.256, "r_y2": 700.624, "r_x3": 533.162, "r_y3": 700.624, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 214, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 557.255, "r_y0": 708.488, "r_x1": 559.188, "r_y1": 708.488, "r_x2": 559.188, "r_y2": 700.687, "r_x3": 557.255, "r_y3": 700.687, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,"}], "headers": [{"label": "page_header", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 347.017, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9316118955612183, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 347.017, "r_y1": 68.48900000000003, "r_x2": 347.017, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "orig": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 365.757, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9318180084228516, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.757, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 365.757, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.139, "r_y0": 68.48900000000003, "r_x1": 558.203, "r_y1": 68.48900000000003, "r_x2": 558.203, "r_y2": 60.559999999999945, "r_x3": 253.139, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "orig": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.322, "r_y1": 95.09699999999998, "r_x2": 294.322, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 295.649, "r_y1": 106.05600000000004, "r_x2": 295.649, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "orig": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "cLayNet data-sets. By evaluating on common label classes of", "orig": "cLayNet data-sets. By evaluating on common label classes of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "each dataset, we observe that the DocLayNet-trained model", "orig": "each dataset, we observe that the DocLayNet-trained model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 294.044, "r_y1": 138.933, "r_x2": 294.044, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "has much less pronounced variations in performance across", "orig": "has much less pronounced variations in performance across", 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specified in table 3, and", "orig": "or excluded DocLayNet's other labels as specified in table 3, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.07, "r_x1": 117.129, "r_y1": 421.07, "r_x2": 117.129, "r_y2": 413.269, "r_x3": 53.798, "r_y3": 413.269, "coord_origin": "TOPLEFT"}, "text": "also PubLayNet's", "orig": "also PubLayNet's", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.362, "r_y0": 421.114, "r_x1": 132.434, "r_y1": 421.114, "r_x2": 132.434, "r_y2": 413.206, "r_x3": 119.362, "r_y3": 413.206, "coord_origin": "TOPLEFT"}, "text": "List", "orig": "List", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 135.177, "r_y0": 421.07, "r_x1": 142.544, "r_y1": 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421.07, "r_x2": 294.046, "r_y2": 413.269, "r_x3": 163.881, "r_y3": 413.269, "coord_origin": "TOPLEFT"}, "text": "Note that the different clustering of", "orig": "Note that the different clustering of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.029, "r_x1": 294.046, "r_y1": 432.029, "r_x2": 294.046, "r_y2": 424.228, "r_x3": 53.798, "r_y3": 424.228, "coord_origin": "TOPLEFT"}, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 442.988, "r_x1": 106.207, "r_y1": 442.988, "r_x2": 106.207, "r_y2": 435.187, "r_x3": 53.798, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.448, "r_y0": 443.032, "r_x1": 123.305, "r_y1": 443.032, "r_x2": 123.305, "r_y2": 435.124, "r_x3": 108.448, "r_y3": 435.124, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.305, "r_y0": 442.988, "r_x1": 125.278, "r_y1": 442.988, "r_x2": 125.278, "r_y2": 435.187, "r_x3": 123.305, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 453.946, "r_x1": 294.276, "r_y1": 453.946, "r_x2": 294.276, "r_y2": 446.145, "r_x3": 63.761, "r_y3": 446.145, "coord_origin": "TOPLEFT"}, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 464.905, "r_x1": 62.989, "r_y1": 464.905, "r_x2": 62.989, "r_y2": 457.104, "r_x3": 53.798, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.853, "r_y0": 464.949, "r_x1": 88.947, "r_y1": 464.949, "r_x2": 88.947, "r_y2": 457.041, "r_x3": 64.853, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.02, "r_y0": 464.905, "r_x1": 104.245, 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We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 295.595, "r_x1": 558.435, "r_y1": 295.595, "r_x2": 558.435, "r_y2": 287.794, "r_x3": 327.918, "r_y3": 287.794, "coord_origin": "TOPLEFT"}, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 306.554, "r_x1": 558.2, "r_y1": 306.554, "r_x2": 558.2, "r_y2": 298.753, "r_x3": 317.955, "r_y3": 298.753, "coord_origin": "TOPLEFT"}, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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"TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "orig": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 697.8199999999999, "r_x1": 558.972, "r_y1": 697.8199999999999, "r_x2": 558.972, "r_y2": 691.752, "r_x3": 333.391, "r_y3": 691.752, "coord_origin": "TOPLEFT"}, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "orig": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 705.79, "r_x1": 558.2, "r_y1": 705.79, "r_x2": 558.2, "r_y2": 699.722, "r_x3": 333.391, "r_y3": 699.722, "coord_origin": "TOPLEFT"}, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "orig": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 24, "label": "page_header", "bbox": {"l": 53.798, "t": 60.559999999999945, "r": 558.203, "b": 68.48900000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.7984216809272766, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 68.48900000000003, "r_x1": 246.244, "r_y1": 68.48900000000003, "r_x2": 246.244, "r_y2": 60.559999999999945, "r_x3": 53.798, "r_y3": 60.559999999999945, "coord_origin": "TOPLEFT"}, "text": "KDD '22, August 14-18, 2022, Washington, DC, USA", "orig": "KDD '22, August 14-18, 2022, Washington, DC, USA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.881, "r_y0": 421.07, "r_x1": 294.046, "r_y1": 421.07, "r_x2": 294.046, "r_y2": 413.269, "r_x3": 163.881, "r_y3": 413.269, "coord_origin": "TOPLEFT"}, "text": "Note that the different clustering of", "orig": "Note that the different clustering of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 432.029, "r_x1": 294.046, "r_y1": 432.029, "r_x2": 294.046, "r_y2": 424.228, "r_x3": 53.798, "r_y3": 424.228, "coord_origin": "TOPLEFT"}, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, 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We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 605.999, "r": 156.005, "b": 615.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 53.529, "t": 620.694, "r": 295.558, "b": 705.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "section_header", "bbox": {"l": 317.955, "t": 86.798, "r": 405.73, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 559.714, "b": 186.00599999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 189.164, "r": 559.717, "b": 284.636, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 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"coord_origin": "TOPLEFT"}, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 442.988, "r_x1": 106.207, "r_y1": 442.988, "r_x2": 106.207, "r_y2": 435.187, "r_x3": 53.798, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.448, "r_y0": 443.032, "r_x1": 123.305, "r_y1": 443.032, "r_x2": 123.305, "r_y2": 435.124, "r_x3": 108.448, "r_y3": 435.124, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 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Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 446.145, "r": 295.559, "b": 585.453, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 453.946, "r_x1": 294.276, "r_y1": 453.946, "r_x2": 294.276, "r_y2": 446.145, "r_x3": 63.761, "r_y3": 446.145, "coord_origin": "TOPLEFT"}, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 464.905, "r_x1": 62.989, "r_y1": 464.905, "r_x2": 62.989, "r_y2": 457.104, "r_x3": 53.798, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.853, "r_y0": 464.949, "r_x1": 88.947, "r_y1": 464.949, "r_x2": 88.947, "r_y2": 457.041, "r_x3": 64.853, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.02, "r_y0": 464.905, "r_x1": 104.245, "r_y1": 464.905, "r_x2": 104.245, "r_y2": 457.104, "r_x3": 91.02, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.108, "r_y0": 464.949, "r_x1": 124.781, "r_y1": 464.949, "r_x2": 124.781, "r_y2": 457.041, "r_x3": 106.108, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.855, "r_y0": 464.905, "r_x1": 277.632, "r_y1": 464.905, "r_x2": 277.632, "r_y2": 457.104, "r_x3": 126.855, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 605.999, "r": 156.005, "b": 615.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.529, "t": 620.694, "r": 295.558, "b": 705.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.955, "t": 86.798, "r": 405.73, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 559.714, "b": 186.00599999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 189.164, "r": 559.717, "b": 284.636, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.623, "t": 287.794, "r": 558.435, "b": 317.513, "coord_origin": "TOPLEFT"}, "confidence": 0.9676101207733154, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 295.595, "r_x1": 558.435, "r_y1": 295.595, "r_x2": 558.435, "r_y2": 287.794, "r_x3": 327.918, "r_y3": 287.794, "coord_origin": "TOPLEFT"}, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 306.554, "r_x1": 558.2, "r_y1": 306.554, "r_x2": 558.2, "r_y2": 298.753, "r_x3": 317.955, "r_y3": 298.753, "coord_origin": "TOPLEFT"}, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 317.513, "r_x1": 535.65, "r_y1": 317.513, "r_x2": 535.65, "r_y2": 309.712, "r_x3": 317.623, "r_y3": 309.712, "coord_origin": "TOPLEFT"}, "text": "work will inspire the research community to close that gap.", "orig": "work will inspire the research community to close that gap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.955, "t": 336.03, "r": 387.37, "b": 345.097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 345.097, "r_x1": 387.37, "r_y1": 345.097, "r_x2": 387.37, "r_y2": 336.03, "r_x3": 317.955, "r_y3": 336.03, "coord_origin": "TOPLEFT"}, "text": "REFERENCES", "orig": "REFERENCES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 349.037, "r": 558.201, "b": 371.078, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.198, "r_y0": 355.105, "r_x1": 329.406, "r_y1": 355.105, "r_x2": 329.406, "r_y2": 349.037, "r_x3": 321.198, "r_y3": 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Document Analysis and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 143, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 371.078, "r_x1": 365.596, "r_y1": 371.078, "r_x2": 365.596, "r_y2": 364.928, "r_x3": 333.391, "r_y3": 364.928, "coord_origin": "TOPLEFT"}, "text": "Recognition", "orig": "Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 144, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.596, "r_y0": 371.045, "r_x1": 367.13, "r_y1": 371.045, "r_x2": 367.13, "r_y2": 364.977, "r_x3": 365.596, "r_y3": 364.977, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 145, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.874, "r_y0": 371.045, "r_x1": 434.295, "r_y1": 371.045, "r_x2": 434.295, "r_y2": 364.977, "r_x3": 368.874, "r_y3": 364.977, "coord_origin": "TOPLEFT"}, "text": "pages 1449-1453, 2013.", "orig": "pages 1449-1453, 2013.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[1] Max G\u00f6bel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013."}, {"label": "list_item", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 321.198, "t": 372.947, "r": 559.38, "b": 402.958, "coord_origin": "TOPLEFT"}, "confidence": 0.9480945467948914, "cells": [{"index": 146, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.198, "r_y0": 379.015, "r_x1": 329.406, "r_y1": 379.015, "r_x2": 329.406, "r_y2": 372.947, "r_x3": 321.198, "r_y3": 372.947, "coord_origin": "TOPLEFT"}, "text": "[2]", "orig": "[2]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 147, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 379.015, "r_x1": 547.031, "r_y1": 379.015, "r_x2": 547.031, "r_y2": 372.947, "r_x3": 333.391, "r_y3": 372.947, "coord_origin": "TOPLEFT"}, "text": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher.", "orig": "Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 148, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 551.818, "r_y0": 379.015, "r_x1": 559.38, "r_y1": 379.015, "r_x2": 559.38, "r_y2": 372.947, "r_x3": 551.818, "r_y3": 372.947, "coord_origin": "TOPLEFT"}, "text": "Ic-", "orig": "Ic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 149, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 386.985, "r_x1": 441.477, "r_y1": 386.985, "r_x2": 441.477, "r_y2": 380.917, "r_x3": 333.391, "r_y3": 380.917, "coord_origin": "TOPLEFT"}, "text": "dar2017 competition on recognition", "orig": "dar2017 competition on recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 150, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.664, "r_y0": 386.985, "r_x1": 450.454, "r_y1": 386.985, "r_x2": 450.454, "r_y2": 380.917, "r_x3": 444.664, "r_y3": 380.917, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 151, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.634, "r_y0": 386.985, "r_x1": 559.38, "r_y1": 386.985, "r_x2": 559.38, "r_y2": 380.917, "r_x3": 453.634, "r_y3": 380.917, "coord_origin": "TOPLEFT"}, "text": "documents with complex layouts -", "orig": "documents with complex layouts -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 152, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 394.955, "r_x1": 367.434, "r_y1": 394.955, "r_x2": 367.434, "r_y2": 388.887, "r_x3": 333.391, "r_y3": 388.887, "coord_origin": "TOPLEFT"}, "text": "rdcl2017. In", "orig": "rdcl2017. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 153, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 369.174, "r_y0": 394.988, "r_x1": 558.204, "r_y1": 394.988, "r_x2": 558.204, "r_y2": 388.838, "r_x3": 369.174, "r_y3": 388.838, "coord_origin": "TOPLEFT"}, "text": "2017 14th IAPR International Conference on Document Analysis and", "orig": "2017 14th IAPR International Conference on Document Analysis and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 154, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 402.958, "r_x1": 390.876, "r_y1": 402.958, "r_x2": 390.876, "r_y2": 396.808, "r_x3": 333.391, "r_y3": 396.808, "coord_origin": "TOPLEFT"}, "text": "Recognition (ICDAR)", "orig": "Recognition (ICDAR)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 155, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 390.877, "r_y0": 402.925, "r_x1": 392.411, "r_y1": 402.925, "r_x2": 392.411, "r_y2": 396.857, "r_x3": 390.877, "r_y3": 396.857, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 156, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 394.155, "r_y0": 402.925, "r_x1": 492.178, "r_y1": 402.925, "r_x2": 492.178, "r_y2": 396.857, "r_x3": 394.155, "r_y3": 396.857, "coord_origin": "TOPLEFT"}, "text": "volume 01, pages 1404-1410, 2017.", "orig": "volume 01, pages 1404-1410, 2017.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. 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IEEE Computer Society, Oct 2017."}, {"label": "list_item", "id": 23, "page_no": 7, "cluster": {"id": 23, "label": "list_item", "bbox": {"l": 317.955, "t": 675.811, "r": 558.972, "b": 705.79, "coord_origin": "TOPLEFT"}, "confidence": 0.889543354511261, "cells": [{"index": 227, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 681.879, "r_x1": 329.406, "r_y1": 681.879, "r_x2": 329.406, "r_y2": 675.811, "r_x3": 317.955, "r_y3": 675.811, "coord_origin": "TOPLEFT"}, "text": "[13]", "orig": "[13]", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 228, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 681.879, "r_x1": 558.967, "r_y1": 681.879, "r_x2": 558.967, "r_y2": 675.811, "r_x3": 333.391, "r_y3": 675.811, "coord_origin": "TOPLEFT"}, "text": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "orig": "Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 229, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.182, "r_y0": 689.849, "r_x1": 558.967, "r_y1": 689.849, "r_x2": 558.967, "r_y2": 683.781, "r_x3": 333.182, "r_y3": 683.781, "coord_origin": "TOPLEFT"}, "text": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "orig": "TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 230, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 697.8199999999999, "r_x1": 558.972, "r_y1": 697.8199999999999, "r_x2": 558.972, "r_y2": 691.752, "r_x3": 333.391, "r_y3": 691.752, "coord_origin": "TOPLEFT"}, "text": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "orig": "Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 231, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.391, "r_y0": 705.79, "r_x1": 558.2, "r_y1": 705.79, "r_x2": 558.2, "r_y2": 699.722, "r_x3": 333.391, "r_y3": 699.722, "coord_origin": "TOPLEFT"}, "text": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "orig": "Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "[13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 53.502, "t": 87.644, "r": 295.649, "b": 149.89200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9824112057685852, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.502, "r_y0": 95.09699999999998, "r_x1": 294.322, "r_y1": 95.09699999999998, "r_x2": 294.322, "r_y2": 87.644, "r_x3": 53.502, "r_y3": 87.644, "coord_origin": "TOPLEFT"}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "orig": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 106.05600000000004, "r_x1": 295.649, "r_y1": 106.05600000000004, "r_x2": 295.649, "r_y2": 98.60299999999995, "r_x3": 53.798, "r_y3": 98.60299999999995, "coord_origin": "TOPLEFT"}, "text": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "orig": "R-CNN R50 network across the PubLayNet, DocBank & Do-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 117.01499999999999, "r_x1": 294.044, "r_y1": 117.01499999999999, "r_x2": 294.044, "r_y2": 109.56200000000001, "r_x3": 53.798, "r_y3": 109.56200000000001, "coord_origin": "TOPLEFT"}, "text": "cLayNet data-sets. By evaluating on common label classes of", "orig": "cLayNet data-sets. By evaluating on common label classes of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 127.97400000000005, "r_x1": 294.044, "r_y1": 127.97400000000005, "r_x2": 294.044, "r_y2": 120.52099999999996, "r_x3": 53.798, "r_y3": 120.52099999999996, "coord_origin": "TOPLEFT"}, "text": "each dataset, we observe that the DocLayNet-trained model", "orig": "each dataset, we observe that the DocLayNet-trained model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 138.933, "r_x1": 294.044, "r_y1": 138.933, "r_x2": 294.044, "r_y2": 131.48000000000002, "r_x3": 53.798, "r_y3": 131.48000000000002, "coord_origin": "TOPLEFT"}, "text": "has much less pronounced variations in performance across", "orig": "has much less pronounced variations in performance across", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 149.89200000000005, "r_x1": 101.159, "r_y1": 149.89200000000005, "r_x2": 101.159, "r_y2": 142.43899999999996, "r_x3": 53.798, "r_y3": 142.43899999999996, "coord_origin": "TOPLEFT"}, "text": "all datasets.", "orig": "all datasets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. 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167.381, "r_y3": 391.351, "coord_origin": "TOPLEFT"}, "text": "Before training, we either mapped", "orig": "Before training, we either mapped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 410.111, "r_x1": 294.047, "r_y1": 410.111, "r_x2": 294.047, "r_y2": 402.31, "r_x3": 53.798, "r_y3": 402.31, "coord_origin": "TOPLEFT"}, "text": "or excluded DocLayNet's other labels as specified in table 3, and", "orig": "or excluded DocLayNet's other labels as specified in table 3, and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 421.07, "r_x1": 117.129, "r_y1": 421.07, "r_x2": 117.129, "r_y2": 413.269, "r_x3": 53.798, "r_y3": 413.269, "coord_origin": "TOPLEFT"}, "text": "also PubLayNet's", "orig": "also PubLayNet's", "text_direction": "left_to_right", 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"coord_origin": "TOPLEFT"}, "text": "lists (by list-element vs. whole list objects) naturally decreases the", "orig": "lists (by list-element vs. whole list objects) naturally decreases the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 442.988, "r_x1": 106.207, "r_y1": 442.988, "r_x2": 106.207, "r_y2": 435.187, "r_x3": 53.798, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": "mAP score for", "orig": "mAP score for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 108.448, "r_y0": 443.032, "r_x1": 123.305, "r_y1": 443.032, "r_x2": 123.305, "r_y2": 435.124, "r_x3": 108.448, "r_y3": 435.124, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 123.305, "r_y0": 442.988, "r_x1": 125.278, "r_y1": 442.988, "r_x2": 125.278, "r_y2": 435.187, "r_x3": 123.305, "r_y3": 435.187, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text ."}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 53.467, "t": 446.145, "r": 295.559, "b": 585.453, "coord_origin": "TOPLEFT"}, "confidence": 0.9868056774139404, "cells": [{"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 63.761, "r_y0": 453.946, "r_x1": 294.276, "r_y1": 453.946, "r_x2": 294.276, "r_y2": 446.145, "r_x3": 63.761, "r_y3": 446.145, "coord_origin": "TOPLEFT"}, "text": "For comparison of DocBank with DocLayNet, we trained only", "orig": "For comparison of DocBank with DocLayNet, we trained only", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 464.905, "r_x1": 62.989, "r_y1": 464.905, "r_x2": 62.989, "r_y2": 457.104, "r_x3": 53.798, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.853, "r_y0": 464.949, "r_x1": 88.947, "r_y1": 464.949, "r_x2": 88.947, "r_y2": 457.041, "r_x3": 64.853, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Picture", "orig": "Picture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 91.02, "r_y0": 464.905, "r_x1": 104.245, "r_y1": 464.905, "r_x2": 104.245, "r_y2": 457.104, "r_x3": 91.02, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 106.108, "r_y0": 464.949, "r_x1": 124.781, "r_y1": 464.949, "r_x2": 124.781, "r_y2": 457.041, "r_x3": 106.108, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 126.855, "r_y0": 464.905, "r_x1": 277.632, "r_y1": 464.905, "r_x2": 277.632, "r_y2": 457.104, "r_x3": 126.855, "r_y3": 457.104, "coord_origin": "TOPLEFT"}, "text": "clusters of each dataset. We had to exclude", "orig": "clusters of each dataset. We had to exclude", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 279.497, "r_y0": 464.949, "r_x1": 294.048, "r_y1": 464.949, "r_x2": 294.048, "r_y2": 457.041, "r_x3": 279.497, "r_y3": 457.041, "coord_origin": "TOPLEFT"}, "text": "Text", "orig": "Text", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 475.864, "r_x1": 294.047, "r_y1": 475.864, "r_x2": 294.047, "r_y2": 468.063, "r_x3": 53.798, "r_y3": 468.063, "coord_origin": "TOPLEFT"}, "text": "because successive paragraphs are often grouped together into a", "orig": "because successive paragraphs are often grouped together into a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 486.823, "r_x1": 294.045, "r_y1": 486.823, "r_x2": 294.045, "r_y2": 479.022, "r_x3": 53.798, "r_y3": 479.022, "coord_origin": "TOPLEFT"}, "text": "single object in DocBank. This paragraph grouping is incompatible", "orig": "single object in DocBank. This paragraph grouping is incompatible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.467, "r_y0": 497.782, "r_x1": 294.045, "r_y1": 497.782, "r_x2": 294.045, "r_y2": 489.981, "r_x3": 53.467, "r_y3": 489.981, "coord_origin": "TOPLEFT"}, "text": "with the individual paragraphs of DocLayNet. As can be seen in", "orig": "with the individual paragraphs of DocLayNet. As can be seen in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 508.741, "r_x1": 295.559, "r_y1": 508.741, "r_x2": 295.559, "r_y2": 500.94, "r_x3": 53.529, "r_y3": 500.94, "coord_origin": "TOPLEFT"}, "text": "Table 5, DocLayNet trained models yield better performance com-", "orig": "Table 5, DocLayNet trained models yield better performance com-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 519.7, "r_x1": 294.047, "r_y1": 519.7, "r_x2": 294.047, "r_y2": 511.899, "r_x3": 53.798, "r_y3": 511.899, "coord_origin": "TOPLEFT"}, "text": "pared to the previous datasets. It is noteworthy that the models", "orig": "pared to the previous datasets. It is noteworthy that the models", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 530.659, "r_x1": 294.212, "r_y1": 530.659, "r_x2": 294.212, "r_y2": 522.858, "r_x3": 53.798, "r_y3": 522.858, "coord_origin": "TOPLEFT"}, "text": "trained on PubLayNet and DocBank perform very well on their", "orig": "trained on PubLayNet and DocBank perform very well on their", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 541.6179999999999, "r_x1": 294.047, "r_y1": 541.6179999999999, "r_x2": 294.047, "r_y2": 533.817, "r_x3": 53.798, "r_y3": 533.817, "coord_origin": "TOPLEFT"}, "text": "own test set, but have a much lower performance on the foreign", "orig": "own test set, but have a much lower performance on the foreign", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 552.577, "r_x1": 294.047, "r_y1": 552.577, "r_x2": 294.047, "r_y2": 544.7760000000001, "r_x3": 53.798, "r_y3": 544.7760000000001, "coord_origin": "TOPLEFT"}, "text": "datasets. While this also applies to DocLayNet, the difference is", "orig": "datasets. While this also applies to DocLayNet, the difference is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 563.5360000000001, "r_x1": 294.047, "r_y1": 563.5360000000001, "r_x2": 294.047, "r_y2": 555.735, "r_x3": 53.798, "r_y3": 555.735, "coord_origin": "TOPLEFT"}, "text": "far less pronounced. Thus we conclude that DocLayNet trained", "orig": "far less pronounced. Thus we conclude that DocLayNet trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 574.494, "r_x1": 294.214, "r_y1": 574.494, "r_x2": 294.214, "r_y2": 566.693, "r_x3": 53.798, "r_y3": 566.693, "coord_origin": "TOPLEFT"}, "text": "models are overall more robust and will produce better results for", "orig": "models are overall more robust and will produce better results for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 585.453, "r_x1": 157.521, "r_y1": 585.453, "r_x2": 157.521, "r_y2": 577.652, "r_x3": 53.798, "r_y3": 577.652, "coord_origin": "TOPLEFT"}, "text": "challenging, unseen layouts.", "orig": "challenging, unseen layouts.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts."}, {"label": "section_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 53.798, "t": 605.999, "r": 156.005, "b": 615.066, "coord_origin": "TOPLEFT"}, "confidence": 0.9574698805809021, "cells": [{"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 615.066, "r_x1": 156.005, "r_y1": 615.066, "r_x2": 156.005, "r_y2": 605.999, "r_x3": 53.798, "r_y3": 605.999, "coord_origin": "TOPLEFT"}, "text": "Example Predictions", "orig": "Example Predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example Predictions"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 53.529, "t": 620.694, "r": 295.558, "b": 705.207, "coord_origin": "TOPLEFT"}, "confidence": 0.9859886169433594, "cells": [{"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.529, "r_y0": 628.495, "r_x1": 295.557, "r_y1": 628.495, "r_x2": 295.557, "r_y2": 620.694, "r_x3": 53.529, "r_y3": 620.694, "coord_origin": "TOPLEFT"}, "text": "To conclude this section, we illustrate the quality of layout predic-", "orig": "To conclude this section, we illustrate the quality of layout predic-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 639.454, "r_x1": 294.045, "r_y1": 639.454, "r_x2": 294.045, "r_y2": 631.653, "r_x3": 53.798, "r_y3": 631.653, "coord_origin": "TOPLEFT"}, "text": "tions one can expect from DocLayNet-trained models by providing", "orig": "tions one can expect from DocLayNet-trained models by providing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 650.413, "r_x1": 295.556, "r_y1": 650.413, "r_x2": 295.556, "r_y2": 642.612, "r_x3": 53.798, "r_y3": 642.612, "coord_origin": "TOPLEFT"}, "text": "a selection of examples without any further post-processing ap-", "orig": "a selection of examples without any further post-processing ap-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 661.3720000000001, "r_x1": 294.045, "r_y1": 661.3720000000001, "r_x2": 294.045, "r_y2": 653.571, "r_x3": 53.798, "r_y3": 653.571, "coord_origin": "TOPLEFT"}, "text": "plied. Figure 6 shows selected layout predictions on pages from the", "orig": "plied. Figure 6 shows selected layout predictions on pages from the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 672.331, "r_x1": 295.558, "r_y1": 672.331, "r_x2": 295.558, "r_y2": 664.53, "r_x3": 53.798, "r_y3": 664.53, "coord_origin": "TOPLEFT"}, "text": "test-set of DocLayNet. Results look decent in general across docu-", "orig": "test-set of DocLayNet. Results look decent in general across docu-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 683.289, "r_x1": 294.047, "r_y1": 683.289, "r_x2": 294.047, "r_y2": 675.488, "r_x3": 53.798, "r_y3": 675.488, "coord_origin": "TOPLEFT"}, "text": "ment categories, however one can also observe mistakes such as", "orig": "ment categories, however one can also observe mistakes such as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 694.248, "r_x1": 294.045, "r_y1": 694.248, "r_x2": 294.045, "r_y2": 686.447, "r_x3": 53.798, "r_y3": 686.447, "coord_origin": "TOPLEFT"}, "text": "overlapping clusters of different classes, or entirely missing boxes", "orig": "overlapping clusters of different classes, or entirely missing boxes", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 53.798, "r_y0": 705.207, "r_x1": 136.074, "r_y1": 705.207, "r_x2": 136.074, "r_y2": 697.406, "r_x3": 53.798, "r_y3": 697.406, "coord_origin": "TOPLEFT"}, "text": "due to low confidence.", "orig": "due to low confidence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence."}, {"label": "section_header", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "section_header", "bbox": {"l": 317.955, "t": 86.798, "r": 405.73, "b": 95.86500000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.931998074054718, "cells": [{"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 95.86500000000001, "r_x1": 323.562, "r_y1": 95.86500000000001, "r_x2": 323.562, "r_y2": 86.798, "r_x3": 317.955, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.471, "r_y0": 95.86500000000001, "r_x1": 405.73, "r_y1": 95.86500000000001, "r_x2": 405.73, "r_y2": 86.798, "r_x3": 334.471, "r_y3": 86.798, "coord_origin": "TOPLEFT"}, "text": "CONCLUSION", "orig": "CONCLUSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6 CONCLUSION"}, {"label": "text", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "text", "bbox": {"l": 317.955, "t": 101.49199999999996, "r": 559.714, "b": 186.00599999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.985493004322052, "cells": [{"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 109.293, "r_x1": 558.204, "r_y1": 109.293, "r_x2": 558.204, "r_y2": 101.49199999999996, "r_x3": 317.955, "r_y3": 101.49199999999996, "coord_origin": "TOPLEFT"}, "text": "In this paper, we presented the DocLayNet dataset. It provides the", "orig": "In this paper, we presented the DocLayNet dataset. It provides the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 120.25199999999995, "r_x1": 558.204, "r_y1": 120.25199999999995, "r_x2": 558.204, "r_y2": 112.45100000000002, "r_x3": 317.955, "r_y3": 112.45100000000002, "coord_origin": "TOPLEFT"}, "text": "document conversion and layout analysis research community a", "orig": "document conversion and layout analysis research community a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 131.211, "r_x1": 558.433, "r_y1": 131.211, "r_x2": 558.433, "r_y2": 123.40999999999997, "r_x3": 317.955, "r_y3": 123.40999999999997, "coord_origin": "TOPLEFT"}, "text": "new and challenging dataset to improve and fine-tune novel ML", "orig": "new and challenging dataset to improve and fine-tune novel ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 142.16999999999996, "r_x1": 558.204, "r_y1": 142.16999999999996, "r_x2": 558.204, "r_y2": 134.36900000000003, "r_x3": 317.955, "r_y3": 134.36900000000003, "coord_origin": "TOPLEFT"}, "text": "methods on. In contrast to many other datasets, DocLayNet was", "orig": "methods on. In contrast to many other datasets, DocLayNet was", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 153.12900000000002, "r_x1": 558.204, "r_y1": 153.12900000000002, "r_x2": 558.204, "r_y2": 145.32799999999997, "r_x3": 317.955, "r_y3": 145.32799999999997, "coord_origin": "TOPLEFT"}, "text": "created by human annotation in order to obtain reliable layout", "orig": "created by human annotation in order to obtain reliable layout", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 121, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 164.08799999999997, "r_x1": 559.713, "r_y1": 164.08799999999997, "r_x2": 559.713, "r_y2": 156.28700000000003, "r_x3": 317.955, "r_y3": 156.28700000000003, "coord_origin": "TOPLEFT"}, "text": "ground-truth on a wide variety of publication- and typesetting-", "orig": "ground-truth on a wide variety of publication- and typesetting-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 122, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 175.04700000000003, "r_x1": 559.714, "r_y1": 175.04700000000003, "r_x2": 559.714, "r_y2": 167.24599999999998, "r_x3": 317.955, "r_y3": 167.24599999999998, "coord_origin": "TOPLEFT"}, "text": "styles. Including a large proportion of documents outside the scien-", "orig": "styles. Including a large proportion of documents outside the scien-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 123, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 186.00599999999997, "r_x1": 540.044, "r_y1": 186.00599999999997, "r_x2": 540.044, "r_y2": 178.20500000000004, "r_x3": 317.955, "r_y3": 178.20500000000004, "coord_origin": "TOPLEFT"}, "text": "tific publishing domain adds significant value in this respect.", "orig": "tific publishing domain adds significant value in this respect.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect."}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 317.686, "t": 189.164, "r": 559.717, "b": 284.636, "coord_origin": "TOPLEFT"}, "confidence": 0.9800531268119812, "cells": [{"index": 124, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 196.96500000000003, "r_x1": 558.198, "r_y1": 196.96500000000003, "r_x2": 558.198, "r_y2": 189.164, "r_x3": 327.918, "r_y3": 189.164, "coord_origin": "TOPLEFT"}, "text": "From the dataset, we have derived on the one hand reference", "orig": "From the dataset, we have derived on the one hand reference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 125, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 207.92399999999998, "r_x1": 558.204, "r_y1": 207.92399999999998, "r_x2": 558.204, "r_y2": 200.12300000000005, "r_x3": 317.955, "r_y3": 200.12300000000005, "coord_origin": "TOPLEFT"}, "text": "metrics for human performance on document-layout annotation", "orig": "metrics for human performance on document-layout annotation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 126, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.686, "r_y0": 218.88300000000004, "r_x1": 559.717, "r_y1": 218.88300000000004, "r_x2": 559.717, "r_y2": 211.082, "r_x3": 317.686, "r_y3": 211.082, "coord_origin": "TOPLEFT"}, "text": "(through double and triple annotations) and on the other hand eval-", "orig": "(through double and triple annotations) and on the other hand eval-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 127, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 229.841, "r_x1": 558.202, "r_y1": 229.841, "r_x2": 558.202, "r_y2": 222.03999999999996, "r_x3": 317.955, "r_y3": 222.03999999999996, "coord_origin": "TOPLEFT"}, "text": "uated the baseline performance of commonly used object detection", "orig": "uated the baseline performance of commonly used object detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 128, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 240.79999999999995, "r_x1": 558.205, "r_y1": 240.79999999999995, "r_x2": 558.205, "r_y2": 232.99900000000002, "r_x3": 317.955, "r_y3": 232.99900000000002, "coord_origin": "TOPLEFT"}, "text": "methods. We also illustrated the impact of various dataset-related", "orig": "methods. We also illustrated the impact of various dataset-related", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 129, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 251.75900000000001, "r_x1": 559.184, "r_y1": 251.75900000000001, "r_x2": 559.184, "r_y2": 243.95799999999997, "r_x3": 317.955, "r_y3": 243.95799999999997, "coord_origin": "TOPLEFT"}, "text": "aspects on model performance through data-ablation experiments,", "orig": "aspects on model performance through data-ablation experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 130, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 262.71799999999996, "r_x1": 558.202, "r_y1": 262.71799999999996, "r_x2": 558.202, "r_y2": 254.91700000000003, "r_x3": 317.955, "r_y3": 254.91700000000003, "coord_origin": "TOPLEFT"}, "text": "both from a size and class-label perspective. Last but not least, we", "orig": "both from a size and class-label perspective. Last but not least, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 131, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 273.677, "r_x1": 558.203, "r_y1": 273.677, "r_x2": 558.203, "r_y2": 265.876, "r_x3": 317.955, "r_y3": 265.876, "coord_origin": "TOPLEFT"}, "text": "compared the accuracy of models trained on other public datasets", "orig": "compared the accuracy of models trained on other public datasets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 132, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 284.636, "r_x1": 540.994, "r_y1": 284.636, "r_x2": 540.994, "r_y2": 276.83500000000004, "r_x3": 317.955, "r_y3": 276.83500000000004, "coord_origin": "TOPLEFT"}, "text": "and showed that DocLayNet trained models are more robust.", "orig": "and showed that DocLayNet trained models are more robust.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust."}, {"label": "text", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "text", "bbox": {"l": 317.623, "t": 287.794, "r": 558.435, "b": 317.513, "coord_origin": "TOPLEFT"}, "confidence": 0.9676101207733154, "cells": [{"index": 133, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 327.918, "r_y0": 295.595, "r_x1": 558.435, "r_y1": 295.595, "r_x2": 558.435, "r_y2": 287.794, "r_x3": 327.918, "r_y3": 287.794, "coord_origin": "TOPLEFT"}, "text": "To date, there is still a significant gap between human and ML", "orig": "To date, there is still a significant gap between human and ML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 134, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 306.554, "r_x1": 558.2, "r_y1": 306.554, "r_x2": 558.2, "r_y2": 298.753, "r_x3": 317.955, "r_y3": 298.753, "coord_origin": "TOPLEFT"}, "text": "accuracy on the layout interpretation task, and we hope that this", "orig": "accuracy on the layout interpretation task, and we hope that this", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 135, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.623, "r_y0": 317.513, "r_x1": 535.65, "r_y1": 317.513, "r_x2": 535.65, "r_y2": 309.712, "r_x3": 317.623, "r_y3": 309.712, "coord_origin": "TOPLEFT"}, "text": "work will inspire the research community to close that gap.", "orig": "work will inspire the research community to close that gap.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap."}, {"label": "section_header", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 317.955, "t": 336.03, "r": 387.37, "b": 345.097, "coord_origin": "TOPLEFT"}, "confidence": 0.9378752708435059, "cells": [{"index": 136, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 317.955, "r_y0": 345.097, "r_x1": 387.37, "r_y1": 345.097, "r_x2": 387.37, "r_y2": 336.03, "r_x3": 317.955, "r_y3": 336.03, "coord_origin": "TOPLEFT"}, "text": "REFERENCES", "orig": "REFERENCES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "REFERENCES"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 321.198, "t": 349.037, "r": 558.201, "b": 371.078, "coord_origin": "TOPLEFT"}, "confidence": 0.9372857213020325, "cells": [{"index": 137, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.198, "r_y0": 355.105, "r_x1": 329.406, "r_y1": 355.105, "r_x2": 329.406, "r_y2": 349.037, "r_x3": 321.198, "r_y3": 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Inference timing results for all experiments -5.1 -We have chosen the PubTabNet data set to perform HPO, since it includes a -Table -5.2 -We picked the model parameter configuration that produced the best prediction -Additionally, +order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. +5.1 Hyper Parameter Optimization +We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. +# enc-layers# dec-layersLanguageTEDsmAPInferencesimplecomplexall(0.75)time (secs)66OTSL HTML0.965 0.9690.934 0.9270.955 0.9550.88 0.8572.73 5.3944OTSL HTML0.938 0.9520.904 0.9090.927 0.9380.853 0.8431.97 3.7724OTSL HTML0.923 0.9450.897 0.9010.915 0.9310.859 0.8341.91 3.8142OTSL HTML0.952 0.9440.92 0.9030.942 0.9310.857 0.8241.22 2Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. +5.2 Quantitative Results +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables. +Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json index e86c8366..68a0b635 100644 --- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json +++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.json @@ -1 +1 @@ -{"schema_name": "DoclingDocument", "version": "1.2.0", "name": "2305.03393v1-pg9", "origin": {"mimetype": "application/pdf", "binary_hash": 3463920545297462180, "filename": "2305.03393v1-pg9.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/tables/0"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 194.478, "t": 698.337, "r": 447.545, "b": 689.963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.984, "t": 698.337, "r": 480.593, "b": 689.963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 673.126, "r": 480.591, "b": 663.83, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "order to compute the TED score. 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Inference timing results for all experiments"}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 622.844, "r": 149.403, "b": 613.617, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "5.1", "text": "5.1", "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 606.474, "r": 480.582, "b": 597.178, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 518.264, "r": 160.118, "b": 508.127, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 283.878, "r": 149.403, "b": 274.65100000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "5.2", "text": "5.2", "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 267.509, "r": 480.562, "b": 258.21299999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "We picked the model parameter configuration that produced the best prediction", "text": "We picked the model parameter configuration that produced the best prediction"}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 149.709, "t": 171.86699999999996, "r": 205.6, "b": 162.57100000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Additionally,", "text": "Additionally,"}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/5"}], "content_layer": "body", "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.66741943359375, "t": 454.45458984375, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [], "num_rows": 0, "num_cols": 0, "grid": []}}], "key_value_items": [], "form_items": [], "pages": {"1": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 1}}} \ No newline at end of file +{"schema_name": "DoclingDocument", "version": "1.3.0", "name": "2305.03393v1-pg9", "origin": {"mimetype": "application/pdf", "binary_hash": 3463920545297462180, "filename": "2305.03393v1-pg9.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/tables/0"}, {"cref": "#/texts/6"}, {"cref": "#/texts/7"}, {"cref": "#/texts/8"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 194.478, "t": 698.337, "r": 447.545, "b": 689.963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 475.984, "t": 698.337, "r": 480.593, "b": 689.963, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 673.126, "r": 480.597, "b": 639.919, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 622.844, "r": 318.451, "b": 613.617, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 606.474, "r": 480.596, "b": 536.933, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/5", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 518.264, "r": 480.599, "b": 464.763, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 283.878, "r": 264.408, "b": 274.65100000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 267.509, "r": 480.722, "b": 174.52600000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 557]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 171.86699999999996, "r": 480.599, "b": 126.70600000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [{"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/5"}], "content_layer": "body", "label": "table", "prov": [{"page_no": 1, "bbox": {"l": 139.66741943359375, "t": 454.45458984375, "r": 475.00927734375, "b": 322.5054626464844, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/5"}], "references": [], "footnotes": [], "image": null, "data": {"table_cells": [{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 347.106, "r": 278.333, "b": 355.48, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 341.627, "r": 417.123, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, 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"end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 420.531, "r": 310.009, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.923 0.945", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 420.531, "r": 347.709, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.897 0.901", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 420.531, "r": 386.252, "b": 442.328, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.915 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 419.24, "r": 418.782, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.859 0.834", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 419.24, "r": 458.388, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.91 3.81", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 161.906, "t": 452.312, "r": 166.515, "b": 460.686, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 452.312, "r": 214.118, "b": 460.686, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 446.832, "r": 272.94, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 446.832, "r": 310.009, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.952 0.944", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 446.832, "r": 347.709, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.92 0.903", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 445.541, "r": 386.252, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 445.541, "r": 418.782, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 445.541, "r": 458.388, "b": 468.158, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}], "num_rows": 6, "num_cols": 8, "grid": [[{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 347.106, "r": 278.333, "b": 355.48, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 341.627, "r": 348.263, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 341.627, "r": 417.123, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "mAP", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 430.771, "t": 341.627, "r": 467.141, "b": 350.001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "Inference", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 144.592, "t": 341.627, "r": 183.828, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 341.627, "r": 231.425, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 347.106, "r": 278.333, "b": 355.48, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 286.686, "t": 354.578, "r": 312.325, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "simple", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 320.702, "t": 354.578, "r": 353.718, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "complex", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 369.306, "t": 354.578, "r": 379.031, "b": 362.952, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "all", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 394.927, "t": 352.586, "r": 418.473, "b": 360.96, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "(0.75)", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 427.148, "t": 352.586, "r": 470.773, "b": 360.96, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "time (secs)", "column_header": true, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.906, "t": 373.408, "r": 166.515, "b": 381.782, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 373.408, "r": 214.118, "b": 381.782, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "6", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 367.928, "r": 272.94, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 367.928, "r": 310.009, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.965 0.969", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 367.928, "r": 347.709, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.934 0.927", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 363.676, "t": 367.928, "r": 384.668, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.955 0.955", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 396.206, "t": 366.637, "r": 417.198, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.88 0.857", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 366.637, "r": 458.388, "b": 389.254, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "2.73 5.39", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.906, "t": 399.709, "r": 166.515, "b": 408.083, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 399.709, "r": 214.118, "b": 408.083, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 394.23, "r": 272.94, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 394.23, "r": 310.009, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.938 0.952", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 394.23, "r": 347.709, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.904 0.909", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 394.23, "r": 386.252, "b": 416.027, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.927 0.938", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 392.939, "r": 418.782, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.853 0.843", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 392.939, "r": 458.388, "b": 415.555, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.97 3.77", "column_header": false, "row_header": false, "row_section": false}], [{"bbox": {"l": 161.906, "t": 426.01, "r": 166.515, "b": 434.384, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 209.509, "t": 426.01, "r": 214.118, "b": 434.384, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "4", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 420.531, "r": 272.94, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 420.531, "r": 310.009, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.923 0.945", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 420.531, "r": 347.709, "b": 441.856, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.897 0.901", "column_header": false, "row_header": false, "row_section": 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Inference timing results for all experiments +order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. -## 5.1 +## 5.1 Hyper Parameter Optimization -We have chosen the PubTabNet data set to perform HPO, since it includes a +We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. -Table +Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -## 5.2 +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP | Inference | +|----------------|----------------|------------|-------------|-------------|-------------|-------------|-------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | (0.75) | time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | -We picked the model parameter configuration that produced the best prediction +## 5.2 Quantitative Results -Additionally, \ No newline at end of file +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables. + +Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json index e13abd7b..309b3c4d 100644 --- a/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json +++ b/tests/data/groundtruth/docling_v2/2305.03393v1-pg9.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. 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536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.591, "b": 128.16999999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 149.403, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.582, "b": 194.822, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 160.118, "b": 283.873, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 149.403, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.562, "b": 533.787, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 620.133, "r": 205.6, "b": 629.429, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, 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"r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments"}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 149.403, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.582, "b": 194.822, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a"}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 160.118, "b": 283.873, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table"}, {"label": "section_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 149.403, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.562, "b": 533.787, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction"}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 620.133, "r": 205.6, "b": 629.429, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments"}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 149.403, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.582, "b": 194.822, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. 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available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, 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524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 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Inference timing results for all experiments", "orig": "order to compute the TED score. 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Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.87400000000002, "r": 480.597, "b": 152.08100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9806433916091919, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.16999999999996, "r_x1": 480.591, "r_y1": 128.16999999999996, "r_x2": 480.591, "r_y2": 118.87400000000002, "r_x3": 134.765, "r_y3": 118.87400000000002, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 140.125, "r_x1": 480.597, "r_y1": 140.125, "r_x2": 480.597, "r_y2": 130.82899999999995, "r_x3": 134.765, "r_y3": 130.82899999999995, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 152.08100000000002, "r_x1": 210.788, "r_y1": 152.08100000000002, "r_x2": 210.788, "r_y2": 142.78499999999997, "r_x3": 134.765, "r_y3": 142.78499999999997, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 7, "page_no": 0, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 169.15599999999995, "r": 318.451, "b": 178.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9577404260635376, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.38300000000004, "r_x1": 149.403, "r_y1": 178.38300000000004, "r_x2": 149.403, "r_y2": 169.15599999999995, "r_x3": 134.765, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 178.38300000000004, "r_x1": 318.451, "r_y1": 178.38300000000004, "r_x2": 318.451, "r_y2": 169.15599999999995, "r_x3": 160.859, "r_y3": 169.15599999999995, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 185.52599999999995, "r": 480.596, "b": 255.067, "coord_origin": "TOPLEFT"}, "confidence": 0.98504239320755, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.822, "r_x1": 480.582, "r_y1": 194.822, "r_x2": 480.582, "r_y2": 185.52599999999995, "r_x3": 134.765, "r_y3": 185.52599999999995, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.77700000000004, "r_x1": 480.588, "r_y1": 206.77700000000004, "r_x2": 480.588, "r_y2": 197.481, "r_x3": 134.765, "r_y3": 197.481, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.73199999999997, "r_x1": 480.589, "r_y1": 218.73199999999997, "r_x2": 480.589, "r_y2": 209.43600000000004, "r_x3": 134.765, "r_y3": 209.43600000000004, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.687, "r_x1": 480.596, "r_y1": 230.687, "r_x2": 480.596, "r_y2": 221.39099999999996, "r_x3": 134.765, "r_y3": 221.39099999999996, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.64200000000005, "r_x1": 440.943, "r_y1": 242.64200000000005, "r_x2": 440.943, "r_y2": 233.346, "r_x3": 134.765, "r_y3": 233.346, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 243.111, "r_x1": 480.588, "r_y1": 243.111, "r_x2": 480.588, "r_y2": 232.02099999999996, "r_x3": 444.868, "r_y3": 232.02099999999996, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 255.067, "r_x1": 145.201, "r_y1": 255.067, "r_x2": 145.201, "r_y2": 243.97699999999998, "r_x3": 134.765, "r_y3": 243.97699999999998, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.59799999999996, "r_x1": 311.223, "r_y1": 254.59799999999996, "r_x2": 311.223, "r_y2": 245.30200000000002, "r_x3": 149.149, "r_y3": 245.30200000000002, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 273.736, "r": 480.599, "b": 327.237, "coord_origin": "TOPLEFT"}, "confidence": 0.9591909050941467, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.873, "r_x1": 160.118, "r_y1": 283.873, "r_x2": 160.118, "r_y2": 273.736, "r_x3": 134.765, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.344, "r_y0": 283.873, "r_x1": 175.602, "r_y1": 283.873, "r_x2": 175.602, "r_y2": 273.736, "r_x3": 167.344, "r_y3": 273.736, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 283.401, "r_x1": 255.735, "r_y1": 283.401, "r_x2": 255.735, "r_y2": 275.02700000000004, "r_x3": 188.133, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.007, "r_y0": 283.401, "r_x1": 269.687, "r_y1": 283.401, "r_x2": 269.687, "r_y2": 275.02700000000004, "r_x3": 262.007, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.95, "r_y0": 283.401, "r_x1": 300.648, "r_y1": 283.401, "r_x2": 300.648, "r_y2": 275.02700000000004, "r_x3": 275.95, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 283.401, "r_x1": 321.759, "r_y1": 283.401, "r_x2": 321.759, "r_y2": 275.02700000000004, "r_x3": 306.92, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.022, "r_y0": 283.401, "r_x1": 419.233, "r_y1": 283.401, "r_x2": 419.233, "r_y2": 275.02700000000004, "r_x3": 328.022, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.496, "r_y0": 283.401, "r_x1": 435.22, "r_y1": 283.401, "r_x2": 435.22, "r_y2": 275.02700000000004, "r_x3": 425.496, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.492, "r_y0": 283.401, "r_x1": 454.287, "r_y1": 283.401, "r_x2": 454.287, "r_y2": 275.02700000000004, "r_x3": 441.492, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.559, "r_y0": 283.401, "r_x1": 480.577, "r_y1": 283.401, "r_x2": 480.577, "r_y2": 275.02700000000004, "r_x3": 460.559, "r_y3": 275.02700000000004, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 294.36, "r_x1": 480.599, "r_y1": 294.36, "r_x2": 480.599, "r_y2": 285.986, "r_x3": 134.765, "r_y3": 285.986, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 305.319, "r_x1": 480.599, "r_y1": 305.319, "r_x2": 480.599, "r_y2": 296.945, "r_x3": 134.765, "r_y3": 296.945, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 316.278, "r_x1": 163.505, "r_y1": 316.278, "r_x2": 163.505, "r_y2": 307.904, "r_x3": 134.765, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 316.278, "r_x1": 443.266, "r_y1": 316.278, "r_x2": 443.266, "r_y2": 307.904, "r_x3": 167.817, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 316.278, "r_x1": 480.592, "r_y1": 316.278, "r_x2": 480.592, "r_y2": 307.904, "r_x3": 447.577, "r_y3": 307.904, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.237, "r_x1": 480.588, "r_y1": 327.237, "r_x2": 480.588, "r_y2": 318.863, "r_x3": 134.765, "r_y3": 318.863, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66741943359375, "t": 337.54541015625, "r": 475.00927734375, "b": 469.4945373535156, "coord_origin": "TOPLEFT"}, "confidence": 0.9901031255722046, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.001, "r_x1": 168.045, "r_y1": 350.001, "r_x2": 168.045, "r_y2": 341.627, "r_x3": 160.37, "r_y3": 341.627, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 362.952, "r_x1": 183.828, "r_y1": 362.952, "r_x2": 183.828, "r_y2": 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"page_no": 0, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 508.122, "r": 264.408, "b": 517.3489999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9589294195175171, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 517.3489999999999, "r_x1": 149.403, "r_y1": 517.3489999999999, "r_x2": 149.403, "r_y2": 508.122, "r_x3": 134.765, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 517.3489999999999, "r_x1": 264.408, "r_y1": 517.3489999999999, "r_x2": 264.408, "r_y2": 508.122, "r_x3": 160.859, "r_y3": 508.122, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 524.491, "r": 480.722, "b": 617.4739999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9849975109100342, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.787, "r_x1": 480.562, "r_y1": 533.787, "r_x2": 480.562, "r_y2": 524.491, "r_x3": 134.765, "r_y3": 524.491, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.7429999999999, "r_x1": 164.924, "r_y1": 545.7429999999999, "r_x2": 164.924, "r_y2": 536.447, "r_x3": 134.765, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.7429999999999, "r_x1": 480.59, "r_y1": 545.7429999999999, "r_x2": 480.59, "r_y2": 536.447, "r_x3": 169.324, "r_y3": 536.447, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.698, "r_x1": 480.722, "r_y1": 557.698, "r_x2": 480.722, "r_y2": 548.402, "r_x3": 134.765, "r_y3": 548.402, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.653, "r_x1": 480.603, "r_y1": 569.653, "r_x2": 480.603, "r_y2": 560.357, "r_x3": 134.765, "r_y3": 560.357, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.608, "r_x1": 480.594, "r_y1": 581.608, "r_x2": 480.594, "r_y2": 572.312, "r_x3": 134.765, "r_y3": 572.312, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.563, "r_x1": 480.592, "r_y1": 593.563, "r_x2": 480.592, "r_y2": 584.267, "r_x3": 134.765, "r_y3": 584.267, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.519, "r_x1": 237.316, "r_y1": 605.519, "r_x2": 237.316, "r_y2": 596.223, "r_x3": 134.765, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on di", "orig": "mAP scores even on di", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.317, "r_y0": 605.519, "r_x1": 245.616, "r_y1": 605.519, "r_x2": 245.616, "r_y2": 596.223, "r_x3": 237.317, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "ffi", "orig": "ffi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.617, "r_y0": 605.519, "r_x1": 480.58, "r_y1": 605.519, "r_x2": 480.58, "r_y2": 596.223, "r_x3": 245.617, "r_y3": 596.223, "coord_origin": "TOPLEFT"}, "text": "cult financial tables (FinTabNet) that contain sparse", "orig": "cult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.4739999999999, "r_x1": 206.801, "r_y1": 617.4739999999999, "r_x2": 206.801, "r_y2": 608.178, "r_x3": 134.765, "r_y3": 608.178, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.133, "r": 480.599, "b": 665.294, "coord_origin": "TOPLEFT"}, "confidence": 0.9850137829780579, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 629.429, "r_x1": 205.6, "r_y1": 629.429, "r_x2": 205.6, "r_y2": 620.133, "r_x3": 149.709, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 629.429, "r_x1": 224.409, "r_y1": 629.429, "r_x2": 224.409, "r_y2": 620.133, "r_x3": 210.578, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 629.429, "r_x1": 257.737, "r_y1": 629.429, "r_x2": 257.737, "r_y2": 620.133, "r_x3": 229.383, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 629.429, "r_x1": 307.321, "r_y1": 629.429, "r_x2": 307.321, "r_y2": 620.133, "r_x3": 262.722, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 629.429, "r_x1": 480.595, "r_y1": 629.429, "r_x2": 480.595, "r_y2": 620.133, "r_x3": 312.301, "r_y3": 620.133, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 641.384, "r_x1": 480.599, "r_y1": 641.384, "r_x2": 480.599, "r_y2": 632.088, "r_x3": 134.765, "r_y3": 632.088, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 119, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 653.3389999999999, "r_x1": 480.594, "r_y1": 653.3389999999999, "r_x2": 480.594, "r_y2": 644.043, "r_x3": 134.765, "r_y3": 644.043, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 120, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 665.294, "r_x1": 405.8, "r_y1": 665.294, "r_x2": 405.8, "r_y2": 655.998, "r_x3": 134.765, "r_y3": 655.998, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 93.66300000000001, "r": 447.545, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9373534917831421, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.03700000000003, "r_x1": 447.545, "r_y1": 102.03700000000003, "r_x2": 447.545, "r_y2": 93.66300000000001, "r_x3": 194.478, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 93.66300000000001, "r": 480.593, "b": 102.03700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8858680725097656, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.03700000000003, "r_x1": 480.593, "r_y1": 102.03700000000003, "r_x2": 480.593, "r_y2": 93.66300000000001, "r_x3": 475.984, "r_y3": 93.66300000000001, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.doctags.txt b/tests/data/groundtruth/docling_v2/2305.03393v1.doctags.txt index 3c1ba0e7..f338de02 100644 --- a/tests/data/groundtruth/docling_v2/2305.03393v1.doctags.txt +++ b/tests/data/groundtruth/docling_v2/2305.03393v1.doctags.txt @@ -1,147 +1,149 @@ arXiv:2305.03393v1 [cs.CV] 5 May 2023 -Optimized Table Tokenization for Table Structure -Maksym Lysak +Optimized Table Tokenization for Table Structure Recognition +Maksym Lysak [0000 - 0002 - 3723 - 6960] , Ahmed Nassar [0000 - 0002 - 9468 - 0822] , Nikolaos Livathinos [0000 - 0001 - 8513 - 3491] , Christoph Auer [0000 - 0001 - 5761 - 0422] , [0000 - 0002 - 8088 - 0823] and Peter Staar IBM Research {mly,ahn,nli,cau,taa}@zurich.ibm.com -Abstract. -Keywords: -1 -Tables are ubiquitous in documents such as scientific papers, patents, reports, -In modern document understanding systems [1,15], table extraction is typi- +Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community. +Keywords: Table Structure Recognition · Data Representation · Transformers · Optimization. +1 Introduction +Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods. +In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of 2 M. Lysak, et al. -Fig. 1. -today, -Recently +Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL). +today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]. +Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR. Optimized Table Tokenization for Table Structure Recognition 3 -While the majority of research in TSR is currently focused on the develop- -The main contribution of this paper is the introduction of a new optimised ta- -The paper is structured as follows. In section 2, we give an overview of the -2 -Approaches to formalize the logical structure and layout of tables in electronic +While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments. +The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML. +The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps. +2 Related Work +Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell. 4 M. Lysak, et al. -Other work [20] aims at predicting a grid for each table and deciding which cells -Within the -Im2Seq approaches have shown to be well-suited for the TSR task and allow a -3 -All known Im2Seq based models for TSR fundamentally work in similar ways. +Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence. +Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. +Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task. +3 Problem Statement +All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab- Optimized Table Tokenization for Table Structure Recognition 5 -ulary and can be interpreted as a table structure. For example, with the HTML -Fig. 2. -Obviously, HTML and other general-purpose markup languages were not de- -Additionally, it would be desirable if the representation would easily allow -In a valid HTML table, the token sequence must describe a 2D grid of table +ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary. +Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet. +Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. +Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible. +In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence 6 M. Lysak, et al. -generation. Implicitly, this also means that Im2Seq models need to learn these -In practice, we observe two major issues with prediction quality when train- -4 -To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, -4.1 -In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines +generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output. +In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content. +4 Optimised Table Structure Language +To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture. +4.1 Language Definition +In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid. The OTSL vocabulary is comprised of the following tokens: -- -- -- -- -- +-"C" cell a new table cell that either has or does not have cell content +-"L" cell left-looking cell , merging with the left neighbor cell to create a span +-"U" cell up-looking cell , merging with the upper neighbor cell to create a span +-"X" cell cross cell , to merge with both left and upper neighbor cells +-"NL" new-line , switch to the next row. -A notable attribute of OTSL is that it has the capability of achieving lossless +A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML. Optimized Table Tokenization for Table Structure Recognition 7 -Fig. 3. -4.2 +Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding +4.2 Language Syntax The OTSL representation follows these syntax rules: -1. -2. +1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell. +2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell. -3. -4. -5. -6. -The left neighbour of an "X" cell must be either another "X" cell or a "U" +3. Cross cell rule : +The left neighbour of an "X" cell must be either another "X" cell or a "U" cell, and the upper neighbour of an "X" cell must be either another "X" cell or an "L" cell. +4. First row rule : Only "L" cells and "C" cells are allowed in the first row. +5. First column rule : Only "U" cells and "C" cells are allowed in the first column. +6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token. -The application of these rules gives OTSL a set of unique properties. First -These characteristics can be easily learned by sequence generator networks, +The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a "C"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid. +These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern 8 M. Lysak, et al. -reduces significantly the column drift seen in the HTML based models (see Fig- -4.3 -The design of OTSL allows to validate a table structure easily on an unfinished -5 -To evaluate the impact of OTSL on prediction accuracy and inference times, we -Fig. 4. -We rely on standard metrics such as Tree Edit Distance score (TEDs) for +reduces significantly the column drift seen in the HTML based models (see Figure 5). +4.3 Error-detection and -mitigation +The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied. +5 Experiments +To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available. +Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach. +We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in Optimized Table Tokenization for Table Structure Recognition 9 -order to compute the TED score. Inference timing results for all experiments -5.1 -We have chosen the PubTabNet data set to perform HPO, since it includes a -Table -5.2 -We picked the model parameter configuration that produced the best prediction -Additionally, +order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. +5.1 Hyper Parameter Optimization +We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. +# enc-layers# dec-layersLanguageTEDsmAPInferencesimplecomplexall(0.75)time (secs)66OTSL HTML0.965 0.9690.934 0.9270.955 0.9550.88 0.8572.73 5.3944OTSL HTML0.938 0.9520.904 0.9090.927 0.9380.853 0.8431.97 3.7724OTSL HTML0.923 0.9450.897 0.9010.915 0.9310.859 0.8341.91 3.8142OTSL HTML0.952 0.9440.92 0.9030.942 0.9310.857 0.8241.22 2Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. +5.2 Quantitative Results +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables. +Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. 10 M. Lysak, et al. -Table 2. -5.3 -To illustrate -Fig. 5. +Data setLanguageTEDsmAP(0.75)Inference time (secs)simplecomplexallPubTabNetOTSL HTML0.965 0.9690.934 0.9270.955 0.9550.88 0.8572.73 5.39FinTabNetOTSL HTML0.955 0.9170.961 0.9220.959 0.920.862 0.7221.85 3.26PubTables-1MOTSL HTML0.987 0.9830.964 0.9440.977 0.9660.896 0.8891.79 3.26Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). +5.3 Qualitative Results +To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes. +Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. μ +μ +≥ Optimized Table Tokenization for Table Structure Recognition 11 -Fig. 6. +Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406_003_01.png" PubTabNet. 12 M. Lysak, et al. -6 -We demonstrated that representing tables in HTML for the task of table struc- -First and foremost, given the same network configuration, inference time for -Secondly, OTSL has more inherent structure and a significantly restricted vo- +6 Conclusion +We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits. +First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1). +Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation. References -1. -2. -3. -4. +1. 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Lysak, et al."}, {"self_ref": "#/texts/13", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 134.765, "t": 665.666, "r": 162.644, "b": 657.74, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Fig. 1.", "text": "Fig. 1."}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.765, "t": 339.686, "r": 161.329, "b": 330.889, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "today,", "text": "today,"}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 149.709, "t": 267.44900000000007, "r": 187.471, "b": 258.65200000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Recently", "text": "Recently"}, {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/17", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 475.984, "t": 700.507, "r": 480.591, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3"}, {"self_ref": "#/texts/18", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 149.709, "t": 673.066, "r": 480.592, "b": 664.269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "While the majority of research in TSR is currently focused on the develop-", "text": "While the majority of research in TSR is currently focused on the develop-"}, {"self_ref": "#/texts/19", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 149.709, "t": 577.164, "r": 480.594, "b": 568.367, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text": "The main contribution of this paper is the introduction of a new optimised ta-"}, {"self_ref": "#/texts/20", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 149.709, "t": 457.352, "r": 480.588, "b": 448.555, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text": "The paper is structured as follows. In section 2, we give an overview of the"}, {"self_ref": "#/texts/21", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 134.765, "t": 329.912, "r": 141.489, "b": 319.344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2", "level": 1}, {"self_ref": "#/texts/22", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.765, "t": 303.314, "r": 480.591, "b": 294.517, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 80]}], "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text": "Approaches to formalize the logical structure and layout of tables in electronic"}, {"self_ref": "#/texts/23", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 700.507, "r": 139.372, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/24", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 167.813, "t": 700.507, "r": 231.722, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/25", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 673.066, "r": 480.594, "b": 664.269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text": "Other work [20] aims at predicting a grid for each table and deciding which cells"}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 149.709, "t": 529.343, "r": 199.213, "b": 520.546, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Within the", "text": "Within the"}, {"self_ref": "#/texts/27", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 149.709, "t": 301.934, "r": 480.594, "b": 293.137, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a"}, {"self_ref": "#/texts/28", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 186.45000000000005, "r": 141.489, "b": 175.88200000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3", "level": 1}, {"self_ref": "#/texts/29", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 159.85199999999998, "r": 480.591, "b": 151.05500000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text": "All known Im2Seq based models for TSR fundamentally work in similar ways."}, {"self_ref": "#/texts/30", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/31", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 475.984, "t": 700.507, "r": 480.591, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5"}, {"self_ref": "#/texts/32", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.765, "t": 673.066, "r": 480.586, "b": 664.269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "ulary and can be interpreted as a table structure. For example, with the HTML", "text": "ulary and can be interpreted as a table structure. For example, with the HTML"}, {"self_ref": "#/texts/33", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 145.607, "t": 570.921, "r": 173.486, "b": 562.995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Fig. 2.", "text": "Fig. 2."}, {"self_ref": "#/texts/34", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 149.709, "t": 423.793, "r": 480.593, "b": 414.996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text": "Obviously, HTML and other general-purpose markup languages were not de-"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 149.709, "t": 255.957, "r": 480.593, "b": 247.15999999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "Additionally, it would be desirable if the representation would easily allow", "text": "Additionally, it would be desirable if the representation would easily allow"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 149.709, "t": 207.673, "r": 480.593, "b": 198.87599999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text": "In a valid HTML table, the token sequence must describe a 2D grid of table"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 700.507, "r": 139.372, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6"}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 167.813, "t": 700.507, "r": 231.722, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 673.066, "r": 480.595, "b": 664.269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text": "generation. Implicitly, this also means that Im2Seq models need to learn these"}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.709, "t": 648.517, "r": 480.59, "b": 639.72, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "In practice, we observe two major issues with prediction quality when train-", "text": "In practice, we observe two major issues with prediction quality when train-"}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 471.369, "r": 141.489, "b": 460.801, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4", "level": 1}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 442.883, "r": 480.591, "b": 434.086, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 326.128, "r": 149.402, "b": 317.321, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "4.1", "text": "4.1", "level": 1}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 303.002, "r": 480.589, "b": 294.205, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 80]}], "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines"}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.709, "t": 266.49800000000005, "r": 409.311, "b": 257.701, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "The OTSL vocabulary is comprised of the following tokens:", "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 244.02999999999997, "r": 146.72, "b": 235.22299999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 231.437, "r": 146.72, "b": 222.63, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 206.88800000000003, "r": 146.72, "b": 198.08100000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 182.34000000000003, "r": 146.72, "b": 173.53300000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/50", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 169.74599999999998, "r": 146.72, "b": 160.93899999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.709, "t": 147.89699999999993, "r": 480.593, "b": 139.10000000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text": "A notable attribute of OTSL is that it has the capability of achieving lossless"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 475.984, "t": 700.507, "r": 480.591, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 134.765, "t": 666.201, "r": 162.644, "b": 658.275, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Fig. 3.", "text": "Fig. 3."}, {"self_ref": "#/texts/55", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 334.511, "t": 519.112, "r": 426.599, "b": 513.0, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "text": "4 - 2d merges: \"C\", \"L\", \"U\", \"X\"", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 134.765, "t": 486.704, "r": 149.402, "b": 477.897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "4.2", "text": "4.2", "level": 1}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.765, "t": 466.752, "r": 363.796, "b": 457.955, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "The OTSL representation follows these syntax rules:", "text": "The OTSL representation follows these syntax rules:"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 444.819, "r": 146.72, "b": 436.022, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/59", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 420.905, "r": 146.72, "b": 412.108, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/60", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 396.992, "r": 146.72, "b": 388.195, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3.", "level": 1}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 349.164, "r": 146.72, "b": 340.367, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "4.", "text": "4.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 337.206, "r": 146.72, "b": 328.409, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "5.", "text": "5.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/63", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 313.293, "r": 146.72, "b": 304.496, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "6.", "text": "6.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 151.701, "t": 385.033, "r": 480.592, "b": 376.236, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 74]}], "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 149.709, "t": 279.4069999999999, "r": 480.596, "b": 270.61, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "The application of these rules gives OTSL a set of unique properties. First", "text": "The application of these rules gives OTSL a set of unique properties. First"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 149.709, "t": 147.89699999999993, "r": 480.592, "b": 139.10000000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "These characteristics can be easily learned by sequence generator networks,", "text": "These characteristics can be easily learned by sequence generator networks,"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 700.507, "r": 139.372, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8"}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 167.813, "t": 700.507, "r": 231.722, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 673.066, "r": 480.589, "b": 664.269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text": "reduces significantly the column drift seen in the HTML based models (see Fig-"}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 630.443, "r": 149.402, "b": 621.636, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "4.3", "text": "4.3", "level": 1}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 609.718, "r": 480.596, "b": 600.921, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 79]}], "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text": "The design of OTSL allows to validate a table structure easily on an unfinished"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 470.836, "r": 141.489, "b": 460.268, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5", "level": 1}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 444.75, "r": 480.594, "b": 435.953, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 307.352, "r": 162.644, "b": 299.426, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Fig. 4.", "text": "Fig. 4."}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 149.709, "t": 171.80700000000002, "r": 480.588, "b": 163.01, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 475.984, "t": 700.507, "r": 480.591, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 673.066, "r": 480.594, "b": 664.269, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "order to compute the TED score. Inference timing results for all experiments", "text": "order to compute the TED score. Inference timing results for all experiments"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 622.814, "r": 149.402, "b": 614.007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "5.1", "text": "5.1", "level": 1}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 606.414, "r": 480.592, "b": 597.617, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 516.928, "r": 160.118, "b": 509.002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 5]}], "orig": "Table", "text": "Table"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 283.848, "r": 149.402, "b": 275.04099999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "5.2", "text": "5.2", "level": 1}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 267.44900000000007, "r": 480.591, "b": 258.65200000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "We picked the model parameter configuration that produced the best prediction", "text": "We picked the model parameter configuration that produced the best prediction"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 149.709, "t": 171.80700000000002, "r": 205.597, "b": 163.01, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Additionally,", "text": "Additionally,"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 700.507, "r": 143.979, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 167.821, "t": 700.507, "r": 231.72, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/87", "parent": {"cref": "#/tables/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 676.164, "r": 173.094, "b": 668.238, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Table 2.", "text": "Table 2."}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 503.085, "r": 149.402, "b": 494.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "5.3", "text": "5.3", "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 482.139, "r": 189.59, "b": 473.342, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "To illustrate", "text": "To illustrate"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 394.41, "r": 162.644, "b": 386.484, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Fig. 5.", "text": "Fig. 5."}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 11, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 11, "bbox": {"l": 471.376, "t": 700.507, "r": 480.59, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 134.765, "t": 666.201, "r": 162.644, "b": 658.275, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Fig. 6.", "text": "Fig. 6."}, {"self_ref": "#/texts/94", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 700.507, "r": 143.979, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 167.821, "t": 700.507, "r": 231.72, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al."}, {"self_ref": "#/texts/96", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 674.451, "r": 141.489, "b": 663.883, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6", "level": 1}, {"self_ref": "#/texts/97", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 645.136, "r": 480.595, "b": 636.339, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text": "We demonstrated that representing tables in HTML for the task of table struc-"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 149.709, "t": 584.556, "r": 480.593, "b": 575.759, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "First and foremost, given the same network configuration, inference time for", "text": "First and foremost, given the same network configuration, inference time for"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 149.709, "t": 464.201, "r": 480.59, "b": 455.404, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 298.179, "r": 197.686, "b": 287.611, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "level": 1}, {"self_ref": "#/texts/101", 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"arXiv:2305.03393v1 [cs.CV] 5 May 2023", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 676.101, "r": 480.597, "b": 645.486, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/2", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 139.343, "t": 621.841, "r": 476.013, "b": 591.836, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 208]}], "orig": "Maksym Lysak [0000 - 0002 - 3723 - 6960] , Ahmed Nassar [0000 - 0002 - 9468 - 0822] , Nikolaos Livathinos [0000 - 0001 - 8513 - 3491] , Christoph Auer [0000 - 0001 - 5761 - 0422] , [0000 - 0002 - 8088 - 0823]", "text": "Maksym Lysak [0000 - 0002 - 3723 - 6960] , Ahmed Nassar [0000 - 0002 - 9468 - 0822] , Nikolaos Livathinos [0000 - 0001 - 8513 - 3491] , Christoph Auer [0000 - 0001 - 5761 - 0422] , [0000 - 0002 - 8088 - 0823]", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/3", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 229.521, "t": 596.416, "r": 298.609, "b": 587.619, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "and Peter Staar", "text": "and Peter Staar", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/4", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 279.105, "t": 577.074, "r": 336.251, "b": 565.785, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "IBM Research", "text": "IBM Research", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/5", "parent": {"cref": "#/groups/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 222.966, "t": 563.191, "r": 392.39, "b": 555.722, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "text": "{mly,ahn,nli,cau,taa}@zurich.ibm.com", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.111, "t": 523.914, "r": 452.249, "b": 326.324, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1198]}], "orig": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 163.111, "t": 315.521, "r": 452.241, "b": 293.273, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 269.88, "r": 228.934, "b": 259.312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 Introduction", "text": "1 Introduction", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 243.71299999999997, "r": 480.596, "b": 163.18499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 500]}], "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 134.765, "t": 159.85199999999998, "r": 480.596, "b": 127.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 134.765, "t": 700.507, "r": 139.372, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 2, "bbox": {"l": 167.813, "t": 700.507, "r": 231.722, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/13", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 134.765, "t": 667.881, "r": 480.592, "b": 590.838, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 574]}], "orig": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. 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On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 134.765, "t": 267.44900000000007, "r": 480.595, "b": 127.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 911]}], "orig": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 3, "bbox": {"l": 475.984, "t": 700.507, "r": 480.591, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "3", "text": "3", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.765, "t": 673.066, "r": 480.592, "b": 580.583, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 584]}], "orig": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.765, "t": 577.164, "r": 480.596, "b": 460.77, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 721]}], "orig": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.765, "t": 457.352, "r": 480.596, "b": 352.913, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 626]}], "orig": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 3, "bbox": {"l": 134.765, "t": 329.912, "r": 236.769, "b": 319.344, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 Related Work", "text": "2 Related Work", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 134.765, "t": 303.314, "r": 484.121, "b": 127.144, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1161]}], "orig": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 700.507, "r": 139.372, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 4, "bbox": {"l": 167.813, "t": 700.507, "r": 231.722, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 673.066, "r": 480.596, "b": 532.762, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 939]}], "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 529.343, "r": 480.596, "b": 305.353, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1404]}], "orig": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 301.934, "r": 480.594, "b": 209.45100000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 572]}], "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 186.45000000000005, "r": 269.624, "b": 175.88200000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "3 Problem Statement", "text": "3 Problem Statement", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 134.765, "t": 159.85199999999998, "r": 480.594, "b": 127.144, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/85", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 5, "bbox": {"l": 475.984, "t": 700.507, "r": 480.591, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "5", "text": "5", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.765, "t": 673.066, "r": 480.594, "b": 604.493, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 422]}], "orig": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/87", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 145.607, "t": 573.136, "r": 469.752, "b": 561.847, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.765, "t": 423.793, "r": 480.595, "b": 259.57899999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1021]}], "orig": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.765, "t": 255.957, "r": 480.593, "b": 211.29399999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 313]}], "orig": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/90", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 134.765, "t": 207.673, "r": 480.595, "b": 127.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 542]}], "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 700.507, "r": 139.372, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "6", "text": "6", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 6, "bbox": {"l": 167.813, "t": 700.507, "r": 231.722, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/93", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 673.066, "r": 480.595, "b": 652.314, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 132]}], "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/94", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 648.517, "r": 480.596, "b": 496.258, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 977]}], "orig": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/95", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 471.369, "r": 372.508, "b": 460.801, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "4 Optimised Table Structure Language", "text": "4 Optimised Table Structure Language", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/96", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 442.883, "r": 480.595, "b": 350.4, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 563]}], "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/97", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 326.128, "r": 261.801, "b": 317.321, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "4.1 Language Definition", "text": "4.1 Language Definition", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/98", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 303.002, "r": 480.589, "b": 270.294, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 165]}], "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/99", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 149.709, "t": 266.49800000000005, "r": 409.311, "b": 257.701, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "The OTSL vocabulary is comprised of the following tokens:", "text": "The OTSL vocabulary is comprised of the following tokens:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/100", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 244.02999999999997, "r": 460.544, "b": 235.22299999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "-\"C\" cell a new table cell that either has or does not have cell content", "text": "-\"C\" cell a new table cell that either has or does not have cell content", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 231.437, "r": 480.594, "b": 210.67499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "text": "-\"L\" cell left-looking cell , merging with the left neighbor cell to create a span", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 206.88800000000003, "r": 480.588, "b": 186.12599999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "text": "-\"U\" cell up-looking cell , merging with the upper neighbor cell to create a span", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 182.34000000000003, "r": 454.555, "b": 173.53300000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 140.993, "t": 169.74599999999998, "r": 328.617, "b": 160.93899999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "-\"NL\" new-line , switch to the next row.", "text": "-\"NL\" new-line , switch to the next row.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 134.765, "t": 147.89699999999993, "r": 480.593, "b": 127.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 99]}], "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 7, "bbox": {"l": 475.984, "t": 700.507, "r": 480.591, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "7", "text": "7", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/108", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 134.765, "t": 668.416, "r": 480.587, "b": 635.209, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 207]}], "orig": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/109", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 374.493, "t": 623.946, "r": 428.861, "b": 614.119, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "C L C L L", "text": "C L C L L", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/110", "parent": {"cref": "#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 373.769, "t": 599.386, "r": 448.17, "b": 589.787, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "C C C C C NL", "text": "C C C C C NL", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/111", "parent": {"cref": 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"#/pictures/2"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 169.747, "t": 548.689, "r": 175.271, "b": 541.049, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "E", "text": "E", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/147", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 134.765, "t": 486.704, "r": 246.652, "b": 477.897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "4.2 Language Syntax", "text": "4.2 Language Syntax", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/148", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.765, "t": 466.752, "r": 363.796, "b": 457.955, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "The OTSL representation follows these syntax rules:", "text": "The OTSL representation follows these syntax rules:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/149", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 444.829, "r": 480.589, "b": 424.066, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 108]}], "orig": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/150", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 420.915, "r": 480.592, "b": 400.153, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 106]}], "orig": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/151", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 397.002, "r": 226.074, "b": 388.195, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "3. Cross cell rule :", "text": "3. Cross cell rule :", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/152", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 151.701, "t": 385.033, "r": 480.592, "b": 352.326, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 167]}], "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 349.174, "r": 474.59, "b": 340.367, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 78]}], "orig": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 337.216, "r": 480.588, "b": 316.454, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 7, "bbox": {"l": 138.973, "t": 313.303, "r": 480.595, "b": 292.54, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 144]}], "orig": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.765, "t": 279.4069999999999, "r": 480.596, "b": 151.058, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 848]}], "orig": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/157", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 134.765, "t": 147.89699999999993, "r": 480.593, "b": 127.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 153]}], "orig": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 700.507, "r": 139.372, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "8", "text": "8", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 8, "bbox": {"l": 167.813, "t": 700.507, "r": 231.722, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/160", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 673.066, "r": 480.589, "b": 652.314, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/161", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 630.443, "r": 319.347, "b": 621.636, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "4.3 Error-detection and -mitigation", "text": "4.3 Error-detection and -mitigation", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/162", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 609.718, "r": 480.596, "b": 493.324, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 797]}], "orig": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/163", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 470.836, "r": 229.035, "b": 460.268, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "5 Experiments", "text": "5 Experiments", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/164", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 444.75, "r": 480.595, "b": 340.312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 309.567, "r": 480.591, "b": 287.319, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 104]}], "orig": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 147.3, "t": 251.23800000000006, "r": 149.407, "b": 248.909, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/167", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 150.809, "t": 251.23800000000006, "r": 155.721, "b": 248.909, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "Item", "text": "Item", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/168", "parent": {"cref": "#/pictures/3"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 162.76, "t": 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null, "hyperlink": null}, {"self_ref": "#/texts/227", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 134.765, "t": 171.80700000000002, "r": 480.592, "b": 127.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 299]}], "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/228", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 194.478, "t": 700.507, "r": 447.543, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Optimized Table Tokenization for Table Structure Recognition", "text": "Optimized Table Tokenization for Table Structure Recognition", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 9, "bbox": {"l": 475.984, "t": 700.507, "r": 480.591, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "9", "text": "9", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 673.066, "r": 480.596, "b": 640.358, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 163]}], "orig": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 622.814, "r": 318.448, "b": 614.007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "5.1 Hyper Parameter Optimization", "text": "5.1 Hyper Parameter Optimization", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/232", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 606.414, "r": 480.593, "b": 537.841, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 423]}], "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/233", "parent": {"cref": "#/tables/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 519.143, "r": 480.595, "b": 464.018, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 398]}], "orig": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/234", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 283.848, "r": 264.403, "b": 275.04099999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "5.2 Quantitative Results", "text": "5.2 Quantitative Results", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/235", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 267.44900000000007, "r": 480.596, "b": 174.96500000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 555]}], "orig": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/236", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 134.765, "t": 171.80700000000002, "r": 480.596, "b": 127.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 289]}], "orig": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/237", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 700.507, "r": 143.979, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/238", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 10, "bbox": {"l": 167.821, "t": 700.507, "r": 231.72, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/239", "parent": {"cref": "#/tables/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 678.379, "r": 480.594, "b": 645.172, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 192]}], "orig": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "text": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/240", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 503.085, "r": 257.087, "b": 494.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "5.3 Qualitative Results", "text": "5.3 Qualitative Results", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/241", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 482.139, "r": 480.59, "b": 425.522, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/242", "parent": {"cref": "#/pictures/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 134.765, "t": 396.625, "r": 480.591, "b": 352.283, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "Fig. 5. 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Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/334", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.505, "t": 479.448, "r": 177.596, "b": 471.665, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "B", "text": "B", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/335", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 171.058, "t": 299.246, "r": 177.149, "b": 291.463, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "C", "text": "C", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/336", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 164.442, "r": 374.963, "b": 158.60500000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "Incorrect end of HTML sequence", "text": "Incorrect end of HTML sequence", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/337", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 283.047, "t": 174.56600000000003, "r": 398.06, "b": 168.72900000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Horizontally merged cells are not present", "text": "Horizontally merged cells are not present", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/338", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 293.642, "t": 326.326, "r": 437.508, "b": 320.489, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Repeating pattern is well represented in predictions", "text": "Repeating pattern is well represented in predictions", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/339", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.891, "t": 503.564, "r": 239.235, "b": 497.727, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "Repeating pattern of", "text": "Repeating pattern of", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/340", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 181.891, "t": 497.03, "r": 251.529, "b": 491.193, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "horizontally merged cells", "text": "horizontally merged cells", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/341", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 247.834, "t": 605.038, "r": 253.613, "b": 597.674, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "A", "text": "A", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/342", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 292.19, "t": 184.11799999999994, "r": 381.547, "b": 178.28099999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Bounding box drifting at the end", "text": "Bounding box drifting at the end", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/343", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.374, "t": 410.637, "r": 180.173, "b": 388.599, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "OTSL", "text": "OTSL", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/344", "parent": {"cref": "#/pictures/5"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 172.374, "t": 236.22299999999996, "r": 180.174, "b": 213.25199999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "HTML", "text": "HTML", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/345", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 700.507, "r": 143.979, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "12", "text": "12", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/346", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 12, "bbox": {"l": 167.821, "t": 700.507, "r": 231.72, "b": 689.218, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "M. Lysak, et al.", "text": "M. Lysak, et al.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/347", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 674.451, "r": 219.255, "b": 663.883, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "6 Conclusion", "text": "6 Conclusion", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/348", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 645.136, "r": 480.596, "b": 588.518, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 330]}], "orig": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/349", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 584.556, "r": 480.595, "b": 468.163, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 724]}], "orig": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/350", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 464.201, "r": 480.595, "b": 323.897, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 926]}], "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/351", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 134.765, "t": 298.179, "r": 197.686, "b": 287.611, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "References", "text": "References", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/352", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 139.371, "t": 271.398, "r": 480.592, "b": 228.12799999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 270]}], "orig": "1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. 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"start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.923 0.945", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 418.361, "r": 347.704, "b": 442.601, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.897 0.901", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 418.361, "r": 386.249, "b": 441.453, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.915 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 420.576, "r": 418.779, "b": 442.601, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 6, 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false, "row_header": false, "row_section": false}, {"bbox": {"l": 245.176, "t": 444.662, "r": 272.94, "b": 468.903, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "OTSL HTML", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.017, "t": 444.662, "r": 310.004, "b": 468.903, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 3, "end_col_offset_idx": 4, "text": "0.952 0.944", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 326.717, "t": 444.662, "r": 347.704, "b": 468.903, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 4, "end_col_offset_idx": 5, "text": "0.92 0.903", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 362.088, "t": 446.877, "r": 386.249, "b": 468.903, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 5, "end_col_offset_idx": 6, "text": "0.942 0.931", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 394.618, "t": 446.877, "r": 418.779, "b": 468.903, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 6, "end_col_offset_idx": 7, "text": "0.857 0.824", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 439.527, "t": 446.877, "r": 458.384, "b": 468.903, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 7, "end_col_offset_idx": 8, "text": "1.22 2", "column_header": false, "row_header": false, "row_section": false}], "num_rows": 6, "num_cols": 8, "grid": [[{"bbox": {"l": 144.592, "t": 339.457, "r": 183.828, "b": 363.697, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "# enc-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 192.195, "t": 339.457, "r": 231.431, "b": 363.697, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "# dec-layers", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 239.798, "t": 344.936, "r": 278.318, "b": 356.225, "coord_origin": "TOPLEFT"}, "row_span": 2, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Language", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 339.457, "r": 348.264, "b": 350.746, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 339.457, "r": 348.264, "b": 350.746, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 324.67, "t": 339.457, "r": 348.264, "b": 350.746, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 3, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 3, "end_col_offset_idx": 6, "text": "TEDs", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 396.271, "t": 339.457, "r": 417.127, "b": 350.746, "coord_origin": "TOPLEFT"}, "row_span": 1, 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null, "page_no": 9}, "10": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 10}, "11": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 11}, "12": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 12}, "13": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 13}, "14": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 14}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/2305.03393v1.md b/tests/data/groundtruth/docling_v2/2305.03393v1.md index 073374d4..f70a21b6 100644 --- a/tests/data/groundtruth/docling_v2/2305.03393v1.md +++ b/tests/data/groundtruth/docling_v2/2305.03393v1.md @@ -1,6 +1,6 @@ -## Optimized Table Tokenization for Table Structure +## Optimized Table Tokenization for Table Structure Recognition -Maksym Lysak +Maksym Lysak [0000 - 0002 - 3723 - 6960] , Ahmed Nassar [0000 - 0002 - 9468 - 0822] , Nikolaos Livathinos [0000 - 0001 - 8513 - 3491] , Christoph Auer [0000 - 0001 - 5761 - 0422] , [0000 - 0002 - 8088 - 0823] and Peter Staar @@ -8,176 +8,195 @@ IBM Research {mly,ahn,nli,cau,taa}@zurich.ibm.com -Abstract. +Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community. -Keywords: +Keywords: Table Structure Recognition · Data Representation · Transformers · Optimization. -## 1 +## 1 Introduction -Tables are ubiquitous in documents such as scientific papers, patents, reports, +Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods. -In modern document understanding systems [1,15], table extraction is typi- +In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of -Fig. 1. +Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL). -today, +today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]. -Recently +Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR. -While the majority of research in TSR is currently focused on the develop- +While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments. -The main contribution of this paper is the introduction of a new optimised ta- +The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML. -The paper is structured as follows. In section 2, we give an overview of the +The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps. -## 2 +## 2 Related Work -Approaches to formalize the logical structure and layout of tables in electronic +Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell. -Other work [20] aims at predicting a grid for each table and deciding which cells +Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence. -Within the +Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. -Im2Seq approaches have shown to be well-suited for the TSR task and allow a +Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task. -## 3 +## 3 Problem Statement -All known Im2Seq based models for TSR fundamentally work in similar ways. +All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab- -ulary and can be interpreted as a table structure. For example, with the HTML +ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary. -Fig. 2. +Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet. -Obviously, HTML and other general-purpose markup languages were not de- +Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. -Additionally, it would be desirable if the representation would easily allow +Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible. -In a valid HTML table, the token sequence must describe a 2D grid of table +In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence -generation. Implicitly, this also means that Im2Seq models need to learn these +generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output. -In practice, we observe two major issues with prediction quality when train- +In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content. -## 4 +## 4 Optimised Table Structure Language -To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, +To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture. -## 4.1 +## 4.1 Language Definition -In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines +In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid. The OTSL vocabulary is comprised of the following tokens: -- - -- - -- - -- - -- - +- -"C" cell a new table cell that either has or does not have cell content +- -"L" cell left-looking cell , merging with the left neighbor cell to create a span +- -"U" cell up-looking cell , merging with the upper neighbor cell to create a span +- -"X" cell cross cell , to merge with both left and upper neighbor cells +- -"NL" new-line , switch to the next row. -A notable attribute of OTSL is that it has the capability of achieving lossless +A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML. -Fig. 3. +Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding -## 4.2 +## 4.2 Language Syntax The OTSL representation follows these syntax rules: -- 1. -- 2. +- 1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell. +- 2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell. -## 3. +## 3. Cross cell rule : -- 4. -- 5. -- 6. -- The left neighbour of an "X" cell must be either another "X" cell or a "U" +- The left neighbour of an "X" cell must be either another "X" cell or a "U" cell, and the upper neighbour of an "X" cell must be either another "X" cell or an "L" cell. +- 4. First row rule : Only "L" cells and "C" cells are allowed in the first row. +- 5. First column rule : Only "U" cells and "C" cells are allowed in the first column. +- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token. -The application of these rules gives OTSL a set of unique properties. First +The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a "C"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid. -These characteristics can be easily learned by sequence generator networks, +These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern -reduces significantly the column drift seen in the HTML based models (see Fig- +reduces significantly the column drift seen in the HTML based models (see Figure 5). -## 4.3 +## 4.3 Error-detection and -mitigation -The design of OTSL allows to validate a table structure easily on an unfinished +The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied. -## 5 +## 5 Experiments -To evaluate the impact of OTSL on prediction accuracy and inference times, we +To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available. -Fig. 4. +Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach. -We rely on standard metrics such as Tree Edit Distance score (TEDs) for +We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in -order to compute the TED score. Inference timing results for all experiments +order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. -## 5.1 +## 5.1 Hyper Parameter Optimization -We have chosen the PubTabNet data set to perform HPO, since it includes a +We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. -Table +Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -## 5.2 +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP | Inference | +|----------------|----------------|------------|-------------|-------------|-------------|-------------|-------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | (0.75) | time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | -We picked the model parameter configuration that produced the best prediction +## 5.2 Quantitative Results -Additionally, +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables. -Table 2. +Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. -## 5.3 +Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). -To illustrate +| Data set | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) | +|--------------|------------|-------------|-------------|-------------|-------------|-------------------------| +| Data set | Language | simple | complex | all | mAP(0.75) | Inference time (secs) | +| PubTabNet | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| FinTabNet | OTSL HTML | 0.955 0.917 | 0.961 0.922 | 0.959 0.92 | 0.862 0.722 | 1.85 3.26 | +| PubTables-1M | OTSL HTML | 0.987 0.983 | 0.964 0.944 | 0.977 0.966 | 0.896 0.889 | 1.79 3.26 | -Fig. 5. +## 5.3 Qualitative Results + +To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes. + +Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444\_006\_00.png" PubTabNet. μ -Fig. 6. +μ + +≥ + +Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406\_003\_01.png" PubTabNet. -## 6 +## 6 Conclusion -We demonstrated that representing tables in HTML for the task of table struc- +We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits. -First and foremost, given the same network configuration, inference time for +First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1). -Secondly, OTSL has more inherent structure and a significantly restricted vo- +Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. 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"r_y3": 270.30100000000004, "coord_origin": "TOPLEFT"}, "text": "Abstract.", "orig": "Abstract.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.617, "r_y0": 279.375, "r_x1": 452.245, "r_y1": 279.375, "r_x2": 452.245, "r_y2": 268.086, "r_x3": 211.617, "r_y3": 268.086, "coord_origin": "TOPLEFT"}, "text": "Extracting tables from documents is a crucial task in any", "orig": "Extracting tables from documents is a crucial task in any", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 290.334, "r_x1": 452.242, "r_y1": 290.334, "r_x2": 452.242, "r_y2": 279.04499999999996, "r_x3": 163.111, "r_y3": 279.04499999999996, "coord_origin": "TOPLEFT"}, "text": "document conversion pipeline. Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. The benefits of OTSL are that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 399.923, "r_x1": 169.253, "r_y1": 399.923, "r_x2": 169.253, "r_y2": 388.634, "r_x3": 163.111, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.795, "r_y0": 399.923, "r_x1": 452.244, "r_y1": 399.923, "r_x2": 452.244, "r_y2": 388.634, "r_x3": 172.795, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "orig": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 410.882, "r_x1": 452.249, "r_y1": 410.882, "r_x2": 452.249, "r_y2": 399.593, "r_x3": 163.111, "r_y3": 399.593, "coord_origin": "TOPLEFT"}, "text": "the sequence length to half of HTML on average. Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 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"coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, 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"from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.04, "r_x1": 480.592, "r_y1": 569.04, "r_x2": 480.592, "r_y2": 560.2429999999999, "r_x3": 134.765, "r_y3": 560.2429999999999, "coord_origin": "TOPLEFT"}, "text": "manuals, specification sheets or marketing material. They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. Therefore, accurate table extraction is achieved these days with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.815, "r_x1": 279.327, "r_y1": 628.815, "r_x2": 279.327, "r_y2": 620.018, "r_x3": 134.765, "r_y3": 620.018, "coord_origin": "TOPLEFT"}, "text": "machine-learning based methods.", "orig": "machine-learning based methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.708, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 149.708, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "In modern document understanding systems [1,15], table extraction is typi-", "orig": "In modern document understanding systems [1,15], table extraction is typi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.596, "r_y1": 652.9, "r_x2": 480.596, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. 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Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 134.26, "r_x1": 162.644, "r_y1": 134.26, "r_x2": 162.644, "r_y2": 126.33399999999995, "r_x3": 134.765, "r_y3": 126.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 1.", "orig": "Fig. 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.062, "r_y0": 135.40800000000002, "r_x1": 480.591, "r_y1": 135.40800000000002, "r_x2": 480.591, "r_y2": 124.11900000000003, "r_x3": 167.062, "r_y3": 124.11900000000003, "coord_origin": "TOPLEFT"}, "text": "Comparison between HTML and OTSL table structure representation: (A)", "orig": "Comparison between HTML and OTSL table structure representation: (A)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 146.36699999999996, "r_x1": 190.823, "r_y1": 146.36699999999996, "r_x2": 190.823, "r_y2": 135.07799999999997, "r_x3": 134.765, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "table-example", "orig": "table-example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.199, "r_y0": 146.36699999999996, "r_x1": 480.59, "r_y1": 146.36699999999996, "r_x2": 480.59, "r_y2": 135.07799999999997, "r_x3": 195.199, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "with complex row and column headers, including a 2D empty span,", "orig": "with complex row and column headers, including a 2D empty span,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 157.32600000000002, "r_x1": 480.591, "r_y1": 157.32600000000002, "r_x2": 480.591, "r_y2": 146.03700000000003, "r_x3": 134.765, "r_y3": 146.03700000000003, "coord_origin": "TOPLEFT"}, "text": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "orig": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 168.28499999999997, "r_x1": 480.592, "r_y1": 168.28499999999997, "r_x2": 480.592, "r_y2": 156.99599999999998, "r_x3": 134.765, "r_y3": 156.99599999999998, "coord_origin": "TOPLEFT"}, "text": "HTML representation, (D) OTSL representation. 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On the other hand, the problem of table", "orig": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. 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In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. 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Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. 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Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. The graph nodes represent the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "orig": "content of each table cell, an embedding vector from the table image, or geomet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.589, "r_y1": 652.9, "r_x2": 480.589, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "orig": "ric coordinates of the table cell. The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 118.93399999999997, "r": 480.592, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 214.836, "r": 480.594, "b": 223.63300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 149.709, "t": 334.648, "r": 480.588, "b": 343.445, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. 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134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": 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2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 118.93399999999997, "r": 480.592, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While the majority of research in TSR is currently focused on the develop-"}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 214.836, "r": 480.594, "b": 223.63300000000004, "coord_origin": "TOPLEFT"}, 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In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the"}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 141.489, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 480.591, "b": 497.483, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic"}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 118.93399999999997, "r": 480.592, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While the majority of research in TSR is currently focused on the develop-"}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 214.836, "r": 480.594, "b": 223.63300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised ta-"}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 149.709, "t": 334.648, "r": 480.588, "b": 343.445, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the"}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 141.489, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 480.591, "b": 497.483, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic"}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. For each decoded table cell (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.299, "r_y0": 319.275, "r_x1": 356.971, "r_y1": 319.275, "r_x2": 356.971, "r_y2": 310.478, "r_x3": 333.299, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.085, "r_y0": 319.275, "r_x1": 363.725, "r_y1": 319.275, "r_x2": 363.725, "r_y2": 310.478, "r_x3": 357.085, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "),", "orig": "),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.64, "r_y0": 319.275, "r_x1": 480.594, "r_y1": 319.275, "r_x2": 480.594, "r_y2": 310.478, "r_x3": 367.64, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "the attention is passed to", "orig": "the attention is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 148.598, "r_y1": 331.23, "r_x2": 148.598, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.277, "r_y0": 331.23, "r_x1": 166.531, "r_y1": 331.23, "r_x2": 166.531, "r_y2": 322.433, "r_x3": 152.277, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.427, "r_y0": 331.23, "r_x1": 202.111, "r_y1": 331.23, "r_x2": 202.111, "r_y2": 322.433, "r_x3": 170.427, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "decoder", "orig": "decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.864, "r_y0": 331.23, "r_x1": 480.587, "r_y1": 331.23, "r_x2": 480.587, "r_y2": 322.433, "r_x3": 206.864, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to predict the content with an embedded OCR approach. The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.7280000000001, "r_x1": 176.662, "r_y1": 534.7280000000001, "r_x2": 176.662, "r_y2": 525.931, "r_x3": 134.765, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "to deliver", "orig": "to deliver", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.896, "r_y0": 534.7280000000001, "r_x1": 246.551, "r_y1": 534.7280000000001, "r_x2": 246.551, "r_y2": 525.931, "r_x3": 180.896, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.785, "r_y0": 534.7280000000001, "r_x1": 480.59, "r_y1": 534.7280000000001, "r_x2": 480.59, "r_y2": 525.931, "r_x3": 250.785, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "prediction accuracy [9]. This motivated the authors", "orig": "prediction accuracy [9]. 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"coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, 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These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 149.709, "t": 262.65700000000004, "r": 199.213, "b": 271.45399999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 149.709, "t": 490.066, "r": 480.594, "b": 498.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 605.55, "r": 141.489, "b": 616.1179999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.591, "b": 640.9449999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.591, "r_y1": 640.9449999999999, "r_x2": 480.591, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.586, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "orig": "ulary and can be interpreted as a table structure. For example, with the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 221.07899999999995, "r": 173.486, "b": 229.005, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 368.207, "r": 480.593, "b": 377.004, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, 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For example, with the HTML", "orig": "ulary and can be interpreted as a table structure. For example, with the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML"}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 221.07899999999995, "r": 173.486, "b": 229.005, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 2."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 368.207, "r": 480.593, "b": 377.004, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not de-"}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 149.709, "t": 536.043, "r": 480.593, "b": 544.84, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow"}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 584.327, "r": 480.593, "b": 593.124, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.586, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML", "orig": "ulary and can be interpreted as a table structure. For example, with the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML"}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 221.07899999999995, "r": 173.486, "b": 229.005, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 2."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 368.207, "r": 480.593, "b": 377.004, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, 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Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 556.777, "r_x1": 193.206, "r_y1": 556.777, "r_x2": 193.206, "r_y2": 547.98, "r_x3": 151.701, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "\"C\" cell -", "orig": "\"C\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.522, "r_y0": 556.777, "r_x1": 263.466, "r_y1": 556.777, "r_x2": 263.466, "r_y2": 547.98, "r_x3": 196.522, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "a new table cell", "orig": "a new table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.815, "r_y0": 556.777, "r_x1": 460.544, "r_y1": 556.777, "r_x2": 460.544, "r_y2": 547.98, "r_x3": 267.815, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "that either has or does not have cell content", "orig": "that either has or does not have cell content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 569.37, "r_x1": 146.72, "r_y1": 569.37, "r_x2": 146.72, "r_y2": 560.563, "r_x3": 140.993, "r_y3": 560.563, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 569.37, "r_x1": 194.3, "r_y1": 569.37, "r_x2": 194.3, "r_y2": 560.573, "r_x3": 151.701, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": "\"L\" cell -", "orig": "\"L\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 198.659, "r_y0": 569.37, "r_x1": 245.75, "r_y1": 569.37, "r_x2": 245.75, "r_y2": 560.573, "r_x3": 198.659, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": "left-looking", "orig": "left-looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.263, "r_y0": 569.37, "r_x1": 264.518, "r_y1": 569.37, "r_x2": 264.518, "r_y2": 560.573, "r_x3": 250.263, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.518, "r_y0": 569.37, "r_x1": 267.285, "r_y1": 569.37, "r_x2": 267.285, "r_y2": 560.573, "r_x3": 264.518, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 271.638, "r_y0": 569.37, "r_x1": 480.594, "r_y1": 569.37, "r_x2": 480.594, "r_y2": 560.573, "r_x3": 271.638, "r_y3": 560.573, "coord_origin": "TOPLEFT"}, "text": "merging with the left neighbor cell to create a", "orig": "merging with the left neighbor cell to create a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 581.325, "r_x1": 171.676, "r_y1": 581.325, "r_x2": 171.676, "r_y2": 572.528, "r_x3": 151.701, "r_y3": 572.528, "coord_origin": "TOPLEFT"}, "text": "span", "orig": "span", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 593.919, "r_x1": 146.72, "r_y1": 593.919, "r_x2": 146.72, "r_y2": 585.112, "r_x3": 140.993, "r_y3": 585.112, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 593.919, "r_x1": 194.111, "r_y1": 593.919, "r_x2": 194.111, "r_y2": 585.1220000000001, "r_x3": 151.701, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": "\"U\" cell -", "orig": "\"U\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 197.748, "r_y0": 593.919, "r_x1": 259.895, "r_y1": 593.919, "r_x2": 259.895, "r_y2": 585.1220000000001, "r_x3": 197.748, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": "up-looking cell", "orig": "up-looking cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 259.892, "r_y0": 593.919, "r_x1": 262.659, "r_y1": 593.919, "r_x2": 262.659, "r_y2": 585.1220000000001, "r_x3": 259.892, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.295, "r_y0": 593.919, "r_x1": 480.588, "r_y1": 593.919, "r_x2": 480.588, "r_y2": 585.1220000000001, "r_x3": 266.295, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": "merging with the upper neighbor cell to create a", "orig": "merging with the upper neighbor cell to create a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 605.874, "r_x1": 171.676, "r_y1": 605.874, "r_x2": 171.676, "r_y2": 597.077, "r_x3": 151.701, "r_y3": 597.077, "coord_origin": "TOPLEFT"}, "text": "span", "orig": "span", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 618.467, "r_x1": 193.483, "r_y1": 618.467, "r_x2": 193.483, "r_y2": 609.67, "r_x3": 151.701, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "\"X\" cell -", "orig": "\"X\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.799, "r_y0": 618.467, "r_x1": 236.12, "r_y1": 618.467, "r_x2": 236.12, "r_y2": 609.67, "r_x3": 196.799, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "cross cell", "orig": "cross cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 236.125, "r_y0": 618.467, "r_x1": 238.892, "r_y1": 618.467, "r_x2": 238.892, "r_y2": 609.67, "r_x3": 236.125, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 242.209, "r_y0": 618.467, "r_x1": 454.555, "r_y1": 618.467, "r_x2": 454.555, "r_y2": 609.67, "r_x3": 242.209, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "to merge with both left and upper neighbor cells", "orig": "to merge with both left and upper neighbor cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 631.061, "r_x1": 181.994, "r_y1": 631.061, "r_x2": 181.994, "r_y2": 622.264, "r_x3": 151.701, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "\"NL\" -", "orig": "\"NL\" -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.317, "r_y0": 631.061, "r_x1": 221.462, "r_y1": 631.061, "r_x2": 221.462, "r_y2": 622.264, "r_x3": 185.317, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "new-line", "orig": "new-line", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.461, "r_y0": 631.061, "r_x1": 224.228, "r_y1": 631.061, "r_x2": 224.228, "r_y2": 622.264, "r_x3": 221.461, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.545, "r_y0": 631.061, "r_x1": 328.617, "r_y1": 631.061, "r_x2": 328.617, "r_y2": 622.264, "r_x3": 227.545, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "switch to the next row.", "orig": "switch to the next row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 228.223, "r_y1": 664.855, "r_x2": 228.223, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "conversion to HTML.", "orig": "conversion to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 143.48299999999995, "r": 480.59, "b": 152.27999999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 141.489, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.591, "b": 357.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 149.402, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 497.795, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 146.72, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 140.993, "t": 560.563, "r": 146.72, "b": 569.37, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 569.37, "r_x1": 146.72, "r_y1": 569.37, "r_x2": 146.72, "r_y2": 560.563, "r_x3": 140.993, "r_y3": 560.563, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 140.993, "t": 585.112, "r": 146.72, "b": 593.919, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 593.919, "r_x1": 146.72, "r_y1": 593.919, "r_x2": 146.72, "r_y2": 585.112, "r_x3": 140.993, "r_y3": 585.112, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 140.993, "t": 609.66, "r": 146.72, "b": 618.467, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 146.72, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.593, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 143.48299999999995, "r": 480.59, "b": 152.27999999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when train-"}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 141.489, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.591, "b": 357.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,"}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 149.402, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 497.795, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines"}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 146.72, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.993, "t": 560.563, "r": 146.72, "b": 569.37, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 569.37, "r_x1": 146.72, "r_y1": 569.37, "r_x2": 146.72, "r_y2": 560.563, "r_x3": 140.993, "r_y3": 560.563, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.993, "t": 585.112, "r": 146.72, "b": 593.919, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 593.919, "r_x1": 146.72, "r_y1": 593.919, "r_x2": 146.72, "r_y2": 585.112, "r_x3": 140.993, "r_y3": 585.112, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.993, "t": 609.66, "r": 146.72, "b": 618.467, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 146.72, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.593, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless"}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 149.709, "t": 143.48299999999995, "r": 480.59, "b": 152.27999999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when train-"}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 141.489, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.591, "b": 357.914, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,"}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 149.402, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 497.795, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines"}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 146.72, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, 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"cluster": {"id": 7, "label": "list_item", "bbox": {"l": 140.993, "t": 585.112, "r": 146.72, "b": 593.919, "coord_origin": "TOPLEFT"}, "confidence": 0.9438967704772949, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 593.919, "r_x1": 146.72, "r_y1": 593.919, "r_x2": 146.72, "r_y2": 585.112, "r_x3": 140.993, "r_y3": 585.112, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.993, "t": 609.66, "r": 146.72, "b": 618.467, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 146.72, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.593, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless"}], "headers": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.383, "r_y0": 355.978, "r_x1": 260.15, "r_y1": 355.978, "r_x2": 260.15, "r_y2": 347.181, "r_x3": 257.383, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.184, "r_y0": 355.978, "r_x1": 480.589, "r_y1": 355.978, "r_x2": 480.589, "r_y2": 347.181, "r_x3": 264.184, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"L\" cell must be either", "orig": "The left neighbour of an \"L\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 367.934, "r_x1": 283.594, "r_y1": 367.934, "r_x2": 283.594, 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\"U\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 403.805, "r_x1": 146.72, "r_y1": 403.805, "r_x2": 146.72, "r_y2": 395.008, "r_x3": 138.973, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 403.805, "r_x1": 223.304, "r_y1": 403.805, "r_x2": 223.304, "r_y2": 394.998, "r_x3": 151.701, "r_y3": 394.998, "coord_origin": "TOPLEFT"}, "text": "Cross cell rule", "orig": "Cross cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 223.307, "r_y0": 403.805, "r_x1": 226.074, "r_y1": 403.805, "r_x2": 226.074, "r_y2": 395.008, "r_x3": 223.307, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 415.764, "r_x1": 480.592, "r_y1": 415.764, "r_x2": 480.592, "r_y2": 406.967, "r_x3": 151.701, "r_y3": 406.967, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 427.719, "r_x1": 480.592, "r_y1": 427.719, "r_x2": 480.592, "r_y2": 418.922, "r_x3": 151.701, "r_y3": 418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, "r_y2": 442.836, "r_x3": 138.973, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 451.633, "r_x1": 221.323, "r_y1": 451.633, "r_x2": 221.323, "r_y2": 442.826, "r_x3": 151.701, "r_y3": 442.826, "coord_origin": "TOPLEFT"}, "text": "First row rule", "orig": "First row rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.327, "r_y0": 451.633, "r_x1": 224.094, "r_y1": 451.633, "r_x2": 224.094, "r_y2": 442.836, "r_x3": 221.327, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.411, "r_y0": 451.633, "r_x1": 474.59, "r_y1": 451.633, "r_x2": 474.59, "r_y2": 442.836, "r_x3": 227.411, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "orig": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 463.591, "r_x1": 146.72, "r_y1": 463.591, "r_x2": 146.72, "r_y2": 454.794, "r_x3": 138.973, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 463.591, "r_x1": 240.72, "r_y1": 463.591, "r_x2": 240.72, "r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79899999999998, "r": 162.644, "b": 133.72500000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", 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First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.592, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks,"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 125.79899999999998, "r": 162.644, "b": 133.72500000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 3."}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65028381347656, "t": 163.79708862304688, "r": 449.5505676269531, "b": 280.3409423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7868534922599792, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 334.511, "r_y0": 279.0, "r_x1": 426.599, "r_y1": 279.0, "r_x2": 426.599, "r_y2": 272.88800000000003, "r_x3": 334.511, "r_y3": 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First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 644.1030000000001, "r": 480.592, "b": 652.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks,"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, "r_y0": 403.867, "r_x1": 480.595, "r_y1": 403.867, "r_x2": 480.595, "r_y2": 395.07, "r_x3": 195.902, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. Secondly we pick the best hyper-parameters", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.822, "r_x1": 172.59, "r_y1": 415.822, "r_x2": 172.59, "r_y2": 407.025, "r_x3": 134.765, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "found in", "orig": "found in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 177.492, "r_y0": 415.822, "r_x1": 191.325, "r_y1": 415.822, "r_x2": 191.325, "r_y2": 407.025, "r_x3": 177.492, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.226, "r_y0": 415.822, "r_x1": 213.463, "r_y1": 415.822, "r_x2": 213.463, "r_y2": 407.025, "r_x3": 196.226, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "first", "orig": "first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 218.364, "r_y0": 415.822, "r_x1": 236.125, "r_y1": 415.822, "r_x2": 236.125, "r_y2": 407.025, "r_x3": 218.364, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "step", "orig": "step", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.027, "r_y0": 415.822, "r_x1": 297.661, "r_y1": 415.822, "r_x2": 297.661, "r_y2": 407.025, "r_x3": 241.027, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "and evaluate", "orig": "and evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 302.563, "r_y0": 415.822, "r_x1": 480.593, "r_y1": 415.822, "r_x2": 480.593, "r_y2": 407.025, "r_x3": 302.563, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "how OTSL impacts the performance of", "orig": "how OTSL impacts the performance of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.778, "r_x1": 377.321, "r_y1": 427.778, "r_x2": 377.321, "r_y2": 418.981, "r_x3": 134.765, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "TableFormer after training on other publicly available", "orig": "TableFormer after training on other publicly available", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.874, "r_y0": 427.778, "r_x1": 421.951, "r_y1": 427.778, "r_x2": 421.951, "r_y2": 418.981, "r_x3": 381.874, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "data sets", "orig": "data sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.504, "r_y0": 427.778, "r_x1": 480.595, "r_y1": 427.778, "r_x2": 480.595, "r_y2": 418.981, "r_x3": 426.504, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "(FinTabNet,", "orig": "(FinTabNet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.733, "r_x1": 480.592, "r_y1": 439.733, "r_x2": 480.592, "r_y2": 430.936, "r_x3": 134.765, "r_y3": 430.936, "coord_origin": "TOPLEFT"}, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 451.688, "r_x1": 479.303, "r_y1": 451.688, "r_x2": 479.303, "r_y2": 442.891, "r_x3": 134.765, "r_y3": 442.891, "coord_origin": "TOPLEFT"}, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "orig": "verted into OTSL format for this purpose, and will be made publicly available.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 484.648, "coord_origin": "TOPLEFT"}, "text": "Fig. 4.", "orig": "Fig. 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.196, "r_y0": 493.722, "r_x1": 480.591, "r_y1": 493.722, "r_x2": 480.591, "r_y2": 482.433, "r_x3": 165.196, "r_y3": 482.433, "coord_origin": "TOPLEFT"}, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "orig": "Architecture sketch of the TableFormer model, which is a representative for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.681, "r_x1": 206.702, "r_y1": 504.681, "r_x2": 206.702, "r_y2": 493.392, "r_x3": 134.765, "r_y3": 493.392, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approach.", "orig": "Im2Seq approach.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.3, "r_y0": 543.091, "r_x1": 149.407, "r_y1": 543.091, "r_x2": 149.407, "r_y2": 540.762, "r_x3": 147.3, "r_y3": 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unfinished"}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 141.489, "b": 331.732, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.25, "r": 480.594, "b": 356.047, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we"}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 484.648, "r": 162.644, "b": 492.574, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 484.648, "coord_origin": "TOPLEFT"}, "text": "Fig. 4.", "orig": "Fig. 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 4."}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 149.709, "t": 620.193, "r": 480.588, "b": 628.99, "coord_origin": "TOPLEFT"}, "confidence": 0.951270341873169, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 480.588, "r_y1": 628.99, "r_x2": 480.588, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for"}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.70968627929688, "t": 508.0638427734375, "r": 472.73382568359375, "b": 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91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8631826043128967, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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"r_x1": 264.403, "r_y1": 516.9590000000001, "r_x2": 264.403, "r_y2": 508.152, "r_x3": 160.859, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.348, "r_x1": 480.591, "r_y1": 533.348, "r_x2": 480.591, "r_y2": 524.5509999999999, "r_x3": 134.765, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 164.921, "r_y1": 545.304, "r_x2": 164.921, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378753662109, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8857625722885132, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 149.402, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.592, "b": 194.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 275.072, "r": 160.118, "b": 282.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 282.998, "r_x1": 160.118, "r_y1": 282.998, "r_x2": 160.118, "r_y2": 275.072, "r_x3": 134.765, "r_y3": 275.072, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.152, "r": 149.402, "b": 516.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 516.9590000000001, "r_x1": 149.402, "r_y1": 516.9590000000001, "r_x2": 149.402, "r_y2": 508.152, "r_x3": 134.765, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.591, "b": 533.348, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.348, "r_x1": 480.591, "r_y1": 533.348, "r_x2": 480.591, "r_y2": 524.5509999999999, "r_x3": 134.765, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 149.709, "t": 620.193, "r": 205.597, "b": 628.99, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"0": 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{"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 127.731, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments"}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 149.402, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.592, "b": 194.38300000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a"}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 275.072, "r": 160.118, "b": 282.998, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 282.998, "r_x1": 160.118, "r_y1": 282.998, "r_x2": 160.118, "r_y2": 275.072, "r_x3": 134.765, "r_y3": 275.072, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table"}, {"label": "section_header", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 508.152, "r": 149.402, "b": 516.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 516.9590000000001, "r_x1": 149.402, "r_y1": 516.9590000000001, "r_x2": 149.402, "r_y2": 508.152, "r_x3": 134.765, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.591, "b": 533.348, "coord_origin": "TOPLEFT"}, "confidence": 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115.83600000000001, "r_x3": 134.765, "r_y3": 115.83600000000001, "coord_origin": "TOPLEFT"}, "text": "Table 2.", "orig": "Table 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2."}, {"label": "section_header", "id": 3, "page_no": 9, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 288.915, "r": 149.402, "b": 297.722, "coord_origin": "TOPLEFT"}, "confidence": 0.9546447992324829, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 297.722, "r_x1": 149.402, "r_y1": 297.722, "r_x2": 149.402, "r_y2": 288.915, "r_x3": 134.765, "r_y3": 288.915, "coord_origin": "TOPLEFT"}, "text": "5.3", "orig": "5.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.3"}, {"label": "text", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 309.861, "r": 189.59, "b": 318.658, "coord_origin": "TOPLEFT"}, "confidence": 0.9834085702896118, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 318.658, "r_x1": 189.59, "r_y1": 318.658, "r_x2": 189.59, "r_y2": 309.861, "r_x3": 134.765, "r_y3": 309.861, "coord_origin": "TOPLEFT"}, "text": "To illustrate", "orig": "To illustrate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To illustrate"}, {"label": "caption", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 134.765, "t": 397.59, "r": 162.644, "b": 405.516, "coord_origin": "TOPLEFT"}, "confidence": 0.9482728838920593, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.516, "r_x1": 162.644, "r_y1": 405.516, "r_x2": 162.644, "r_y2": 397.59, "r_x3": 134.765, "r_y3": 397.59, "coord_origin": "TOPLEFT"}, "text": "Fig. 5.", "orig": "Fig. 5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 5."}, {"label": "table", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "table", "bbox": {"l": 143.6376495361328, "t": 156.3477020263672, "r": 470.8485412597656, "b": 263.2624816894531, "coord_origin": "TOPLEFT"}, "confidence": 0.9884640574455261, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "lcel", "lcel", "ched", "ched", "nl", "ucel", "ucel", "ched", "ched", "ched", "ucel", "ucel", "nl", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}, {"label": "picture", "id": 2, "page_no": 9, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 162.67430114746094, "t": 444.62255859375, "r": 451.70062255859375, "b": 663.2135620117188, "coord_origin": "TOPLEFT"}, "confidence": 0.9742383360862732, 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.376, "r_y0": 102.78200000000004, "r_x1": 480.59, "r_y1": 102.78200000000004, "r_x2": 480.59, "r_y2": 91.49300000000005, "r_x3": 471.376, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 6.", "orig": "Fig. 6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.215, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 165.215, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "Visualization of predicted structure and detected bounding boxes on a complex", "orig": "Visualization of predicted structure and detected bounding boxes on a complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 480.588, "r_y1": 145.832, "r_x2": 480.588, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table with many rows. 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.821, "r_y0": 102.78200000000004, "r_x1": 231.72, "r_y1": 102.78200000000004, "r_x2": 231.72, "r_y2": 91.49300000000005, "r_x3": 167.821, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.11699999999996, "r_x1": 141.489, "r_y1": 128.11699999999996, "r_x2": 141.489, "r_y2": 117.54899999999998, "r_x3": 134.765, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 128.11699999999996, "r_x1": 219.255, "r_y1": 128.11699999999996, "r_x2": 219.255, "r_y2": 117.54899999999998, "r_x3": 154.938, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "Conclusion", "orig": "Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 155.66099999999994, "r_x1": 480.595, "r_y1": 155.66099999999994, "r_x2": 480.595, "r_y2": 146.86400000000003, "r_x3": 134.765, "r_y3": 146.86400000000003, "coord_origin": "TOPLEFT"}, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.61599999999999, "r_x1": 152.499, "r_y1": 167.61599999999999, "r_x2": 152.499, "r_y2": 158.81899999999996, "r_x3": 134.765, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "ture", "orig": "ture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.963, "r_y0": 167.61599999999999, "r_x1": 205.131, "r_y1": 167.61599999999999, "r_x2": 205.131, "r_y2": 158.81899999999996, "r_x3": 156.963, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "recognition", "orig": "recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 209.594, "r_y0": 167.61599999999999, "r_x1": 480.595, "r_y1": 167.61599999999999, "r_x2": 480.595, "r_y2": 158.81899999999996, "r_x3": 209.594, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "with Im2Seq models is ill-suited and has serious limitations.", "orig": "with Im2Seq models is ill-suited and has serious limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly used general purpose languages,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.48199999999997, "r_x1": 239.539, "r_y1": 203.48199999999997, "r_x2": 239.539, "r_y2": 194.68499999999995, "r_x3": 134.765, "r_y3": 194.68499999999995, "coord_origin": "TOPLEFT"}, "text": "has several key benefits.", "orig": "has several key benefits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 216.24099999999999, "r_x1": 480.593, "r_y1": 216.24099999999999, "r_x2": 480.593, "r_y2": 207.44399999999996, "r_x3": 149.709, "r_y3": 207.44399999999996, "coord_origin": "TOPLEFT"}, "text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"throughput and responsiveness.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.518, "r_y0": 553.809, "r_x1": 176.341, "r_y1": 553.809, "r_x2": 176.341, "r_y2": 542.52, "r_x3": 151.518, "r_y3": 542.52, "coord_origin": "TOPLEFT"}, "text": "CoRR", "orig": "CoRR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.464, "r_y0": 552.6610000000001, "r_x1": 250.68, "r_y1": 552.6610000000001, "r_x2": 250.68, "r_y2": 544.735, "r_x3": 179.464, "r_y3": 544.735, "coord_origin": "TOPLEFT"}, "text": "abs/2206.00785", "orig": "abs/2206.00785", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.804, "r_y0": 553.809, "r_x1": 281.957, "r_y1": 553.809, "r_x2": 281.957, "r_y2": 542.52, "r_x3": 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Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. The benefits of OTSL are that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 399.923, "r_x1": 169.253, "r_y1": 399.923, "r_x2": 169.253, "r_y2": 388.634, "r_x3": 163.111, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.795, "r_y0": 399.923, "r_x1": 452.244, "r_y1": 399.923, "r_x2": 452.244, "r_y2": 388.634, "r_x3": 172.795, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "orig": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 410.882, "r_x1": 452.249, "r_y1": 410.882, "r_x2": 452.249, "r_y2": 399.593, "r_x3": 163.111, "r_y3": 399.593, "coord_origin": "TOPLEFT"}, "text": "the sequence length to half of HTML on average. Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 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452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, 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"coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 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They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. 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Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. As of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.899, "r": 480.597, "b": 146.514, "coord_origin": "TOPLEFT"}, "confidence": 0.8918349742889404, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.58100000000002, "r_x1": 480.597, "r_y1": 128.58100000000002, "r_x2": 480.597, "r_y2": 115.899, "r_x3": 134.765, "r_y3": 115.899, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for 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Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. The benefits of OTSL are that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 399.923, "r_x1": 169.253, "r_y1": 399.923, "r_x2": 169.253, "r_y2": 388.634, "r_x3": 163.111, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.795, "r_y0": 399.923, "r_x1": 452.244, "r_y1": 399.923, "r_x2": 452.244, "r_y2": 388.634, "r_x3": 172.795, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "orig": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 410.882, "r_x1": 452.249, "r_y1": 410.882, "r_x2": 452.249, "r_y2": 399.593, "r_x3": 163.111, "r_y3": 399.593, "coord_origin": "TOPLEFT"}, "text": "the sequence length to half of HTML on average. Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 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452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, 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"coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 163.111, "t": 476.479, "r": 452.241, "b": 498.727, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 522.12, "r": 228.934, "b": 532.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 134.765, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 532.688, "r_x1": 228.934, "r_y1": 532.688, "r_x2": 228.934, "r_y2": 522.12, "r_x3": 154.938, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "Introduction", "orig": "Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 548.287, "r": 480.596, "b": 628.815, 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They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. 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Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. As of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 18.34, "t": 165.0, "r": 36.34, "b": 555.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8899643421173096, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "section_header", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 115.899, "r": 480.597, "b": 146.514, "coord_origin": 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documents is a crucial task in any", "orig": "Extracting tables from documents is a crucial task in any", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 290.334, "r_x1": 452.242, "r_y1": 290.334, "r_x2": 452.242, "r_y2": 279.04499999999996, "r_x3": 163.111, "r_y3": 279.04499999999996, "coord_origin": "TOPLEFT"}, "text": "document conversion pipeline. Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. The benefits of OTSL are that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 399.923, "r_x1": 169.253, "r_y1": 399.923, "r_x2": 169.253, "r_y2": 388.634, "r_x3": 163.111, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 172.795, "r_y0": 399.923, "r_x1": 452.244, "r_y1": 399.923, "r_x2": 452.244, "r_y2": 388.634, "r_x3": 172.795, "r_y3": 388.634, "coord_origin": "TOPLEFT"}, "text": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "orig": "reduces the number of tokens to 5 (HTML needs 28+) and shortens", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 410.882, "r_x1": 452.249, "r_y1": 410.882, "r_x2": 452.249, "r_y2": 399.593, "r_x3": 163.111, "r_y3": 399.593, "coord_origin": "TOPLEFT"}, "text": "the sequence length to half of HTML on average. Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 297.115, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "inference", "orig": "inference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.158, "r_y0": 421.841, "r_x1": 365.708, "r_y1": 421.841, "r_x2": 365.708, "r_y2": 410.552, "r_x3": 337.158, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "time is", "orig": "time is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.146, "r_y0": 421.841, "r_x1": 396.249, "r_y1": 421.841, "r_x2": 396.249, "r_y2": 410.552, "r_x3": 370.146, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "halved", "orig": "halved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.696, "r_y0": 421.841, "r_x1": 452.241, "r_y1": 421.841, "r_x2": 452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.282, "r_y0": 443.758, "r_x1": 234.527, "r_y1": 443.758, "r_x2": 234.527, "r_y2": 432.469, "r_x3": 204.282, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "correct.", "orig": "correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.588, "r_y0": 443.758, "r_x1": 452.244, "r_y1": 443.758, "r_x2": 452.244, "r_y2": 432.469, "r_x3": 238.588, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "This in turn eliminates most post-processing needs.", "orig": "This in turn eliminates most post-processing needs.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 454.717, "r_x1": 452.246, "r_y1": 454.717, "r_x2": 452.246, "r_y2": 443.428, "r_x3": 163.111, "r_y3": 443.428, "coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.111, "t": 476.479, "r": 452.241, "b": 498.727, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 522.12, "r": 228.934, "b": 532.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 134.765, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 532.688, "r_x1": 228.934, "r_y1": 532.688, "r_x2": 228.934, "r_y2": 522.12, "r_x3": 154.938, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "Introduction", "orig": "Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 548.287, "r": 480.596, "b": 628.815, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.0840000000001, "r_x1": 480.594, "r_y1": 557.0840000000001, "r_x2": 480.594, "r_y2": 548.287, "r_x3": 134.765, "r_y3": 548.287, "coord_origin": "TOPLEFT"}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.04, "r_x1": 480.592, "r_y1": 569.04, "r_x2": 480.592, "r_y2": 560.2429999999999, "r_x3": 134.765, "r_y3": 560.2429999999999, "coord_origin": "TOPLEFT"}, "text": "manuals, specification sheets or marketing material. They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. Therefore, accurate table extraction is achieved these days with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.815, "r_x1": 279.327, "r_y1": 628.815, "r_x2": 279.327, "r_y2": 620.018, "r_x3": 134.765, "r_y3": 620.018, "coord_origin": "TOPLEFT"}, "text": "machine-learning based methods.", "orig": "machine-learning based methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.708, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 149.708, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "In modern document understanding systems [1,15], table extraction is typi-", "orig": "In modern document understanding systems [1,15], table extraction is typi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.596, "r_y1": 652.9, "r_x2": 480.596, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. As of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. 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Recently, transformer-based models have", "orig": "document conversion pipeline. Recently, transformer-based models have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 301.293, "r_x1": 452.248, "r_y1": 301.293, "r_x2": 452.248, "r_y2": 290.004, "r_x3": 163.111, "r_y3": 290.004, "coord_origin": "TOPLEFT"}, "text": "demonstrated that table-structure can be recognized with impressive ac-", "orig": "demonstrated that table-structure can be recognized with impressive ac-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 312.252, "r_x1": 452.241, "r_y1": 312.252, "r_x2": 452.241, "r_y2": 300.963, "r_x3": 163.111, "r_y3": 300.963, "coord_origin": "TOPLEFT"}, "text": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "orig": "curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 323.21, "r_x1": 452.246, "r_y1": 323.21, "r_x2": 452.246, "r_y2": 311.921, "r_x3": 163.111, "r_y3": 311.921, "coord_origin": "TOPLEFT"}, "text": "only the image of a table, such models predict a sequence of tokens (e.g.", "orig": "only the image of a table, such models predict a sequence of tokens (e.g.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 334.169, "r_x1": 452.246, "r_y1": 334.169, "r_x2": 452.246, "r_y2": 322.88, "r_x3": 163.111, "r_y3": 322.88, "coord_origin": "TOPLEFT"}, "text": "in HTML, LaTeX) which represent the structure of the table. Since the", "orig": "in HTML, LaTeX) which represent the structure of the table. Since the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 345.128, "r_x1": 452.247, "r_y1": 345.128, "r_x2": 452.247, "r_y2": 333.839, "r_x3": 163.111, "r_y3": 333.839, "coord_origin": "TOPLEFT"}, "text": "token representation of the table structure has a significant impact on", "orig": "token representation of the table structure has a significant impact on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 356.087, "r_x1": 452.246, "r_y1": 356.087, "r_x2": 452.246, "r_y2": 344.798, "r_x3": 163.111, "r_y3": 344.798, "coord_origin": "TOPLEFT"}, "text": "the accuracy and run-time performance of any Im2Seq model, we inves-", "orig": "the accuracy and run-time performance of any Im2Seq model, we inves-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 367.046, "r_x1": 452.248, "r_y1": 367.046, "r_x2": 452.248, "r_y2": 355.757, "r_x3": 163.111, "r_y3": 355.757, "coord_origin": "TOPLEFT"}, "text": "tigate in this paper how table-structure representation can be optimised.", "orig": "tigate in this paper how table-structure representation can be optimised.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 378.005, "r_x1": 452.246, "r_y1": 378.005, "r_x2": 452.246, "r_y2": 366.716, "r_x3": 163.111, "r_y3": 366.716, "coord_origin": "TOPLEFT"}, "text": "We propose a new, optimised table-structure language (OTSL) with a", "orig": "We propose a new, optimised table-structure language (OTSL) with a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 388.964, "r_x1": 452.242, "r_y1": 388.964, "r_x2": 452.242, "r_y2": 377.675, "r_x3": 163.111, "r_y3": 377.675, "coord_origin": "TOPLEFT"}, "text": "minimized vocabulary and specific rules. The benefits of OTSL are that", "orig": "minimized vocabulary and specific rules. 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Consequently, model", "orig": "the sequence length to half of HTML on average. Consequently, model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 421.841, "r_x1": 198.194, "r_y1": 421.841, "r_x2": 198.194, "r_y2": 410.552, "r_x3": 163.111, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "accuracy", "orig": "accuracy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.632, "r_y0": 421.841, "r_x1": 292.676, "r_y1": 421.841, "r_x2": 292.676, "r_y2": 410.552, "r_x3": 202.632, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "improves significantly,", "orig": "improves significantly,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.115, "r_y0": 421.841, "r_x1": 332.719, "r_y1": 421.841, "r_x2": 332.719, "r_y2": 410.552, "r_x3": 297.115, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "inference", "orig": "inference", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.158, "r_y0": 421.841, "r_x1": 365.708, "r_y1": 421.841, "r_x2": 365.708, "r_y2": 410.552, "r_x3": 337.158, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "time is", "orig": "time is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 370.146, "r_y0": 421.841, "r_x1": 396.249, "r_y1": 421.841, "r_x2": 396.249, "r_y2": 410.552, "r_x3": 370.146, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "halved", "orig": "halved", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 400.696, "r_y0": 421.841, "r_x1": 452.241, "r_y1": 421.841, "r_x2": 452.241, "r_y2": 410.552, "r_x3": 400.696, "r_y3": 410.552, "coord_origin": "TOPLEFT"}, "text": "compared to", "orig": "compared to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 432.799, "r_x1": 452.242, "r_y1": 432.799, "r_x2": 452.242, "r_y2": 421.51, "r_x3": 163.111, "r_y3": 421.51, "coord_origin": "TOPLEFT"}, "text": "HTML-based models, and the predicted table structures are always syn-", "orig": "HTML-based models, and the predicted table structures are always syn-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 443.758, "r_x1": 200.22, "r_y1": 443.758, "r_x2": 200.22, "r_y2": 432.469, "r_x3": 163.111, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "tactically", "orig": "tactically", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.282, "r_y0": 443.758, "r_x1": 234.527, "r_y1": 443.758, "r_x2": 234.527, "r_y2": 432.469, "r_x3": 204.282, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "correct.", "orig": "correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 238.588, "r_y0": 443.758, "r_x1": 452.244, "r_y1": 443.758, "r_x2": 452.244, "r_y2": 432.469, "r_x3": 238.588, "r_y3": 432.469, "coord_origin": "TOPLEFT"}, "text": "This in turn eliminates most post-processing needs.", "orig": "This in turn eliminates most post-processing needs.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 454.717, "r_x1": 452.246, "r_y1": 454.717, "r_x2": 452.246, "r_y2": 443.428, "r_x3": 163.111, "r_y3": 443.428, "coord_origin": "TOPLEFT"}, "text": "Popular table structure data-sets will be published in OTSL format to", "orig": "Popular table structure data-sets will be published in OTSL format to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 465.676, "r_x1": 225.561, "r_y1": 465.676, "r_x2": 225.561, "r_y2": 454.387, "r_x3": 163.111, "r_y3": 454.387, "coord_origin": "TOPLEFT"}, "text": "the community.", "orig": "the community.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 163.111, "t": 476.479, "r": 452.241, "b": 498.727, "coord_origin": "TOPLEFT"}, "confidence": 0.9304055571556091, "cells": [{"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 486.62, "r_x1": 211.942, "r_y1": 486.62, "r_x2": 211.942, "r_y2": 478.694, "r_x3": 163.111, "r_y3": 478.694, "coord_origin": "TOPLEFT"}, "text": "Keywords:", "orig": "Keywords:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.552, "r_y0": 487.768, "r_x1": 452.241, "r_y1": 487.768, "r_x2": 452.241, "r_y2": 476.479, "r_x3": 216.552, "r_y3": 476.479, "coord_origin": "TOPLEFT"}, "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "orig": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.111, "r_y0": 498.727, "r_x1": 257.642, "r_y1": 498.727, "r_x2": 257.642, "r_y2": 487.438, "r_x3": 163.111, "r_y3": 487.438, "coord_origin": "TOPLEFT"}, "text": "formers \u00b7 Optimization.", "orig": "formers \u00b7 Optimization.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."}, {"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 522.12, "r": 228.934, "b": 532.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9392016530036926, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.688, "r_x1": 141.489, "r_y1": 532.688, "r_x2": 141.489, "r_y2": 522.12, "r_x3": 134.765, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 532.688, "r_x1": 228.934, "r_y1": 532.688, "r_x2": 228.934, "r_y2": 522.12, "r_x3": 154.938, "r_y3": 522.12, "coord_origin": "TOPLEFT"}, "text": "Introduction", "orig": "Introduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1 Introduction"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 548.287, "r": 480.596, "b": 628.815, "coord_origin": "TOPLEFT"}, "confidence": 0.9835679531097412, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.0840000000001, "r_x1": 480.594, "r_y1": 557.0840000000001, "r_x2": 480.594, "r_y2": 548.287, "r_x3": 134.765, "r_y3": 548.287, "coord_origin": "TOPLEFT"}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "orig": "Tables are ubiquitous in documents such as scientific papers, patents, reports,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.04, "r_x1": 480.592, "r_y1": 569.04, "r_x2": 480.592, "r_y2": 560.2429999999999, "r_x3": 134.765, "r_y3": 560.2429999999999, "coord_origin": "TOPLEFT"}, "text": "manuals, specification sheets or marketing material. They often encode highly", "orig": "manuals, specification sheets or marketing material. They often encode highly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.995, "r_x1": 170.451, "r_y1": 580.995, "r_x2": 170.451, "r_y2": 572.198, "r_x3": 134.765, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "valuable", "orig": "valuable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.173, "r_y0": 580.995, "r_x1": 225.832, "r_y1": 580.995, "r_x2": 225.832, "r_y2": 572.198, "r_x3": 175.173, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "information", "orig": "information", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.544, "r_y0": 580.995, "r_x1": 289.826, "r_y1": 580.995, "r_x2": 289.826, "r_y2": 572.198, "r_x3": 230.544, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "and therefore", "orig": "and therefore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 294.538, "r_y0": 580.995, "r_x1": 388.395, "r_y1": 580.995, "r_x2": 388.395, "r_y2": 572.198, "r_x3": 294.538, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "need to be extracted", "orig": "need to be extracted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.107, "r_y0": 580.995, "r_x1": 480.593, "r_y1": 580.995, "r_x2": 480.593, "r_y2": 572.198, "r_x3": 393.107, "r_y3": 572.198, "coord_origin": "TOPLEFT"}, "text": "with high accuracy.", "orig": "with high accuracy.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 592.95, "r_x1": 480.596, "r_y1": 592.95, "r_x2": 480.596, "r_y2": 584.153, "r_x3": 134.765, "r_y3": 584.153, "coord_origin": "TOPLEFT"}, "text": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "orig": "Unfortunately, tables appear in documents in various sizes, styling and struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 604.905, "r_x1": 155.266, "r_y1": 604.905, "r_x2": 155.266, "r_y2": 596.108, "r_x3": 134.765, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.48, "r_y0": 604.905, "r_x1": 480.587, "r_y1": 604.905, "r_x2": 480.587, "r_y2": 596.108, "r_x3": 159.48, "r_y3": 596.108, "coord_origin": "TOPLEFT"}, "text": "making it difficult to recover their correct structure with simple analyt-", "orig": "making it difficult to recover their correct structure with simple analyt-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.86, "r_x1": 149.705, "r_y1": 616.86, "r_x2": 149.705, "r_y2": 608.063, "r_x3": 134.765, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "ical", "orig": "ical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 153.71, "r_y0": 616.86, "r_x1": 480.591, "r_y1": 616.86, "r_x2": 480.591, "r_y2": 608.063, "r_x3": 153.71, "r_y3": 608.063, "coord_origin": "TOPLEFT"}, "text": "methods. Therefore, accurate table extraction is achieved these days with", "orig": "methods. Therefore, accurate table extraction is achieved these days with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.815, "r_x1": 279.327, "r_y1": 628.815, "r_x2": 279.327, "r_y2": 620.018, "r_x3": 134.765, "r_y3": 620.018, "coord_origin": "TOPLEFT"}, "text": "machine-learning based methods.", "orig": "machine-learning based methods.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods."}, {"label": "text", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9696458578109741, "cells": [{"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.708, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 149.708, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "In modern document understanding systems [1,15], table extraction is typi-", "orig": "In modern document understanding systems [1,15], table extraction is typi-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.596, "r_y1": 652.9, "r_x2": 480.596, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "cally a two-step process. Firstly, every table on a page is located with a bounding", "orig": "cally a two-step process. Firstly, every table on a page is located with a bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "box, and secondly, their logical row and column structure is recognized. As of", "orig": "box, and secondly, their logical row and column structure is recognized. As of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of"}], "headers": [{"label": "page_header", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 18.34, "t": 165.0, "r": 36.34, "b": 555.0, "coord_origin": "TOPLEFT"}, "confidence": 0.8899643421173096, "cells": [{"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.34, "r_y0": 555.0, "r_x1": 36.34, "r_y1": 165.0, "r_x2": 18.34, "r_y2": 165.0, "r_x3": 18.34, "r_y3": 555.0, "coord_origin": "TOPLEFT"}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "orig": "arXiv:2305.03393v1 [cs.CV] 5 May 2023", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "arXiv:2305.03393v1 [cs.CV] 5 May 2023"}]}}, {"page_no": 1, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. 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On the other hand, the problem of table", "orig": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 124.11900000000003, "r": 480.592, "b": 201.16200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 134.26, "r_x1": 162.644, "r_y1": 134.26, "r_x2": 162.644, "r_y2": 126.33399999999995, "r_x3": 134.765, "r_y3": 126.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 1.", "orig": "Fig. 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.062, "r_y0": 135.40800000000002, "r_x1": 480.591, "r_y1": 135.40800000000002, "r_x2": 480.591, "r_y2": 124.11900000000003, "r_x3": 167.062, "r_y3": 124.11900000000003, "coord_origin": "TOPLEFT"}, "text": "Comparison between HTML and OTSL table structure representation: (A)", "orig": "Comparison between HTML and OTSL table structure representation: (A)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 146.36699999999996, "r_x1": 190.823, "r_y1": 146.36699999999996, "r_x2": 190.823, "r_y2": 135.07799999999997, "r_x3": 134.765, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "table-example", "orig": "table-example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.199, "r_y0": 146.36699999999996, "r_x1": 480.59, "r_y1": 146.36699999999996, "r_x2": 480.59, "r_y2": 135.07799999999997, "r_x3": 195.199, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "with complex row and column headers, including a 2D empty span,", "orig": "with complex row and column headers, including a 2D empty span,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 157.32600000000002, "r_x1": 480.591, "r_y1": 157.32600000000002, "r_x2": 480.591, "r_y2": 146.03700000000003, "r_x3": 134.765, "r_y3": 146.03700000000003, "coord_origin": "TOPLEFT"}, "text": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "orig": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 168.28499999999997, "r_x1": 480.592, "r_y1": 168.28499999999997, "r_x2": 480.592, "r_y2": 156.99599999999998, "r_x3": 134.765, "r_y3": 156.99599999999998, "coord_origin": "TOPLEFT"}, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "orig": "HTML representation, (D) OTSL representation. This example demonstrates many of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.24400000000003, "r_x1": 480.589, "r_y1": 179.24400000000003, "r_x2": 480.589, "r_y2": 167.95500000000004, "r_x3": 134.765, "r_y3": 167.95500000000004, "coord_origin": "TOPLEFT"}, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "orig": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 190.20299999999997, "r_x1": 480.59, "r_y1": 190.20299999999997, "r_x2": 480.59, "r_y2": 178.914, "r_x3": 134.765, "r_y3": 178.914, "coord_origin": "TOPLEFT"}, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure 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On the other hand, the problem of table", "orig": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 124.11900000000003, "r": 480.592, "b": 201.16200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 134.26, "r_x1": 162.644, "r_y1": 134.26, "r_x2": 162.644, "r_y2": 126.33399999999995, "r_x3": 134.765, "r_y3": 126.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 1.", "orig": "Fig. 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.062, "r_y0": 135.40800000000002, "r_x1": 480.591, "r_y1": 135.40800000000002, "r_x2": 480.591, "r_y2": 124.11900000000003, "r_x3": 167.062, "r_y3": 124.11900000000003, "coord_origin": "TOPLEFT"}, "text": "Comparison between HTML and OTSL table structure representation: (A)", "orig": "Comparison between HTML and OTSL table structure representation: (A)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 146.36699999999996, "r_x1": 190.823, "r_y1": 146.36699999999996, "r_x2": 190.823, "r_y2": 135.07799999999997, "r_x3": 134.765, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "table-example", "orig": "table-example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.199, "r_y0": 146.36699999999996, "r_x1": 480.59, "r_y1": 146.36699999999996, "r_x2": 480.59, "r_y2": 135.07799999999997, "r_x3": 195.199, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "with complex row and column headers, including a 2D empty span,", "orig": "with complex row and column headers, including a 2D empty span,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 157.32600000000002, "r_x1": 480.591, "r_y1": 157.32600000000002, "r_x2": 480.591, "r_y2": 146.03700000000003, "r_x3": 134.765, "r_y3": 146.03700000000003, "coord_origin": "TOPLEFT"}, "text": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "orig": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 168.28499999999997, "r_x1": 480.592, "r_y1": 168.28499999999997, "r_x2": 480.592, "r_y2": 156.99599999999998, "r_x3": 134.765, "r_y3": 156.99599999999998, "coord_origin": "TOPLEFT"}, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "orig": "HTML representation, (D) OTSL representation. This example demonstrates many of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.24400000000003, "r_x1": 480.589, "r_y1": 179.24400000000003, "r_x2": 480.589, "r_y2": 167.95500000000004, "r_x3": 134.765, "r_y3": 167.95500000000004, "coord_origin": "TOPLEFT"}, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "orig": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 190.20299999999997, "r_x1": 480.59, "r_y1": 190.20299999999997, "r_x2": 480.59, "r_y2": 178.914, "r_x3": 134.765, "r_y3": 178.914, "coord_origin": "TOPLEFT"}, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "orig": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 201.16200000000003, "r_x1": 460.871, "r_y1": 201.16200000000003, "r_x2": 460.871, "r_y2": 189.87300000000005, "r_x3": 134.765, "r_y3": 189.87300000000005, "coord_origin": "TOPLEFT"}, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "orig": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9688959121704102, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 396.411, "r_y0": 289.478, "r_x1": 446.134, "r_y1": 288.875, "r_x2": 446.134, "r_y2": 280.49, "r_x3": 396.411, "r_y3": 281.093, "coord_origin": "TOPLEFT"}, "text": "C L C L L", "orig": "C L C L L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.748, "r_y0": 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On the other hand, the problem of table", "orig": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "body": [{"label": "caption", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 134.765, "t": 124.11900000000003, "r": 480.592, "b": 201.16200000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8939083218574524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 134.26, "r_x1": 162.644, "r_y1": 134.26, "r_x2": 162.644, "r_y2": 126.33399999999995, "r_x3": 134.765, "r_y3": 126.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 1.", "orig": "Fig. 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.062, "r_y0": 135.40800000000002, "r_x1": 480.591, "r_y1": 135.40800000000002, "r_x2": 480.591, "r_y2": 124.11900000000003, "r_x3": 167.062, "r_y3": 124.11900000000003, "coord_origin": "TOPLEFT"}, "text": "Comparison between HTML and OTSL table structure representation: (A)", "orig": "Comparison between HTML and OTSL table structure representation: (A)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 146.36699999999996, "r_x1": 190.823, "r_y1": 146.36699999999996, "r_x2": 190.823, "r_y2": 135.07799999999997, "r_x3": 134.765, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "table-example", "orig": "table-example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.199, "r_y0": 146.36699999999996, "r_x1": 480.59, "r_y1": 146.36699999999996, "r_x2": 480.59, "r_y2": 135.07799999999997, "r_x3": 195.199, "r_y3": 135.07799999999997, "coord_origin": "TOPLEFT"}, "text": "with complex row and column headers, including a 2D empty span,", "orig": "with complex row and column headers, including a 2D empty span,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 157.32600000000002, "r_x1": 480.591, "r_y1": 157.32600000000002, "r_x2": 480.591, "r_y2": 146.03700000000003, "r_x3": 134.765, "r_y3": 146.03700000000003, "coord_origin": "TOPLEFT"}, "text": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "orig": "(B) minimal graphical representation of table structure using rectangular layout, (C)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 168.28499999999997, "r_x1": 480.592, "r_y1": 168.28499999999997, "r_x2": 480.592, "r_y2": 156.99599999999998, "r_x3": 134.765, "r_y3": 156.99599999999998, "coord_origin": "TOPLEFT"}, "text": "HTML representation, (D) OTSL representation. This example demonstrates many of", "orig": "HTML representation, (D) OTSL representation. This example demonstrates many of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.24400000000003, "r_x1": 480.589, "r_y1": 179.24400000000003, "r_x2": 480.589, "r_y2": 167.95500000000004, "r_x3": 134.765, "r_y3": 167.95500000000004, "coord_origin": "TOPLEFT"}, "text": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "orig": "the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 190.20299999999997, "r_x1": 480.59, "r_y1": 190.20299999999997, "r_x2": 480.59, "r_y2": 178.914, "r_x3": 134.765, "r_y3": 178.914, "coord_origin": "TOPLEFT"}, "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "orig": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 201.16200000000003, "r_x1": 460.871, "r_y1": 201.16200000000003, "r_x2": 460.871, "r_y2": 189.87300000000005, "r_x3": 134.765, "r_y3": 189.87300000000005, "coord_origin": "TOPLEFT"}, "text": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "orig": "token sequence length per row in HTML versus a fixed length of rows in OTSL).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL)."}, {"label": "picture", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 148.45364379882812, "t": 208.37423706054688, "r": 464.3608093261719, "b": 425.8462219238281, "coord_origin": "TOPLEFT"}, "confidence": 0.9688959121704102, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 396.411, "r_y0": 289.478, "r_x1": 446.134, "r_y1": 288.875, "r_x2": 446.134, "r_y2": 280.49, "r_x3": 396.411, "r_y3": 281.093, "coord_origin": "TOPLEFT"}, "text": "C L C L L", "orig": "C L C L L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 395.748, "r_y0": 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On the other hand, the problem of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.977, "r_x1": 480.587, "r_y1": 496.977, "r_x2": 480.587, "r_y2": 488.18, "r_x3": 134.765, "r_y3": 488.18, "coord_origin": "TOPLEFT"}, "text": "structure recognition (TSR) is a lot more challenging and remains a very active", "orig": "structure recognition (TSR) is a lot more challenging and remains a very active", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.932, "r_x1": 165.481, "r_y1": 508.932, "r_x2": 165.481, "r_y2": 500.135, "r_x3": 134.765, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "area of", "orig": "area of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.874, "r_y0": 508.932, "r_x1": 207.895, "r_y1": 508.932, "r_x2": 207.895, "r_y2": 500.135, "r_x3": 169.874, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "research,", "orig": "research,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 212.289, "r_y0": 508.932, "r_x1": 220.589, "r_y1": 508.932, "r_x2": 220.589, "r_y2": 500.135, "r_x3": 212.289, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 224.992, "r_y0": 508.932, "r_x1": 480.59, "r_y1": 508.932, "r_x2": 480.59, "r_y2": 500.135, "r_x3": 224.992, "r_y3": 500.135, "coord_origin": "TOPLEFT"}, "text": "which many novel machine learning algorithms are being", "orig": "which many novel machine learning algorithms are being", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.887, "r_x1": 313.246, "r_y1": 520.887, "r_x2": 313.246, "r_y2": 512.0899999999999, "r_x3": 134.765, "r_y3": 512.0899999999999, "coord_origin": "TOPLEFT"}, "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "orig": "explored [3,4,5,9,11,12,13,14,17,18,21,22].", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9845512509346008, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 533.348, "r_x1": 187.471, "r_y1": 533.348, "r_x2": 187.471, "r_y2": 524.5509999999999, "r_x3": 149.709, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "Recently", "orig": "Recently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 192.194, "r_y0": 533.348, "r_x1": 480.589, "r_y1": 533.348, "r_x2": 480.589, "r_y2": 524.5509999999999, "r_x3": 192.194, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "emerging SOTA methods for table structure recognition employ", "orig": "emerging SOTA methods for table structure recognition employ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.592, "r_y1": 545.304, "r_x2": 480.592, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "transformer-based models, in which an image of the table is provided to the net-", "orig": "transformer-based models, in which an image of the table is provided to the net-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "work in order to predict the structure of the table as a sequence of tokens. These", "orig": "work in order to predict the structure of the table as a sequence of tokens. These", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.588, "r_y1": 569.2139999999999, "r_x2": 480.588, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.59, "r_y1": 581.169, "r_x2": 480.59, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "a purely data-driven solution. The tokens of the sequence typically belong to a", "orig": "a purely data-driven solution. The tokens of the sequence typically belong to a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.595, "r_y1": 593.124, "r_x2": 480.595, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "markup language such as HTML, Latex or Markdown, which allow to describe", "orig": "markup language such as HTML, Latex or Markdown, which allow to describe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.587, "r_y1": 605.08, "r_x2": 480.587, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "table structure as rows, columns and spanning cells in various configurations.", "orig": "table structure as rows, columns and spanning cells in various configurations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.595, "r_y1": 617.035, "r_x2": 480.595, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "orig": "In Figure 1, we illustrate how HTML is used to represent the table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.594, "r_y1": 628.99, "r_x2": 480.594, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "of a particular example table. Public table-structure data sets such as PubTab-", "orig": "of a particular example table. Public table-structure data sets such as PubTab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.595, "r_y1": 640.9449999999999, "r_x2": 480.595, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.588, "r_y1": 652.9, "r_x2": 480.588, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 421.454, "r_y1": 664.855, "r_x2": 421.454, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "the use of HTML as ground-truth representation format for TSR.", "orig": "the use of HTML as ground-truth representation format for TSR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."}], "headers": [{"label": "page_header", "id": 5, "page_no": 1, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8580349683761597, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "page_header", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9076642990112305, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 2, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.861, "r_y0": 163.59699999999998, "r_x1": 194.555, "r_y1": 163.59699999999998, "r_x2": 194.555, "r_y2": 154.79999999999995, "r_x3": 173.861, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "as is", "orig": "as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.61, "r_y0": 163.59699999999998, "r_x1": 211.535, "r_y1": 163.59699999999998, "r_x2": 211.535, "r_y2": 154.79999999999995, "r_x3": 199.61, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 215.769, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. 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"coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.355, "r_y0": 533.3489999999999, "r_x1": 480.59, "r_y1": 533.3489999999999, "r_x2": 480.59, "r_y2": 524.552, "r_x3": 442.355, "r_y3": 524.552, "coord_origin": "TOPLEFT"}, "text": "columns,", "orig": "columns,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.59, "r_y1": 545.304, "r_x2": 480.59, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "orig": "learning based approaches fall into three categories: object detection (OD) meth-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 152.252, "r_y1": 569.2139999999999, "r_x2": 152.252, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "ods,", "orig": "ods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.433, "r_y0": 569.2139999999999, "r_x1": 257.459, "r_y1": 569.2139999999999, "r_x2": 257.459, "r_y2": 560.417, "r_x3": 157.433, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "Graph-Neural-Network", "orig": "Graph-Neural-Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.65, "r_y0": 569.2139999999999, "r_x1": 480.591, "r_y1": 569.2139999999999, "r_x2": 480.591, "r_y2": 560.417, "r_x3": 262.65, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "(GNN) methods and Image-to-Markup-Sequence", "orig": "(GNN) methods and Image-to-Markup-Sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 484.121, "r_y1": 581.169, "r_x2": 484.121, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. The graph nodes represent the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "orig": "content of each table cell, an embedding vector from the table image, or geomet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.589, "r_y1": 652.9, "r_x2": 480.589, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "orig": "ric coordinates of the table cell. The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 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134.765, "t": 118.93399999999997, "r": 480.592, "b": 211.41700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": 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154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 214.836, "r": 480.596, "b": 331.23, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 334.648, "r": 480.596, "b": 439.087, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 236.769, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 484.121, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. 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"coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.355, "r_y0": 533.3489999999999, "r_x1": 480.59, "r_y1": 533.3489999999999, "r_x2": 480.59, "r_y2": 524.552, "r_x3": 442.355, "r_y3": 524.552, "coord_origin": "TOPLEFT"}, "text": "columns,", "orig": "columns,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.59, "r_y1": 545.304, "r_x2": 480.59, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "orig": "learning based approaches fall into three categories: object detection (OD) meth-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 152.252, "r_y1": 569.2139999999999, "r_x2": 152.252, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "ods,", "orig": "ods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.433, "r_y0": 569.2139999999999, "r_x1": 257.459, "r_y1": 569.2139999999999, "r_x2": 257.459, "r_y2": 560.417, "r_x3": 157.433, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "Graph-Neural-Network", "orig": "Graph-Neural-Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.65, "r_y0": 569.2139999999999, "r_x1": 480.591, "r_y1": 569.2139999999999, "r_x2": 480.591, "r_y2": 560.417, "r_x3": 262.65, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "(GNN) methods and Image-to-Markup-Sequence", "orig": "(GNN) methods and Image-to-Markup-Sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 484.121, "r_y1": 581.169, "r_x2": 484.121, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. 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The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 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false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.861, "r_y0": 163.59699999999998, "r_x1": 194.555, "r_y1": 163.59699999999998, "r_x2": 194.555, "r_y2": 154.79999999999995, "r_x3": 173.861, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "as is", "orig": "as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.61, "r_y0": 163.59699999999998, "r_x1": 211.535, "r_y1": 163.59699999999998, "r_x2": 211.535, "r_y2": 154.79999999999995, "r_x3": 199.61, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 215.769, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 214.836, "r": 480.596, "b": 331.23, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 334.648, "r": 480.596, "b": 439.087, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 236.769, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 484.121, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. In the recent past, a wide", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.393, "r_x1": 164.401, "r_y1": 521.393, "r_x2": 164.401, "r_y2": 512.596, "r_x3": 134.765, "r_y3": 512.596, "coord_origin": "TOPLEFT"}, "text": "variety", "orig": "variety", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.581, "r_y0": 521.393, "r_x1": 177.605, "r_y1": 521.393, "r_x2": 177.605, "r_y2": 512.596, "r_x3": 169.581, "r_y3": 512.596, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 182.786, "r_y0": 521.393, "r_x1": 223.77, "r_y1": 521.393, "r_x2": 223.77, "r_y2": 512.596, "r_x3": 182.786, "r_y3": 512.596, "coord_origin": 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"coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.355, "r_y0": 533.3489999999999, "r_x1": 480.59, "r_y1": 533.3489999999999, "r_x2": 480.59, "r_y2": 524.552, "r_x3": 442.355, "r_y3": 524.552, "coord_origin": "TOPLEFT"}, "text": "columns,", "orig": "columns,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.59, "r_y1": 545.304, "r_x2": 480.59, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "orig": "learning based approaches fall into three categories: object detection (OD) meth-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 152.252, "r_y1": 569.2139999999999, "r_x2": 152.252, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "ods,", "orig": "ods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.433, "r_y0": 569.2139999999999, "r_x1": 257.459, "r_y1": 569.2139999999999, "r_x2": 257.459, "r_y2": 560.417, "r_x3": 157.433, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "Graph-Neural-Network", "orig": "Graph-Neural-Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.65, "r_y0": 569.2139999999999, "r_x1": 480.591, "r_y1": 569.2139999999999, "r_x2": 480.591, "r_y2": 560.417, "r_x3": 262.65, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "(GNN) methods and Image-to-Markup-Sequence", "orig": "(GNN) methods and Image-to-Markup-Sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 484.121, "r_y1": 581.169, "r_x2": 484.121, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. The graph nodes represent the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "orig": "content of each table cell, an embedding vector from the table image, or geomet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.589, "r_y1": 652.9, "r_x2": 480.589, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "orig": "ric coordinates of the table cell. The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "body": [{"label": "text", "id": 2, "page_no": 2, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.592, "b": 211.41700000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9871802926063538, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 127.731, "r_x1": 480.592, "r_y1": 127.731, "r_x2": 480.592, "r_y2": 118.93399999999997, "r_x3": 149.709, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "While the majority of research in TSR is currently focused on the develop-", "orig": "While the majority of research in TSR is currently focused on the develop-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.587, "r_y1": 139.68600000000004, "r_x2": 480.587, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ment and application of novel neural model architectures, the table structure", "orig": "ment and application of novel neural model architectures, the table structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.592, "r_y1": 151.64200000000005, "r_x2": 480.592, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "orig": "representation language (e.g. HTML in PubTabNet and FinTabNet) is usually", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 169.625, "r_y1": 163.59699999999998, "r_x2": 169.625, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "adopted", "orig": "adopted", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 173.861, "r_y0": 163.59699999999998, "r_x1": 194.555, "r_y1": 163.59699999999998, "r_x2": 194.555, "r_y2": 154.79999999999995, "r_x3": 173.861, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "as is", "orig": "as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.61, "r_y0": 163.59699999999998, "r_x1": 211.535, "r_y1": 163.59699999999998, "r_x2": 211.535, "r_y2": 154.79999999999995, "r_x3": 199.61, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "for", "orig": "for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 215.769, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 215.769, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "the sequence tokenization in Im2Seq models. In this paper,", "orig": "the sequence tokenization in Im2Seq models. In this paper,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.592, "r_y1": 175.55200000000002, "r_x2": 480.592, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "we aim for the opposite and investigate the impact of the table structure rep-", "orig": "we aim for the opposite and investigate the impact of the table structure rep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.59, "r_y1": 187.50699999999995, "r_x2": 480.59, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "resentation language with an otherwise unmodified Im2Seq transformer-based", "orig": "resentation language with an otherwise unmodified Im2Seq transformer-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.592, "r_y1": 199.462, "r_x2": 480.592, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "orig": "architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9],", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 348.355, "r_y1": 211.41700000000003, "r_x2": 348.355, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "we select this model to perform our experiments.", "orig": "we select this model to perform our experiments.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments."}, {"label": "text", "id": 1, "page_no": 2, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 214.836, "r": 480.596, "b": 331.23, "coord_origin": "TOPLEFT"}, "confidence": 0.9871861338615417, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 223.63300000000004, "r_x1": 480.594, "r_y1": 223.63300000000004, "r_x2": 480.594, "r_y2": 214.836, "r_x3": 149.709, "r_y3": 214.836, "coord_origin": "TOPLEFT"}, "text": "The main contribution of this paper is the introduction of a new optimised ta-", "orig": "The main contribution of this paper is the introduction of a new optimised ta-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.58799999999997, "r_x1": 480.594, "r_y1": 235.58799999999997, "r_x2": 480.594, "r_y2": 226.79100000000005, "r_x3": 134.765, "r_y3": 226.79100000000005, "coord_origin": "TOPLEFT"}, "text": "ble structure language (OTSL), specifically designed to describe table-structure", "orig": "ble structure language (OTSL), specifically designed to describe table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.54399999999998, "r_x1": 480.587, "r_y1": 247.54399999999998, "r_x2": 480.587, "r_y2": 238.74699999999996, "r_x3": 134.765, "r_y3": 238.74699999999996, "coord_origin": "TOPLEFT"}, "text": "in an compact and structured way for Im2Seq models. OTSL has a number of", "orig": "in an compact and structured way for Im2Seq models. OTSL has a number of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.499, "r_x1": 480.587, "r_y1": 259.499, "r_x2": 480.587, "r_y2": 250.702, "r_x3": 134.765, "r_y3": 250.702, "coord_origin": "TOPLEFT"}, "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "orig": "key features, which make it very attractive to use in Im2Seq models. Specifically,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.45399999999995, "r_x1": 480.588, "r_y1": 271.45399999999995, "r_x2": 480.588, "r_y2": 262.65700000000004, "r_x3": 134.765, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "orig": "compared to other languages such as HTML, OTSL has a minimized vocabulary", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.596, "r_y1": 283.409, "r_x2": 480.596, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "gular layout) and a strict syntax with rules that only look backwards. The latter", "orig": "gular layout) and a strict syntax with rules that only look backwards. The latter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 480.595, "r_y1": 307.319, "r_x2": 480.595, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "allows for syntax validation during inference and ensures a syntactically correct", "orig": "allows for syntax validation during inference and ensures a syntactically correct", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 480.587, "r_y1": 319.275, "r_x2": 480.587, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 179.72, "r_y1": 331.23, "r_x2": 179.72, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to HTML.", "orig": "to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML."}, {"label": "text", "id": 0, "page_no": 2, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 334.648, "r": 480.596, "b": 439.087, "coord_origin": "TOPLEFT"}, "confidence": 0.988121747970581, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 343.445, "r_x1": 480.588, "r_y1": 343.445, "r_x2": 480.588, "r_y2": 334.648, "r_x3": 149.709, "r_y3": 334.648, "coord_origin": "TOPLEFT"}, "text": "The paper is structured as follows. In section 2, we give an overview of the", "orig": "The paper is structured as follows. In section 2, we give an overview of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.401, "r_x1": 158.613, "r_y1": 355.401, "r_x2": 158.613, "r_y2": 346.604, "r_x3": 134.765, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "latest", "orig": "latest", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.366, "r_y0": 355.401, "r_x1": 303.894, "r_y1": 355.401, "r_x2": 303.894, "r_y2": 346.604, "r_x3": 163.366, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "developments in table-structure", "orig": "developments in table-structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 308.636, "r_y0": 355.401, "r_x1": 373.487, "r_y1": 355.401, "r_x2": 373.487, "r_y2": 346.604, "r_x3": 308.636, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "reconstruction.", "orig": "reconstruction.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.239, "r_y0": 355.401, "r_x1": 387.369, "r_y1": 355.401, "r_x2": 387.369, "r_y2": 346.604, "r_x3": 378.239, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "In", "orig": "In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.111, "r_y0": 355.401, "r_x1": 422.055, "r_y1": 355.401, "r_x2": 422.055, "r_y2": 346.604, "r_x3": 392.111, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "section", "orig": "section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.798, "r_y0": 355.401, "r_x1": 480.594, "r_y1": 355.401, "r_x2": 480.594, "r_y2": 346.604, "r_x3": 426.798, "r_y3": 346.604, "coord_origin": "TOPLEFT"}, "text": "3 we review", "orig": "3 we review", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.356, "r_x1": 480.587, "r_y1": 367.356, "r_x2": 480.587, "r_y2": 358.559, "r_x3": 134.765, "r_y3": 358.559, "coord_origin": "TOPLEFT"}, "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "orig": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.311, "r_x1": 480.592, "r_y1": 379.311, "r_x2": 480.592, "r_y2": 370.514, "r_x3": 134.765, "r_y3": 370.514, "coord_origin": "TOPLEFT"}, "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "orig": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.266, "r_x1": 480.592, "r_y1": 391.266, "r_x2": 480.592, "r_y2": 382.469, "r_x3": 134.765, "r_y3": 382.469, "coord_origin": "TOPLEFT"}, "text": "cludes the language definition, syntax rules and error-correction procedures. In", "orig": "cludes the language definition, syntax rules and error-correction procedures. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.221, "r_x1": 480.588, "r_y1": 403.221, "r_x2": 480.588, "r_y2": 394.424, "r_x3": 134.765, "r_y3": 394.424, "coord_origin": "TOPLEFT"}, "text": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "orig": "section 5, we apply OTSL on the TableFormer architecture, compare it to Table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.177, "r_x1": 480.595, "r_y1": 415.177, "r_x2": 480.595, "r_y2": 406.38, "r_x3": 134.765, "r_y3": 406.38, "coord_origin": "TOPLEFT"}, "text": "Former models trained on HTML and ultimately demonstrate the advantages", "orig": "Former models trained on HTML and ultimately demonstrate the advantages", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.132, "r_x1": 142.789, "r_y1": 427.132, "r_x2": 142.789, "r_y2": 418.335, "r_x3": 134.765, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.431, "r_y0": 427.132, "r_x1": 170.173, "r_y1": 427.132, "r_x2": 170.173, "r_y2": 418.335, "r_x3": 147.431, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "using", "orig": "using", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.826, "r_y0": 427.132, "r_x1": 288.974, "r_y1": 427.132, "r_x2": 288.974, "r_y2": 418.335, "r_x3": 174.826, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "OTSL. Finally, in section", "orig": "OTSL. Finally, in section", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.627, "r_y0": 427.132, "r_x1": 480.596, "r_y1": 427.132, "r_x2": 480.596, "r_y2": 418.335, "r_x3": 293.627, "r_y3": 418.335, "coord_origin": "TOPLEFT"}, "text": "6 we conclude our work and outline next", "orig": "6 we conclude our work and outline next", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.087, "r_x1": 201.272, "r_y1": 439.087, "r_x2": 201.272, "r_y2": 430.29, "r_x3": 134.765, "r_y3": 430.29, "coord_origin": "TOPLEFT"}, "text": "potential steps.", "orig": "potential steps.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps."}, {"label": "section_header", "id": 4, "page_no": 2, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 462.088, "r": 236.769, "b": 472.656, "coord_origin": "TOPLEFT"}, "confidence": 0.9439423680305481, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.656, "r_x1": 141.489, "r_y1": 472.656, "r_x2": 141.489, "r_y2": 462.088, "r_x3": 134.765, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 472.656, "r_x1": 236.769, "r_y1": 472.656, "r_x2": 236.769, "r_y2": 462.088, "r_x3": 154.938, "r_y3": 462.088, "coord_origin": "TOPLEFT"}, "text": "Related Work", "orig": "Related Work", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2 Related Work"}, {"label": "text", "id": 3, "page_no": 2, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 488.686, "r": 484.121, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9870182871818542, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.483, "r_x1": 480.591, "r_y1": 497.483, "r_x2": 480.591, "r_y2": 488.686, "r_x3": 134.765, "r_y3": 488.686, "coord_origin": "TOPLEFT"}, "text": "Approaches to formalize the logical structure and layout of tables in electronic", "orig": "Approaches to formalize the logical structure and layout of tables in electronic", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.438, "r_x1": 480.592, "r_y1": 509.438, "r_x2": 480.592, "r_y2": 500.641, "r_x3": 134.765, "r_y3": 500.641, "coord_origin": "TOPLEFT"}, "text": "documents date back more than two decades [16]. In the recent past, a wide", "orig": "documents date back more than two decades [16]. 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"coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 442.355, "r_y0": 533.3489999999999, "r_x1": 480.59, "r_y1": 533.3489999999999, "r_x2": 480.59, "r_y2": 524.552, "r_x3": 442.355, "r_y3": 524.552, "coord_origin": "TOPLEFT"}, "text": "columns,", "orig": "columns,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 480.59, "r_y1": 545.304, "r_x2": 480.59, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "orig": "rows and spanning cells in a given table. Broadly speaking, the current deep-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.589, "r_y1": 557.259, "r_x2": 480.589, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "learning based approaches fall into three categories: object detection (OD) meth-", "orig": "learning based approaches fall into three categories: object detection (OD) meth-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 152.252, "r_y1": 569.2139999999999, "r_x2": 152.252, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "ods,", "orig": "ods,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.433, "r_y0": 569.2139999999999, "r_x1": 257.459, "r_y1": 569.2139999999999, "r_x2": 257.459, "r_y2": 560.417, "r_x3": 157.433, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "Graph-Neural-Network", "orig": "Graph-Neural-Network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.65, "r_y0": 569.2139999999999, "r_x1": 480.591, "r_y1": 569.2139999999999, "r_x2": 480.591, "r_y2": 560.417, "r_x3": 262.65, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "(GNN) methods and Image-to-Markup-Sequence", "orig": "(GNN) methods and Image-to-Markup-Sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 484.121, "r_y1": 581.169, "r_x2": 484.121, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "orig": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "structure annotation using (overlapping) bounding boxes for training, and pro-", "orig": "structure annotation using (overlapping) bounding boxes for training, and pro-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.589, "r_y1": 605.08, "r_x2": 480.589, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "duce bounding-box predictions to define table cells, rows, and columns on a table", "orig": "duce bounding-box predictions to define table cells, rows, and columns on a table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.591, "r_y1": 617.035, "r_x2": 480.591, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "orig": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.586, "r_y1": 628.99, "r_x2": 480.586, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "suggests, represent tables as graph structures. The graph nodes represent the", "orig": "suggests, represent tables as graph structures. The graph nodes represent the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "content of each table cell, an embedding vector from the table image, or geomet-", "orig": "content of each table cell, an embedding vector from the table image, or geomet-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.589, "r_y1": 652.9, "r_x2": 480.589, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "ric coordinates of the table cell. The edges of the graph define the relationship", "orig": "ric coordinates of the table cell. The edges of the graph define the relationship", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.591, "r_y1": 664.856, "r_x2": 480.591, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "orig": "between the nodes, e.g. if they belong to the same column, row, or table cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."}], "headers": [{"label": "page_header", "id": 5, "page_no": 2, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9325801730155945, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 2, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8773486614227295, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3"}]}}, {"page_no": 3, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. For each decoded table cell (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.299, "r_y0": 319.275, "r_x1": 356.971, "r_y1": 319.275, "r_x2": 356.971, "r_y2": 310.478, "r_x3": 333.299, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.085, "r_y0": 319.275, "r_x1": 363.725, "r_y1": 319.275, "r_x2": 363.725, "r_y2": 310.478, "r_x3": 357.085, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "),", "orig": "),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.64, "r_y0": 319.275, "r_x1": 480.594, "r_y1": 319.275, "r_x2": 480.594, "r_y2": 310.478, "r_x3": 367.64, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "the attention is passed to", "orig": "the attention is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 148.598, "r_y1": 331.23, "r_x2": 148.598, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.277, "r_y0": 331.23, "r_x1": 166.531, "r_y1": 331.23, "r_x2": 166.531, "r_y2": 322.433, "r_x3": 152.277, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.427, "r_y0": 331.23, "r_x1": 202.111, "r_y1": 331.23, "r_x2": 202.111, "r_y2": 322.433, "r_x3": 170.427, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "decoder", "orig": "decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.864, "r_y0": 331.23, "r_x1": 480.587, "r_y1": 331.23, "r_x2": 480.587, "r_y2": 322.433, "r_x3": 206.864, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to predict the content with an embedded OCR approach. The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.7280000000001, "r_x1": 176.662, "r_y1": 534.7280000000001, "r_x2": 176.662, "r_y2": 525.931, "r_x3": 134.765, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "to deliver", "orig": "to deliver", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.896, "r_y0": 534.7280000000001, "r_x1": 246.551, "r_y1": 534.7280000000001, "r_x2": 246.551, "r_y2": 525.931, "r_x3": 180.896, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.785, "r_y0": 534.7280000000001, "r_x1": 480.59, "r_y1": 534.7280000000001, "r_x2": 480.59, "r_y2": 525.931, "r_x3": 250.785, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "prediction accuracy [9]. This motivated the authors", "orig": "prediction accuracy [9]. 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558.639, "r_x2": 480.59, "r_y2": 549.842, "r_x3": 134.765, "r_y3": 549.842, "coord_origin": "TOPLEFT"}, "text": "be further improved by optimising the table structure representation language.", "orig": "be further improved by optimising the table structure representation language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 570.594, "r_x1": 183.086, "r_y1": 570.594, "r_x2": 183.086, "r_y2": 561.797, "r_x3": 134.765, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "We believe", "orig": "We believe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.977, "r_y0": 570.594, "r_x1": 204.079, "r_y1": 570.594, "r_x2": 204.079, "r_y2": 561.797, "r_x3": 187.977, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "this", "orig": "this", "text_direction": 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"improving", "orig": "improving", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 413.949, "r_y0": 570.594, "r_x1": 441.09, "r_y1": 570.594, "r_x2": 441.09, "r_y2": 561.797, "r_x3": 413.949, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "neural", "orig": "neural", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 445.982, "r_y0": 570.594, "r_x1": 480.589, "r_y1": 570.594, "r_x2": 480.589, "r_y2": 561.797, "r_x3": 445.982, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "network", "orig": "network", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 582.549, "r_x1": 249.278, "r_y1": 582.549, "r_x2": 249.278, "r_y2": 573.752, "r_x3": 134.765, "r_y3": 573.752, "coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, 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These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 259.23800000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 262.65700000000004, "r": 480.596, "b": 486.647, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. For each decoded table cell (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.299, "r_y0": 319.275, "r_x1": 356.971, "r_y1": 319.275, "r_x2": 356.971, "r_y2": 310.478, "r_x3": 333.299, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.085, "r_y0": 319.275, "r_x1": 363.725, "r_y1": 319.275, "r_x2": 363.725, "r_y2": 310.478, "r_x3": 357.085, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "),", "orig": "),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.64, "r_y0": 319.275, "r_x1": 480.594, "r_y1": 319.275, "r_x2": 480.594, "r_y2": 310.478, "r_x3": 367.64, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "the attention is passed to", "orig": "the attention is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 148.598, "r_y1": 331.23, "r_x2": 148.598, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.277, "r_y0": 331.23, "r_x1": 166.531, "r_y1": 331.23, "r_x2": 166.531, "r_y2": 322.433, "r_x3": 152.277, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.427, "r_y0": 331.23, "r_x1": 202.111, "r_y1": 331.23, "r_x2": 202.111, "r_y2": 322.433, "r_x3": 170.427, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "decoder", "orig": "decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.864, "r_y0": 331.23, "r_x1": 480.587, "r_y1": 331.23, "r_x2": 480.587, "r_y2": 322.433, "r_x3": 206.864, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to predict the content with an embedded OCR approach. The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 490.066, "r": 480.594, "b": 582.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. 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This motivated the authors", "orig": "prediction accuracy [9]. 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558.639, "r_x2": 480.59, "r_y2": 549.842, "r_x3": 134.765, "r_y3": 549.842, "coord_origin": "TOPLEFT"}, "text": "be further improved by optimising the table structure representation language.", "orig": "be further improved by optimising the table structure representation language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 570.594, "r_x1": 183.086, "r_y1": 570.594, "r_x2": 183.086, "r_y2": 561.797, "r_x3": 134.765, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "We believe", "orig": "We believe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.977, "r_y0": 570.594, "r_x1": 204.079, "r_y1": 570.594, "r_x2": 204.079, "r_y2": 561.797, "r_x3": 187.977, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "this", "orig": "this", "text_direction": 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"coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 605.55, "r": 269.624, "b": 616.1179999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.594, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.591, "r_y1": 640.9449999999999, "r_x2": 480.591, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.587, "r_y1": 652.9, "r_x2": 480.587, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 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These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 259.23800000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 262.65700000000004, "r": 480.596, "b": 486.647, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. For each decoded table cell (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 333.299, "r_y0": 319.275, "r_x1": 356.971, "r_y1": 319.275, "r_x2": 356.971, "r_y2": 310.478, "r_x3": 333.299, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 357.085, "r_y0": 319.275, "r_x1": 363.725, "r_y1": 319.275, "r_x2": 363.725, "r_y2": 310.478, "r_x3": 357.085, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "),", "orig": "),", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 367.64, "r_y0": 319.275, "r_x1": 480.594, "r_y1": 319.275, "r_x2": 480.594, "r_y2": 310.478, "r_x3": 367.64, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "the attention is passed to", "orig": "the attention is passed to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.23, "r_x1": 148.598, "r_y1": 331.23, "r_x2": 148.598, "r_y2": 322.433, "r_x3": 134.765, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.277, "r_y0": 331.23, "r_x1": 166.531, "r_y1": 331.23, "r_x2": 166.531, "r_y2": 322.433, "r_x3": 152.277, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "cell", "orig": "cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.427, "r_y0": 331.23, "r_x1": 202.111, "r_y1": 331.23, "r_x2": 202.111, "r_y2": 322.433, "r_x3": 170.427, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "decoder", "orig": "decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 206.864, "r_y0": 331.23, "r_x1": 480.587, "r_y1": 331.23, "r_x2": 480.587, "r_y2": 322.433, "r_x3": 206.864, "r_y3": 322.433, "coord_origin": "TOPLEFT"}, "text": "to predict the content with an embedded OCR approach. The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 490.066, "r": 480.594, "b": 582.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.7280000000001, "r_x1": 176.662, "r_y1": 534.7280000000001, "r_x2": 176.662, "r_y2": 525.931, "r_x3": 134.765, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "to deliver", "orig": "to deliver", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.896, "r_y0": 534.7280000000001, "r_x1": 246.551, "r_y1": 534.7280000000001, "r_x2": 246.551, "r_y2": 525.931, "r_x3": 180.896, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.785, "r_y0": 534.7280000000001, "r_x1": 480.59, "r_y1": 534.7280000000001, "r_x2": 480.59, "r_y2": 525.931, "r_x3": 250.785, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "prediction accuracy [9]. This motivated the authors", "orig": "prediction accuracy [9]. This motivated the authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 546.683, "r_x1": 143.619, "r_y1": 546.683, "r_x2": 143.619, "r_y2": 537.886, "r_x3": 134.765, "r_y3": 537.886, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.56, "r_y0": 546.683, "r_x1": 194.815, "r_y1": 546.683, "r_x2": 194.815, "r_y2": 537.886, "r_x3": 148.56, "r_y3": 537.886, "coord_origin": "TOPLEFT"}, "text": "investigate", "orig": "investigate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.756, "r_y0": 546.683, "r_x1": 205.566, "r_y1": 546.683, "r_x2": 205.566, "r_y2": 537.886, "r_x3": 199.756, "r_y3": 537.886, 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558.639, "r_x2": 480.59, "r_y2": 549.842, "r_x3": 134.765, "r_y3": 549.842, "coord_origin": "TOPLEFT"}, "text": "be further improved by optimising the table structure representation language.", "orig": "be further improved by optimising the table structure representation language.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 570.594, "r_x1": 183.086, "r_y1": 570.594, "r_x2": 183.086, "r_y2": 561.797, "r_x3": 134.765, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "We believe", "orig": "We believe", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.977, "r_y0": 570.594, "r_x1": 204.079, "r_y1": 570.594, "r_x2": 204.079, "r_y2": 561.797, "r_x3": 187.977, "r_y3": 561.797, "coord_origin": "TOPLEFT"}, "text": "this", "orig": "this", "text_direction": 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"coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 605.55, "r": 269.624, "b": 616.1179999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.594, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.591, "r_y1": 640.9449999999999, "r_x2": 480.591, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.587, "r_y1": 652.9, "r_x2": 480.587, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "orig": "Given an image of a table, the Im2Seq model predicts the structure of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.594, "r_y1": 664.856, "r_x2": 480.594, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "body": [{"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 259.23800000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9467443823814392, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells", "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.588, "r_y1": 139.68600000000004, "r_x2": 480.588, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "must be merged using an attention network. Im2Seq methods cast the problem", "orig": "must be merged using an attention network. Im2Seq methods cast the problem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 480.587, "r_y1": 151.64200000000005, "r_x2": 480.587, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 174.162, "r_y1": 163.59699999999998, "r_x2": 174.162, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "structure", "orig": "structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 178.755, "r_y0": 163.59699999999998, "r_x1": 240.56, "r_y1": 163.59699999999998, "r_x2": 240.56, "r_y2": 154.79999999999995, "r_x3": 178.755, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "representation", "orig": "representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 245.143, "r_y0": 163.59699999999998, "r_x1": 286.09, "r_y1": 163.59699999999998, "r_x2": 286.09, "r_y2": 154.79999999999995, "r_x3": 245.143, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "language,", "orig": "language,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 290.683, "r_y0": 163.59699999999998, "r_x1": 327.144, "r_y1": 163.59699999999998, "r_x2": 327.144, "r_y2": 154.79999999999995, "r_x3": 290.683, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "which is", "orig": "which is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.727, "r_y0": 163.59699999999998, "r_x1": 353.584, "r_y1": 163.59699999999998, "r_x2": 353.584, "r_y2": 154.79999999999995, "r_x3": 331.727, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "often", "orig": "often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 358.176, "r_y0": 163.59699999999998, "r_x1": 480.588, "r_y1": 163.59699999999998, "r_x2": 480.588, "r_y2": 154.79999999999995, "r_x3": 358.176, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "implemented with standard", "orig": "implemented with standard", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.593, "r_y1": 175.55200000000002, "r_x2": 480.593, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 480.596, "r_y1": 187.50699999999995, "r_x2": 480.596, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "have a natural advantage over the OD and GNN methods by virtue of directly", "orig": "have a natural advantage over the OD and GNN methods by virtue of directly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 199.462, "r_x1": 480.593, "r_y1": 199.462, "r_x2": 480.593, "r_y2": 190.66499999999996, "r_x3": 134.765, "r_y3": 190.66499999999996, "coord_origin": "TOPLEFT"}, "text": "predicting the table-structure. As such, no post-processing or rules are needed", "orig": "predicting the table-structure. As such, no post-processing or rules are needed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 211.41700000000003, "r_x1": 143.065, "r_y1": 211.41700000000003, "r_x2": 143.065, "r_y2": 202.62, "r_x3": 134.765, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.329, "r_y0": 211.41700000000003, "r_x1": 480.594, "r_y1": 211.41700000000003, "r_x2": 480.594, "r_y2": 202.62, "r_x3": 147.329, "r_y3": 202.62, "coord_origin": "TOPLEFT"}, "text": "order to obtain the table-structure, which is necessary with OD and GNN", "orig": "order to obtain the table-structure, which is necessary with OD and GNN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 223.37300000000005, "r_x1": 480.588, "r_y1": 223.37300000000005, "r_x2": 480.588, "r_y2": 214.57600000000002, "r_x3": 134.765, "r_y3": 214.57600000000002, "coord_origin": "TOPLEFT"}, "text": "approaches. In practice, this is not entirely true, because a predicted sequence", "orig": "approaches. In practice, this is not entirely true, because a predicted sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.32799999999997, "r_x1": 480.59, "r_y1": 235.32799999999997, "r_x2": 480.59, "r_y2": 226.53099999999995, "r_x3": 134.765, "r_y3": 226.53099999999995, "coord_origin": "TOPLEFT"}, "text": "of table-structure markup does not necessarily have to be syntactically correct.", "orig": "of table-structure markup does not necessarily have to be syntactically correct.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.28300000000002, "r_x1": 480.596, "r_y1": 247.28300000000002, "r_x2": 480.596, "r_y2": 238.486, "r_x3": 134.765, "r_y3": 238.486, "coord_origin": "TOPLEFT"}, "text": "Hence, depending on the quality of the predicted sequence, some post-processing", "orig": "Hence, depending on the quality of the predicted sequence, some post-processing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.23800000000006, "r_x1": 480.595, "r_y1": 259.23800000000006, "r_x2": 480.595, "r_y2": 250.44100000000003, "r_x3": 134.765, "r_y3": 250.44100000000003, "coord_origin": "TOPLEFT"}, "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 262.65700000000004, "r": 480.596, "b": 486.647, "coord_origin": "TOPLEFT"}, "confidence": 0.929087221622467, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 271.45399999999995, "r_x1": 199.213, "r_y1": 271.45399999999995, "r_x2": 199.213, "r_y2": 262.65700000000004, "r_x3": 149.709, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Within the", "orig": "Within the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 204.175, "r_y0": 271.45399999999995, "r_x1": 349.163, "r_y1": 271.45399999999995, "r_x2": 349.163, "r_y2": 262.65700000000004, "r_x3": 204.175, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "Im2Seq method, we find several", "orig": "Im2Seq method, we find several", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 354.125, "r_y0": 271.45399999999995, "r_x1": 387.632, "r_y1": 271.45399999999995, "r_x2": 387.632, "r_y2": 262.65700000000004, "r_x3": 354.125, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "popular", "orig": "popular", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 392.593, "r_y0": 271.45399999999995, "r_x1": 425.573, "r_y1": 271.45399999999995, "r_x2": 425.573, "r_y2": 262.65700000000004, "r_x3": 392.593, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "models,", "orig": "models,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 430.535, "r_y0": 271.45399999999995, "r_x1": 480.593, "r_y1": 271.45399999999995, "r_x2": 480.593, "r_y2": 262.65700000000004, "r_x3": 430.535, "r_y3": 262.65700000000004, "coord_origin": "TOPLEFT"}, "text": "namely the", "orig": "namely the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.409, "r_x1": 480.592, "r_y1": 283.409, "r_x2": 480.592, "r_y2": 274.61199999999997, "r_x3": 134.765, "r_y3": 274.61199999999997, "coord_origin": "TOPLEFT"}, "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.364, "r_x1": 480.593, "r_y1": 295.364, "r_x2": 480.593, "r_y2": 286.567, "r_x3": 134.765, "r_y3": 286.567, "coord_origin": "TOPLEFT"}, "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 307.319, "r_x1": 342.021, "r_y1": 307.319, "r_x2": 342.021, "r_y2": 298.522, "r_x3": 134.765, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "to predict a table in HTML representation. The", "orig": "to predict a table in HTML representation. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.064, "r_y0": 307.319, "r_x1": 393.047, "r_y1": 307.319, "r_x2": 393.047, "r_y2": 298.522, "r_x3": 345.064, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "tag decoder", "orig": "tag decoder", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 397.167, "r_y0": 307.319, "r_x1": 480.591, "r_y1": 307.319, "r_x2": 480.591, "r_y2": 298.522, "r_x3": 397.167, "r_y3": 298.522, "coord_origin": "TOPLEFT"}, "text": "predicts a sequence", "orig": "predicts a sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 319.275, "r_x1": 333.299, "r_y1": 319.275, "r_x2": 333.299, "r_y2": 310.478, "r_x3": 134.765, "r_y3": 310.478, "coord_origin": "TOPLEFT"}, "text": "of HTML tags. For each decoded table cell (", "orig": "of HTML tags. 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The", "orig": "to predict the content with an embedded OCR approach. The", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 343.185, "r_x1": 480.595, "r_y1": 343.185, "r_x2": 480.595, "r_y2": 334.388, "r_x3": 134.765, "r_y3": 334.388, "coord_origin": "TOPLEFT"}, "text": "latter makes it susceptible to transcription errors in the cell content of the table.", "orig": "latter makes it susceptible to transcription errors in the cell content of the table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 355.14, "r_x1": 480.587, "r_y1": 355.14, "r_x2": 480.587, "r_y2": 346.343, "r_x3": 134.765, "r_y3": 346.343, "coord_origin": "TOPLEFT"}, "text": "TableFormer address this reliance on OCR and uses two transformer decoders for", "orig": "TableFormer address this reliance on OCR and uses two transformer decoders for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 367.095, "r_x1": 480.589, "r_y1": 367.095, "r_x2": 480.589, "r_y2": 358.298, "r_x3": 134.765, "r_y3": 358.298, "coord_origin": "TOPLEFT"}, "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "orig": "HTML structure and cell bounding box prediction in an end-to-end architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.05, "r_x1": 480.589, "r_y1": 379.05, "r_x2": 480.589, "r_y2": 370.253, "r_x3": 134.765, "r_y3": 370.253, "coord_origin": "TOPLEFT"}, "text": "The predicted cell bounding box is then used to extract text tokens from an", "orig": "The predicted cell bounding box is then used to extract text tokens from an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.006, "r_x1": 480.594, "r_y1": 391.006, "r_x2": 480.594, "r_y2": 382.209, "r_x3": 134.765, "r_y3": 382.209, "coord_origin": "TOPLEFT"}, "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "orig": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 402.961, "r_x1": 480.587, "r_y1": 402.961, "r_x2": 480.587, "r_y2": 394.164, "r_x3": 134.765, "r_y3": 394.164, "coord_origin": "TOPLEFT"}, "text": "[2] proposes a compact double-matrix representation of table rows and columns", "orig": "[2] proposes a compact double-matrix representation of table rows and columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 414.916, "r_x1": 480.596, "r_y1": 414.916, "r_x2": 480.596, "r_y2": 406.119, "r_x3": 134.765, "r_y3": 406.119, "coord_origin": "TOPLEFT"}, "text": "to do error detection and error correction of HTML structure sequences based", "orig": "to do error detection and error correction of HTML structure sequences based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 426.871, "r_x1": 480.592, "r_y1": 426.871, "r_x2": 480.592, "r_y2": 418.074, "r_x3": 134.765, "r_y3": 418.074, "coord_origin": "TOPLEFT"}, "text": "on predictions from [19]. This compact double-matrix representation can not be", "orig": "on predictions from [19]. This compact double-matrix representation can not be", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 438.826, "r_x1": 480.588, "r_y1": 438.826, "r_x2": 480.588, "r_y2": 430.029, "r_x3": 134.765, "r_y3": 430.029, "coord_origin": "TOPLEFT"}, "text": "used directly by the Img2seq model training, so the model uses HTML as an", "orig": "used directly by the Img2seq model training, so the model uses HTML as an", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 450.781, "r_x1": 480.589, "r_y1": 450.781, "r_x2": 480.589, "r_y2": 441.984, "r_x3": 134.765, "r_y3": 441.984, "coord_origin": "TOPLEFT"}, "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 462.737, "r_x1": 384.575, "r_y1": 462.737, "r_x2": 384.575, "r_y2": 453.94, "r_x3": 134.765, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "using bidirectional LSTMs to predict LaTeX code. Kayal", "orig": "using bidirectional LSTMs to predict LaTeX code. Kayal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 391.559, "r_y0": 462.737, "r_x1": 480.588, "r_y1": 462.737, "r_x2": 480.588, "r_y2": 453.94, "r_x3": 391.559, "r_y3": 453.94, "coord_origin": "TOPLEFT"}, "text": "[5] introduces Gated", "orig": "[5] introduces Gated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.692, "r_x1": 480.591, "r_y1": 474.692, "r_x2": 480.591, "r_y2": 465.895, "r_x3": 134.765, "r_y3": 465.895, "coord_origin": "TOPLEFT"}, "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "orig": "ResNet transformers to predict LaTeX code, and a separate OCR module to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 486.647, "r_x1": 203.686, "r_y1": 486.647, "r_x2": 203.686, "r_y2": 477.85, "r_x3": 134.765, "r_y3": 477.85, "coord_origin": "TOPLEFT"}, "text": "extract content.", "orig": "extract content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 490.066, "r": 480.594, "b": 582.549, "coord_origin": "TOPLEFT"}, "confidence": 0.9853583574295044, "cells": [{"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 498.863, "r_x1": 480.594, "r_y1": 498.863, "r_x2": 480.594, "r_y2": 490.066, "r_x3": 149.709, "r_y3": 490.066, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "orig": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 510.818, "r_x1": 480.589, "r_y1": 510.818, "r_x2": 480.589, "r_y2": 502.021, "r_x3": 134.765, "r_y3": 502.021, "coord_origin": "TOPLEFT"}, "text": "full end-to-end network design that can output the final table structure without", "orig": "full end-to-end network design that can output the final table structure without", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 522.773, "r_x1": 480.587, "r_y1": 522.773, "r_x2": 480.587, "r_y2": 513.976, "r_x3": 134.765, "r_y3": 513.976, "coord_origin": "TOPLEFT"}, "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "orig": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 534.7280000000001, "r_x1": 176.662, "r_y1": 534.7280000000001, "r_x2": 176.662, "r_y2": 525.931, "r_x3": 134.765, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "to deliver", "orig": "to deliver", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.896, "r_y0": 534.7280000000001, "r_x1": 246.551, "r_y1": 534.7280000000001, "r_x2": 246.551, "r_y2": 525.931, "r_x3": 180.896, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "state-of-the-art", "orig": "state-of-the-art", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 250.785, "r_y0": 534.7280000000001, "r_x1": 480.59, "r_y1": 534.7280000000001, "r_x2": 480.59, "r_y2": 525.931, "r_x3": 250.785, "r_y3": 525.931, "coord_origin": "TOPLEFT"}, "text": "prediction accuracy [9]. This motivated the authors", "orig": "prediction accuracy [9]. This motivated the authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 546.683, "r_x1": 143.619, "r_y1": 546.683, "r_x2": 143.619, "r_y2": 537.886, "r_x3": 134.765, "r_y3": 537.886, "coord_origin": "TOPLEFT"}, "text": "to", "orig": "to", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.56, "r_y0": 546.683, "r_x1": 194.815, "r_y1": 546.683, "r_x2": 194.815, "r_y2": 537.886, "r_x3": 148.56, "r_y3": 537.886, "coord_origin": "TOPLEFT"}, "text": "investigate", "orig": "investigate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 199.756, "r_y0": 546.683, "r_x1": 205.566, "r_y1": 546.683, "r_x2": 205.566, "r_y2": 537.886, "r_x3": 199.756, "r_y3": 537.886, 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"coord_origin": "TOPLEFT"}, "text": "architectures for this task.", "orig": "architectures for this task.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."}, {"label": "section_header", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 605.55, "r": 269.624, "b": 616.1179999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9393903017044067, "cells": [{"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 616.1179999999999, "r_x1": 141.489, "r_y1": 616.1179999999999, "r_x2": 141.489, "r_y2": 605.55, "r_x3": 134.765, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 616.1179999999999, "r_x1": 269.624, "r_y1": 616.1179999999999, "r_x2": 269.624, "r_y2": 605.55, "r_x3": 154.938, "r_y3": 605.55, "coord_origin": "TOPLEFT"}, "text": "Problem Statement", "orig": "Problem Statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3 Problem Statement"}, {"label": "text", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 632.148, "r": 480.594, "b": 664.856, "coord_origin": "TOPLEFT"}, "confidence": 0.9692807197570801, "cells": [{"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.591, "r_y1": 640.9449999999999, "r_x2": 480.591, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.587, "r_y1": 652.9, "r_x2": 480.587, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "Given an image of a table, the Im2Seq model predicts the structure of the table", "orig": "Given an image of a table, the Im2Seq model predicts the structure of the table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.856, "r_x1": 480.594, "r_y1": 664.856, "r_x2": 480.594, "r_y2": 656.059, "r_x3": 134.765, "r_y3": 656.059, "coord_origin": "TOPLEFT"}, "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"}], "headers": [{"label": "page_header", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7890051603317261, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_header", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9045588374137878, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 187.50699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 218.86400000000003, "r": 469.752, "b": 230.15300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.207, "r": 480.595, "b": 532.421, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 536.043, "r": 480.593, "b": 580.706, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 584.327, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 187.50699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 218.86400000000003, "r": 469.752, "b": 230.15300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.207, "r": 480.595, "b": 532.421, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 536.043, "r": 480.593, "b": 580.706, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 584.327, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.594, "b": 187.50699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9857171773910522, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.586, "r_y1": 127.731, "r_x2": 480.586, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "ulary and can be interpreted as a table structure. 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In reality though, one needs", "orig": "simple table structures without any spanning cells. In reality though, one needs", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 163.59699999999998, "r_x1": 480.586, "r_y1": 163.59699999999998, "r_x2": 480.586, "r_y2": 154.79999999999995, "r_x3": 134.765, "r_y3": 154.79999999999995, "coord_origin": "TOPLEFT"}, "text": "at least 28 HTML tokens to describe the most common complex tables observed", "orig": "at least 28 HTML tokens to describe the most common complex tables observed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 175.55200000000002, "r_x1": 480.594, "r_y1": 175.55200000000002, "r_x2": 480.594, "r_y2": 166.755, "r_x3": 134.765, "r_y3": 166.755, "coord_origin": "TOPLEFT"}, "text": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "orig": "in real-world documents [21,22], due to a variety of spanning cells definitions in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 187.50699999999995, "r_x1": 261.926, "r_y1": 187.50699999999995, "r_x2": 261.926, "r_y2": 178.71000000000004, "r_x3": 134.765, "r_y3": 178.71000000000004, "coord_origin": "TOPLEFT"}, "text": "the HTML token vocabulary.", "orig": "the HTML token vocabulary.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary."}, {"label": "caption", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 145.607, "t": 218.86400000000003, "r": 469.752, "b": 230.15300000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.8472551703453064, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.607, "r_y0": 229.005, "r_x1": 173.486, "r_y1": 229.005, "r_x2": 173.486, "r_y2": 221.07899999999995, "r_x3": 145.607, "r_y3": 221.07899999999995, "coord_origin": "TOPLEFT"}, "text": "Fig. 2.", "orig": "Fig. 2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 176.56, "r_y0": 230.15300000000002, "r_x1": 469.752, "r_y1": 230.15300000000002, "r_x2": 469.752, "r_y2": 218.86400000000003, "r_x3": 176.56, "r_y3": 218.86400000000003, "coord_origin": "TOPLEFT"}, "text": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "orig": "Frequency of tokens in HTML and OTSL as they appear in PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 368.207, "r": 480.595, "b": 532.421, "coord_origin": "TOPLEFT"}, "confidence": 0.9844799637794495, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 377.004, "r_x1": 480.593, "r_y1": 377.004, "r_x2": 480.593, "r_y2": 368.207, "r_x3": 149.709, "r_y3": 368.207, "coord_origin": "TOPLEFT"}, "text": "Obviously, HTML and other general-purpose markup languages were not de-", "orig": "Obviously, HTML and other general-purpose markup languages were not de-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 388.959, "r_x1": 480.587, "r_y1": 388.959, "r_x2": 480.587, "r_y2": 380.162, "r_x3": 134.765, "r_y3": 380.162, "coord_origin": "TOPLEFT"}, "text": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "orig": "signed for Im2Seq models. As such, they have some serious drawbacks. First, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 400.914, "r_x1": 480.592, "r_y1": 400.914, "r_x2": 480.592, "r_y2": 392.117, "r_x3": 134.765, "r_y3": 392.117, "coord_origin": "TOPLEFT"}, "text": "token vocabulary needs to be artificially large in order to describe all plausible", "orig": "token vocabulary needs to be artificially large in order to describe all plausible", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 412.869, "r_x1": 480.59, "r_y1": 412.869, "r_x2": 480.59, "r_y2": 404.072, "r_x3": 134.765, "r_y3": 404.072, "coord_origin": "TOPLEFT"}, "text": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "orig": "tabular structures. Since most Im2Seq models use an autoregressive approach,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 424.825, "r_x1": 480.589, "r_y1": 424.825, "r_x2": 480.589, "r_y2": 416.028, "r_x3": 134.765, "r_y3": 416.028, "coord_origin": "TOPLEFT"}, "text": "they generate the sequence token by token. Therefore, to reduce inference time,", "orig": "they generate the sequence token by token. Therefore, to reduce inference time,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 436.78, "r_x1": 480.593, "r_y1": 436.78, "r_x2": 480.593, "r_y2": 427.983, "r_x3": 134.765, "r_y3": 427.983, "coord_origin": "TOPLEFT"}, "text": "a shorter sequence length is critical. Every table-cell is represented by at least", "orig": "a shorter sequence length is critical. Every table-cell is represented by at least", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 448.735, "r_x1": 187.934, "r_y1": 448.735, "r_x2": 187.934, "r_y2": 439.938, "r_x3": 134.765, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "two tokens (", "orig": "two tokens (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.931, "r_y0": 448.735, "r_x1": 211.603, "r_y1": 448.735, "r_x2": 211.603, "r_y2": 439.938, "r_x3": 187.931, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.754, "r_y0": 448.735, "r_x1": 230.801, "r_y1": 448.735, "r_x2": 230.801, "r_y2": 439.938, "r_x3": 214.754, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 233.839, "r_y0": 448.735, "r_x1": 262.602, "r_y1": 448.735, "r_x2": 262.602, "r_y2": 439.938, "r_x3": 233.839, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "", "orig": "", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.716, "r_y0": 448.735, "r_x1": 480.59, "r_y1": 448.735, "r_x2": 480.59, "r_y2": 439.938, "r_x3": 262.716, "r_y3": 439.938, "coord_origin": "TOPLEFT"}, "text": "). Furthermore, when tokenizing the HTML struc-", "orig": "). Furthermore, when tokenizing the HTML struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 460.69, "r_x1": 155.266, "r_y1": 460.69, "r_x2": 155.266, "r_y2": 451.893, "r_x3": 134.765, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "ture,", "orig": "ture,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.789, "r_y0": 460.69, "r_x1": 260.86, "r_y1": 460.69, "r_x2": 260.86, "r_y2": 451.893, "r_x3": 159.789, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "one needs to explicitly", "orig": "one needs to explicitly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.383, "r_y0": 460.69, "r_x1": 480.588, "r_y1": 460.69, "r_x2": 480.588, "r_y2": 451.893, "r_x3": 265.383, "r_y3": 451.893, "coord_origin": "TOPLEFT"}, "text": "enumerate possible column-spans and row-spans", "orig": "enumerate possible column-spans and row-spans", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 472.645, "r_x1": 480.587, "r_y1": 472.645, "r_x2": 480.587, "r_y2": 463.848, "r_x3": 134.765, "r_y3": 463.848, "coord_origin": "TOPLEFT"}, "text": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "orig": "as words. In practice, this ends up requiring 28 different HTML tokens (when", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 484.601, "r_x1": 480.587, "r_y1": 484.601, "r_x2": 480.587, "r_y2": 475.804, "r_x3": 134.765, "r_y3": 475.804, "coord_origin": "TOPLEFT"}, "text": "including column- and row-spans up to 10 cells) just to describe every table in", "orig": "including column- and row-spans up to 10 cells) just to describe every table in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 496.556, "r_x1": 480.591, "r_y1": 496.556, "r_x2": 480.591, "r_y2": 487.759, "r_x3": 134.765, "r_y3": 487.759, "coord_origin": "TOPLEFT"}, "text": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "orig": "the PubTabNet dataset. Clearly, not every token is equally represented, as is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 508.511, "r_x1": 480.593, "r_y1": 508.511, "r_x2": 480.593, "r_y2": 499.714, "r_x3": 134.765, "r_y3": 499.714, "coord_origin": "TOPLEFT"}, "text": "depicted in Figure 2. This skewed distribution of tokens in combination with", "orig": "depicted in Figure 2. This skewed distribution of tokens in combination with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 520.466, "r_x1": 480.595, "r_y1": 520.466, "r_x2": 480.595, "r_y2": 511.669, "r_x3": 134.765, "r_y3": 511.669, "coord_origin": "TOPLEFT"}, "text": "variable token row-length makes it challenging for models to learn the HTML", "orig": "variable token row-length makes it challenging for models to learn the HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 532.421, "r_x1": 176.929, "r_y1": 532.421, "r_x2": 176.929, "r_y2": 523.624, "r_x3": 134.765, "r_y3": 523.624, "coord_origin": "TOPLEFT"}, "text": "structure.", "orig": "structure.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure."}, {"label": "text", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 536.043, "r": 480.593, "b": 580.706, "coord_origin": "TOPLEFT"}, "confidence": 0.978394627571106, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 544.84, "r_x1": 480.593, "r_y1": 544.84, "r_x2": 480.593, "r_y2": 536.043, "r_x3": 149.709, "r_y3": 536.043, "coord_origin": "TOPLEFT"}, "text": "Additionally, it would be desirable if the representation would easily allow", "orig": "Additionally, it would be desirable if the representation would easily allow", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 556.795, "r_x1": 480.591, "r_y1": 556.795, "r_x2": 480.591, "r_y2": 547.998, "r_x3": 134.765, "r_y3": 547.998, "coord_origin": "TOPLEFT"}, "text": "an early detection of invalid sequences on-the-go, before the prediction of the", "orig": "an early detection of invalid sequences on-the-go, before the prediction of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 568.75, "r_x1": 480.59, "r_y1": 568.75, "r_x2": 480.59, "r_y2": 559.953, "r_x3": 134.765, "r_y3": 559.953, "coord_origin": "TOPLEFT"}, "text": "entire table structure is completed. HTML is not well-suited for this purpose as", "orig": "entire table structure is completed. HTML is not well-suited for this purpose as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 580.706, "r_x1": 452.189, "r_y1": 580.706, "r_x2": 452.189, "r_y2": 571.909, "r_x3": 134.765, "r_y3": 571.909, "coord_origin": "TOPLEFT"}, "text": "the verification of incomplete sequences is non-trivial or even impossible.", "orig": "the verification of incomplete sequences is non-trivial or even impossible.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 584.327, "r": 480.595, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9838283061981201, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 593.124, "r_x1": 480.593, "r_y1": 593.124, "r_x2": 480.593, "r_y2": 584.327, "r_x3": 149.709, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table", "orig": "In a valid HTML table, the token sequence must describe a 2D grid of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 155.846, "r_y1": 605.08, "r_x2": 155.846, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "cells,", "orig": "cells,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.09, "r_y0": 605.08, "r_x1": 199.514, "r_y1": 605.08, "r_x2": 199.514, "r_y2": 596.283, "r_x3": 160.09, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "serialised", "orig": "serialised", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 203.758, "r_y0": 605.08, "r_x1": 212.058, "r_y1": 605.08, "r_x2": 212.058, "r_y2": 596.283, "r_x3": 203.758, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.302, "r_y0": 605.08, "r_x1": 480.59, "r_y1": 605.08, "r_x2": 480.59, "r_y2": 596.283, "r_x3": 216.302, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "row-major ordering, where each row and each column have", "orig": "row-major ordering, where each row and each column have", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 480.594, "r_y1": 617.035, "r_x2": 480.594, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "the same length (while considering row- and column-spans). Furthermore, every", "orig": "the same length (while considering row- and column-spans). Furthermore, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.99, "r_x1": 480.591, "r_y1": 628.99, "r_x2": 480.591, "r_y2": 620.193, "r_x3": 134.765, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "orig": "opening tag in HTML needs to be matched by a closing tag in a correct hierar-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.586, "r_y1": 640.9449999999999, "r_x2": 480.586, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "chical manner. Since the number of tokens for each table row and column can", "orig": "chical manner. Since the number of tokens for each table row and column can", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "vary significantly, especially for large tables with many row- and column-spans,", "orig": "vary significantly, especially for large tables with many row- and column-spans,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 141.405, "r_y1": 664.855, "r_x2": 141.405, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "it", "orig": "it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 145.281, "r_y0": 664.855, "r_x1": 151.975, "r_y1": 664.855, "r_x2": 151.975, "r_y2": 656.058, "r_x3": 145.281, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "is", "orig": "is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 155.861, "r_y0": 664.855, "r_x1": 480.595, "r_y1": 664.855, "r_x2": 480.595, "r_y2": 656.058, "r_x3": 155.861, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "complex to verify the consistency of predicted structures during sequence", "orig": "complex to verify the consistency of predicted structures during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence"}, {"label": "picture", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "picture", "bbox": {"l": 137.41448974609375, "t": 233.51231384277344, "r": 476.5608215332031, "b": 340.2304992675781, "coord_origin": "TOPLEFT"}, "confidence": 0.9387977123260498, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9269194602966309, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8696112036705017, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": 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neighbor cell to create a", "orig": "merging with the upper neighbor cell to create a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 605.874, "r_x1": 171.676, "r_y1": 605.874, "r_x2": 171.676, "r_y2": 597.077, "r_x3": 151.701, "r_y3": 597.077, "coord_origin": "TOPLEFT"}, "text": "span", "orig": "span", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 618.467, "r_x1": 193.483, "r_y1": 618.467, "r_x2": 193.483, "r_y2": 609.67, 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454.555, "r_y2": 609.67, "r_x3": 242.209, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "to merge with both left and upper neighbor cells", "orig": "to merge with both left and upper neighbor cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 631.061, "r_x1": 181.994, "r_y1": 631.061, "r_x2": 181.994, "r_y2": 622.264, "r_x3": 151.701, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "\"NL\" -", "orig": "\"NL\" -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.317, "r_y0": 631.061, "r_x1": 221.462, "r_y1": 631.061, "r_x2": 221.462, "r_y2": 622.264, "r_x3": 185.317, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "new-line", "orig": "new-line", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.461, "r_y0": 631.061, "r_x1": 224.228, "r_y1": 631.061, "r_x2": 224.228, "r_y2": 622.264, "r_x3": 221.461, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.545, "r_y0": 631.061, "r_x1": 328.617, "r_y1": 631.061, "r_x2": 328.617, "r_y2": 622.264, "r_x3": 227.545, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "switch to the next row.", "orig": "switch to the next row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 228.223, "r_y1": 664.855, "r_x2": 228.223, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "conversion to HTML.", "orig": "conversion to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 143.48299999999995, "r": 480.596, "b": 295.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 372.508, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.595, "b": 441.6, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 261.801, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 521.706, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 460.544, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 556.777, "r_x1": 193.206, "r_y1": 556.777, "r_x2": 193.206, "r_y2": 547.98, "r_x3": 151.701, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "\"C\" cell -", "orig": "\"C\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.522, "r_y0": 556.777, "r_x1": 263.466, "r_y1": 556.777, "r_x2": 263.466, "r_y2": 547.98, "r_x3": 196.522, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "a new table cell", "orig": "a new table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.815, "r_y0": 556.777, "r_x1": 460.544, "r_y1": 556.777, "r_x2": 460.544, "r_y2": 547.98, "r_x3": 267.815, "r_y3": 547.98, "coord_origin": 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"text": "up-looking cell", "orig": "up-looking cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 259.892, "r_y0": 593.919, "r_x1": 262.659, "r_y1": 593.919, "r_x2": 262.659, "r_y2": 585.1220000000001, "r_x3": 259.892, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 266.295, "r_y0": 593.919, "r_x1": 480.588, "r_y1": 593.919, "r_x2": 480.588, "r_y2": 585.1220000000001, "r_x3": 266.295, "r_y3": 585.1220000000001, "coord_origin": "TOPLEFT"}, "text": "merging with the upper neighbor cell to create a", "orig": "merging with the upper neighbor cell to create a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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609.67, "coord_origin": "TOPLEFT"}, "text": "to merge with both left and upper neighbor cells", "orig": "to merge with both left and upper neighbor cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 328.617, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 631.061, "r_x1": 181.994, "r_y1": 631.061, "r_x2": 181.994, "r_y2": 622.264, "r_x3": 151.701, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "\"NL\" -", "orig": "\"NL\" -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.317, "r_y0": 631.061, "r_x1": 221.462, "r_y1": 631.061, "r_x2": 221.462, "r_y2": 622.264, "r_x3": 185.317, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "new-line", "orig": "new-line", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.461, "r_y0": 631.061, "r_x1": 224.228, "r_y1": 631.061, "r_x2": 224.228, "r_y2": 622.264, "r_x3": 221.461, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.545, "r_y0": 631.061, "r_x1": 328.617, "r_y1": 631.061, "r_x2": 328.617, "r_y2": 622.264, "r_x3": 227.545, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "switch to the next row.", "orig": "switch to the next row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 228.223, "r_y1": 664.855, "r_x2": 228.223, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "conversion to HTML.", "orig": "conversion to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 14, "page_no": 5, "cluster": {"id": 14, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8378868699073792, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6"}, {"label": "page_header", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8919177651405334, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 143.48299999999995, "r": 480.596, "b": 295.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 372.508, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.595, "b": 441.6, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 261.801, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 521.706, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 460.544, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 556.777, "r_x1": 193.206, "r_y1": 556.777, "r_x2": 193.206, "r_y2": 547.98, "r_x3": 151.701, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "\"C\" cell -", "orig": "\"C\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.522, "r_y0": 556.777, "r_x1": 263.466, "r_y1": 556.777, "r_x2": 263.466, "r_y2": 547.98, "r_x3": 196.522, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "a new table cell", "orig": "a new table cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 267.815, "r_y0": 556.777, "r_x1": 460.544, "r_y1": 556.777, "r_x2": 460.544, "r_y2": 547.98, "r_x3": 267.815, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "that either has or does not have cell content", "orig": "that either has or does not have cell content", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-\"C\" cell a new table cell that either has or does not have cell content"}, {"label": "list_item", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 140.993, "t": 560.563, "r": 480.594, "b": 581.325, "coord_origin": "TOPLEFT"}, "confidence": 0.9319931268692017, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 569.37, "r_x1": 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span"}, {"label": "list_item", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 140.993, "t": 609.66, "r": 454.555, "b": 618.467, "coord_origin": "TOPLEFT"}, "confidence": 0.9036387801170349, "cells": [{"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 618.467, "r_x1": 146.72, "r_y1": 618.467, "r_x2": 146.72, "r_y2": 609.66, "r_x3": 140.993, "r_y3": 609.66, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 618.467, "r_x1": 193.483, "r_y1": 618.467, "r_x2": 193.483, "r_y2": 609.67, "r_x3": 151.701, "r_y3": 609.67, "coord_origin": "TOPLEFT"}, "text": "\"X\" cell -", "orig": "\"X\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.799, 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"from_ocr": false}], "children": []}, "text": "-\"X\" cell cross cell , to merge with both left and upper neighbor cells"}, {"label": "list_item", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 140.993, "t": 622.254, "r": 328.617, "b": 631.061, "coord_origin": "TOPLEFT"}, "confidence": 0.8636871576309204, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 631.061, "r_x1": 146.72, "r_y1": 631.061, "r_x2": 146.72, "r_y2": 622.254, "r_x3": 140.993, "r_y3": 622.254, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 631.061, "r_x1": 181.994, "r_y1": 631.061, "r_x2": 181.994, "r_y2": 622.264, "r_x3": 151.701, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "\"NL\" -", "orig": "\"NL\" -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 185.317, "r_y0": 631.061, "r_x1": 221.462, "r_y1": 631.061, "r_x2": 221.462, "r_y2": 622.264, "r_x3": 185.317, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "new-line", "orig": "new-line", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.461, "r_y0": 631.061, "r_x1": 224.228, "r_y1": 631.061, "r_x2": 224.228, "r_y2": 622.264, "r_x3": 221.461, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": ",", "orig": ",", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.545, "r_y0": 631.061, "r_x1": 328.617, "r_y1": 631.061, "r_x2": 328.617, "r_y2": 622.264, "r_x3": 227.545, "r_y3": 622.264, "coord_origin": "TOPLEFT"}, "text": "switch to the next row.", "orig": "switch to the next row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-\"NL\" new-line , switch to the next row."}, {"label": "text", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9443338513374329, "cells": [{"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless", "orig": "A notable attribute of OTSL is that it has the capability of achieving lossless", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 228.223, "r_y1": 664.855, "r_x2": 228.223, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "conversion to HTML.", "orig": "conversion to HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."}], "body": [{"label": "text", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.595, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9633480906486511, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.595, "r_y1": 127.731, "r_x2": 480.595, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these", "orig": "generation. Implicitly, this also means that Im2Seq models need to learn these", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 364.625, "r_y1": 139.68600000000004, "r_x2": 364.625, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "complex syntax rules, simply to deliver valid output.", "orig": "complex syntax rules, simply to deliver valid output.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 143.48299999999995, "r": 480.596, "b": 295.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9856827855110168, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 152.27999999999997, "r_x1": 480.59, "r_y1": 152.27999999999997, "r_x2": 480.59, "r_y2": 143.48299999999995, "r_x3": 149.709, "r_y3": 143.48299999999995, "coord_origin": "TOPLEFT"}, "text": "In practice, we observe two major issues with prediction quality when train-", "orig": "In practice, we observe two major issues with prediction quality when train-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 164.235, "r_x1": 480.594, "r_y1": 164.235, "r_x2": 480.594, "r_y2": 155.438, "r_x3": 134.765, "r_y3": 155.438, "coord_origin": "TOPLEFT"}, "text": "ing Im2Seq models on HTML table structure generation from images. On the", "orig": "ing Im2Seq models on HTML table structure generation from images. On the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 176.19000000000005, "r_x1": 480.587, "r_y1": 176.19000000000005, "r_x2": 480.587, "r_y2": 167.39300000000003, "r_x3": 134.765, "r_y3": 167.39300000000003, "coord_origin": "TOPLEFT"}, "text": "one hand, we find that on large tables, the visual attention of the model often", "orig": "one hand, we find that on large tables, the visual attention of the model often", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 188.14499999999998, "r_x1": 480.595, "r_y1": 188.14499999999998, "r_x2": 480.595, "r_y2": 179.34799999999996, "r_x3": 134.765, "r_y3": 179.34799999999996, "coord_origin": "TOPLEFT"}, "text": "starts to drift and is not accurately moving forward cell by cell anymore. This", "orig": "starts to drift and is not accurately moving forward cell by cell anymore. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 200.10000000000002, "r_x1": 314.278, "r_y1": 200.10000000000002, "r_x2": 314.278, "r_y2": 191.303, "r_x3": 134.765, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "manifests itself in either in an increasing", "orig": "manifests itself in either in an increasing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 318.056, "r_y0": 200.10000000000002, "r_x1": 374.087, "r_y1": 200.10000000000002, "r_x2": 374.087, "r_y2": 191.303, "r_x3": 318.056, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "location drift", "orig": "location drift", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 378.809, "r_y0": 200.10000000000002, "r_x1": 480.586, "r_y1": 200.10000000000002, "r_x2": 480.586, "r_y2": 191.303, "r_x3": 378.809, "r_y3": 191.303, "coord_origin": "TOPLEFT"}, "text": "for proposed table-cells", "orig": "for proposed table-cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 212.05600000000004, "r_x1": 480.588, "r_y1": 212.05600000000004, "r_x2": 480.588, "r_y2": 203.25900000000001, "r_x3": 134.765, "r_y3": 203.25900000000001, "coord_origin": "TOPLEFT"}, "text": "in later rows on the same column or even complete loss of vertical alignment, as", "orig": "in later rows on the same column or even complete loss of vertical alignment, as", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 224.01099999999997, "r_x1": 480.596, "r_y1": 224.01099999999997, "r_x2": 480.596, "r_y2": 215.21400000000006, "r_x3": 134.765, "r_y3": 215.21400000000006, "coord_origin": "TOPLEFT"}, "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 235.966, "r_x1": 480.595, "r_y1": 235.966, "r_x2": 480.595, "r_y2": 227.16899999999998, "r_x3": 134.765, "r_y3": 227.16899999999998, "coord_origin": "TOPLEFT"}, "text": "but clearly undesired. On the other hand, we find many instances of predictions", "orig": "but clearly undesired. On the other hand, we find many instances of predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 247.92100000000005, "r_x1": 480.588, "r_y1": 247.92100000000005, "r_x2": 480.588, "r_y2": 239.12400000000002, "r_x3": 134.765, "r_y3": 239.12400000000002, "coord_origin": "TOPLEFT"}, "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 259.876, "r_x1": 480.593, "r_y1": 259.876, "r_x2": 480.593, "r_y2": 251.07899999999995, "r_x3": 134.765, "r_y3": 251.07899999999995, "coord_origin": "TOPLEFT"}, "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 271.831, "r_x1": 480.595, "r_y1": 271.831, "r_x2": 480.595, "r_y2": 263.034, "r_x3": 134.765, "r_y3": 263.034, "coord_origin": "TOPLEFT"}, "text": "impact the TSR model performance, since they reflect not only in the task of", "orig": "impact the TSR model performance, since they reflect not only in the task of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 283.787, "r_x1": 480.59, "r_y1": 283.787, "r_x2": 480.59, "r_y2": 274.99, "r_x3": 134.765, "r_y3": 274.99, "coord_origin": "TOPLEFT"}, "text": "pure structure recognition but also in the equally crucial recognition or matching", "orig": "pure structure recognition but also in the equally crucial recognition or matching", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.742, "r_x1": 223.573, "r_y1": 295.742, "r_x2": 223.573, "r_y2": 286.945, "r_x3": 134.765, "r_y3": 286.945, "coord_origin": "TOPLEFT"}, "text": "of table cell content.", "orig": "of table cell content.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."}, {"label": "section_header", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "section_header", "bbox": {"l": 134.765, "t": 320.631, "r": 372.508, "b": 331.199, "coord_origin": "TOPLEFT"}, "confidence": 0.957028865814209, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.199, "r_x1": 141.489, "r_y1": 331.199, "r_x2": 141.489, "r_y2": 320.631, "r_x3": 134.765, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "4", "orig": "4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.199, "r_x1": 372.508, "r_y1": 331.199, "r_x2": 372.508, "r_y2": 320.631, "r_x3": 154.938, "r_y3": 320.631, "coord_origin": "TOPLEFT"}, "text": "Optimised Table Structure Language", "orig": "Optimised Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4 Optimised Table Structure Language"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 349.117, "r": 480.595, "b": 441.6, "coord_origin": "TOPLEFT"}, "confidence": 0.9879323840141296, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 357.914, "r_x1": 480.591, "r_y1": 357.914, "r_x2": 480.591, "r_y2": 349.117, "r_x3": 134.765, "r_y3": 349.117, "coord_origin": "TOPLEFT"}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 369.869, "r_x1": 480.589, "r_y1": 369.869, "r_x2": 480.589, "r_y2": 361.072, "r_x3": 134.765, "r_y3": 361.072, "coord_origin": "TOPLEFT"}, "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "orig": "we propose here our Optimised Table Structure Language (OTSL). OTSL is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 381.825, "r_x1": 480.587, "r_y1": 381.825, "r_x2": 480.587, "r_y2": 373.028, "r_x3": 134.765, "r_y3": 373.028, "coord_origin": "TOPLEFT"}, "text": "designed to express table structure with a minimized vocabulary and a simple", "orig": "designed to express table structure with a minimized vocabulary and a simple", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 393.78, "r_x1": 146.993, "r_y1": 393.78, "r_x2": 146.993, "r_y2": 384.983, "r_x3": 134.765, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "set", "orig": "set", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.088, "r_y0": 393.78, "r_x1": 159.112, "r_y1": 393.78, "r_x2": 159.112, "r_y2": 384.983, "r_x3": 151.088, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 163.196, "r_y0": 393.78, "r_x1": 186.519, "r_y1": 393.78, "r_x2": 186.519, "r_y2": 384.983, "r_x3": 163.196, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "rules,", "orig": "rules,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 190.613, "r_y0": 393.78, "r_x1": 480.589, "r_y1": 393.78, "r_x2": 480.589, "r_y2": 384.983, "r_x3": 190.613, "r_y3": 384.983, "coord_origin": "TOPLEFT"}, "text": "which are both significantly reduced compared to HTML. At the", "orig": "which are both significantly reduced compared to HTML. At the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 405.735, "r_x1": 480.59, "r_y1": 405.735, "r_x2": 480.59, "r_y2": 396.938, "r_x3": 134.765, "r_y3": 396.938, "coord_origin": "TOPLEFT"}, "text": "same time, OTSL enables easy error detection and correction during sequence", "orig": "same time, OTSL enables easy error detection and correction during sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 417.69, "r_x1": 480.595, "r_y1": 417.69, "r_x2": 480.595, "r_y2": 408.893, "r_x3": 134.765, "r_y3": 408.893, "coord_origin": "TOPLEFT"}, "text": "generation. We further demonstrate how the compact structure representation", "orig": "generation. We further demonstrate how the compact structure representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 429.645, "r_x1": 480.589, "r_y1": 429.645, "r_x2": 480.589, "r_y2": 420.848, "r_x3": 134.765, "r_y3": 420.848, "coord_origin": "TOPLEFT"}, "text": "and minimized sequence length improves prediction accuracy and inference time", "orig": "and minimized sequence length improves prediction accuracy and inference time", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 441.6, "r_x1": 276.673, "r_y1": 441.6, "r_x2": 276.673, "r_y2": 432.803, "r_x3": 134.765, "r_y3": 432.803, "coord_origin": "TOPLEFT"}, "text": "in the TableFormer architecture.", "orig": "in the TableFormer architecture.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."}, {"label": "section_header", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 134.765, "t": 465.872, "r": 261.801, "b": 474.679, "coord_origin": "TOPLEFT"}, "confidence": 0.9559652209281921, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 474.679, "r_x1": 149.402, "r_y1": 474.679, "r_x2": 149.402, "r_y2": 465.872, "r_x3": 134.765, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "4.1", "orig": "4.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 474.679, "r_x1": 261.801, "r_y1": 474.679, "r_x2": 261.801, "r_y2": 465.872, "r_x3": 160.859, "r_y3": 465.872, "coord_origin": "TOPLEFT"}, "text": "Language Definition", "orig": "Language Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.1 Language Definition"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 488.998, "r": 480.589, "b": 521.706, "coord_origin": "TOPLEFT"}, "confidence": 0.9671263098716736, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 497.795, "r_x1": 480.589, "r_y1": 497.795, "r_x2": 480.589, "r_y2": 488.998, "r_x3": 134.765, "r_y3": 488.998, "coord_origin": "TOPLEFT"}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "orig": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 509.75, "r_x1": 480.587, "r_y1": 509.75, "r_x2": 480.587, "r_y2": 500.953, "r_x3": 134.765, "r_y3": 500.953, "coord_origin": "TOPLEFT"}, "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "orig": "only 5 tokens that directly describe a tabular structure based on an atomic 2D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 521.706, "r_x1": 154.713, "r_y1": 521.706, "r_x2": 154.713, "r_y2": 512.909, "r_x3": 134.765, "r_y3": 512.909, "coord_origin": "TOPLEFT"}, "text": "grid.", "orig": "grid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."}, {"label": "text", "id": 13, "page_no": 5, "cluster": {"id": 13, "label": "text", "bbox": {"l": 149.709, "t": 525.502, "r": 409.311, "b": 534.299, "coord_origin": "TOPLEFT"}, "confidence": 0.8555866479873657, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 534.299, "r_x1": 409.311, "r_y1": 534.299, "r_x2": 409.311, "r_y2": 525.502, "r_x3": 149.709, "r_y3": 525.502, "coord_origin": "TOPLEFT"}, "text": "The OTSL vocabulary is comprised of the following tokens:", "orig": "The OTSL vocabulary is comprised of the following tokens:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL vocabulary is comprised of the following tokens:"}, {"label": "list_item", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 140.993, "t": 547.97, "r": 460.544, "b": 556.777, "coord_origin": "TOPLEFT"}, "confidence": 0.9184334874153137, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.993, "r_y0": 556.777, "r_x1": 146.72, "r_y1": 556.777, "r_x2": 146.72, "r_y2": 547.97, "r_x3": 140.993, "r_y3": 547.97, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 556.777, "r_x1": 193.206, "r_y1": 556.777, "r_x2": 193.206, "r_y2": 547.98, "r_x3": 151.701, "r_y3": 547.98, "coord_origin": "TOPLEFT"}, "text": "\"C\" cell -", "orig": "\"C\" cell -", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.522, "r_y0": 556.777, "r_x1": 263.466, "r_y1": 556.777, "r_x2": 263.466, "r_y2": 547.98, "r_x3": 196.522, "r_y3": 547.98, "coord_origin": 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Lysak, et al."}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 403.805, "r_x1": 146.72, "r_y1": 403.805, "r_x2": 146.72, "r_y2": 395.008, "r_x3": 138.973, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 403.805, "r_x1": 223.304, "r_y1": 403.805, "r_x2": 223.304, "r_y2": 394.998, "r_x3": 151.701, "r_y3": 394.998, "coord_origin": "TOPLEFT"}, "text": "Cross cell rule", "orig": "Cross cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 223.307, "r_y0": 403.805, "r_x1": 226.074, "r_y1": 403.805, "r_x2": 226.074, "r_y2": 395.008, "r_x3": 223.307, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 415.764, "r_x1": 480.592, "r_y1": 415.764, "r_x2": 480.592, "r_y2": 406.967, "r_x3": 151.701, "r_y3": 406.967, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 427.719, "r_x1": 480.592, "r_y1": 427.719, "r_x2": 480.592, "r_y2": 418.922, "r_x3": 151.701, "r_y3": 418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, "r_y2": 442.836, "r_x3": 138.973, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 451.633, "r_x1": 221.323, "r_y1": 451.633, "r_x2": 221.323, "r_y2": 442.826, "r_x3": 151.701, "r_y3": 442.826, "coord_origin": "TOPLEFT"}, "text": "First row rule", "orig": "First row rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.327, "r_y0": 451.633, "r_x1": 224.094, "r_y1": 451.633, "r_x2": 224.094, "r_y2": 442.836, "r_x3": 221.327, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.411, "r_y0": 451.633, "r_x1": 474.59, "r_y1": 451.633, "r_x2": 474.59, "r_y2": 442.836, "r_x3": 227.411, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "orig": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 463.591, "r_x1": 146.72, "r_y1": 463.591, "r_x2": 146.72, "r_y2": 454.794, "r_x3": 138.973, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 463.591, "r_x1": 240.72, "r_y1": 463.591, "r_x2": 240.72, "r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.587, "b": 156.79100000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", "text_direction": 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160.859, "r_y3": 305.296, "coord_origin": "TOPLEFT"}, "text": "Language Syntax", "orig": "Language Syntax", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.248, "r": 363.796, "b": 334.045, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 334.045, "r_x1": 363.796, "r_y1": 334.045, "r_x2": 363.796, "r_y2": 325.248, "r_x3": 134.765, "r_y3": 325.248, "coord_origin": "TOPLEFT"}, "text": "The OTSL representation follows these syntax rules:", "orig": "The OTSL representation follows these syntax rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 138.973, "t": 347.171, "r": 480.589, "b": 367.934, "coord_origin": "TOPLEFT"}, "confidence": 0.9588112235069275, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 355.978, "r_x1": 146.72, "r_y1": 355.978, "r_x2": 146.72, "r_y2": 347.181, "r_x3": 138.973, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 355.978, "r_x1": 257.379, "r_y1": 355.978, "r_x2": 257.379, "r_y2": 347.171, "r_x3": 151.701, "r_y3": 347.171, "coord_origin": "TOPLEFT"}, "text": "Left-looking cell rule", "orig": "Left-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.383, "r_y0": 355.978, "r_x1": 260.15, "r_y1": 355.978, "r_x2": 260.15, "r_y2": 347.181, "r_x3": 257.383, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, 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418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 138.973, "t": 442.826, "r": 474.59, "b": 451.633, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, 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"r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "list_item", "bbox": {"l": 138.973, "t": 478.697, "r": 480.595, "b": 499.46, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 512.5930000000001, "r": 480.596, "b": 640.942, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7"}, {"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.587, "b": 156.79100000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.276, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 166.276, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "orig": "OTSL description of table structure: A - table example; B - graphical repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 171.412, "r_y1": 145.832, "r_x2": 171.412, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "sentation", "orig": "sentation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.546, "r_y0": 145.832, "r_x1": 182.968, "r_y1": 145.832, "r_x2": 182.968, "r_y2": 134.543, "r_x3": 175.546, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.093, "r_y0": 145.832, "r_x1": 207.058, "r_y1": 145.832, "r_x2": 207.058, "r_y2": 134.543, "r_x3": 187.093, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table", "orig": "table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.192, "r_y0": 145.832, "r_x1": 250.188, "r_y1": 145.832, "r_x2": 250.188, "r_y2": 134.543, "r_x3": 211.192, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "structure;", "orig": "structure;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 254.312, "r_y0": 145.832, "r_x1": 480.587, "r_y1": 145.832, "r_x2": 480.587, "r_y2": 134.543, "r_x3": 254.312, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "C - mapping structure on a grid; D - OTSL structure", "orig": "C - mapping structure on a grid; D - OTSL structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 156.79100000000005, "r_x1": 306.116, "r_y1": 156.79100000000005, "r_x2": 306.116, "r_y2": 145.50199999999995, "r_x3": 134.765, "r_y3": 145.50199999999995, "coord_origin": "TOPLEFT"}, "text": "encoding; E - explanation on cell encoding", "orig": "encoding; E - explanation on cell encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65028381347656, "t": 163.79708862304688, "r": 449.5505676269531, "b": 280.3409423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7868534922599792, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 374.493, "r_y0": 177.88099999999997, "r_x1": 428.861, "r_y1": 177.22199999999998, "r_x2": 428.861, "r_y2": 168.05399999999997, "r_x3": 374.493, "r_y3": 168.71299999999997, "coord_origin": "TOPLEFT"}, "text": "C L C L L", "orig": "C L C L L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 373.769, "r_y0": 202.21299999999997, "r_x1": 448.17, "r_y1": 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359.562, "r_y3": 168.37900000000002, "coord_origin": "TOPLEFT"}, "text": "D", "orig": "D", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 74, "label": "text", "bbox": {"l": 169.747, "t": 243.31100000000004, "r": 175.271, "b": 250.95100000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.747, "r_y0": 250.95100000000002, "r_x1": 175.271, "r_y1": 250.95100000000002, "r_x2": 175.271, "r_y2": 243.31100000000004, "r_x3": 169.747, "r_y3": 243.31100000000004, "coord_origin": "TOPLEFT"}, "text": "E", "orig": "E", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "section_header", "id": 9, "page_no": 6, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 134.765, "t": 305.296, "r": 246.652, "b": 314.103, "coord_origin": "TOPLEFT"}, "confidence": 0.9233023524284363, "cells": [{"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 314.103, "r_x1": 149.402, "r_y1": 314.103, "r_x2": 149.402, "r_y2": 305.296, "r_x3": 134.765, "r_y3": 305.296, "coord_origin": "TOPLEFT"}, "text": "4.2", "orig": "4.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 314.103, "r_x1": 246.652, "r_y1": 314.103, "r_x2": 246.652, "r_y2": 305.296, "r_x3": 160.859, "r_y3": 305.296, "coord_origin": "TOPLEFT"}, "text": "Language Syntax", "orig": "Language Syntax", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.2 Language Syntax"}, {"label": "text", "id": 10, "page_no": 6, "cluster": {"id": 10, "label": "text", "bbox": {"l": 134.765, "t": 325.248, "r": 363.796, "b": 334.045, "coord_origin": "TOPLEFT"}, "confidence": 0.9169168472290039, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 334.045, "r_x1": 363.796, "r_y1": 334.045, "r_x2": 363.796, "r_y2": 325.248, "r_x3": 134.765, "r_y3": 325.248, "coord_origin": "TOPLEFT"}, "text": "The OTSL representation follows these syntax rules:", "orig": "The OTSL representation follows these syntax rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The OTSL representation follows these syntax rules:"}, {"label": "list_item", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 138.973, "t": 347.171, "r": 480.589, "b": 367.934, "coord_origin": "TOPLEFT"}, "confidence": 0.9588112235069275, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 355.978, "r_x1": 146.72, "r_y1": 355.978, "r_x2": 146.72, "r_y2": 347.181, "r_x3": 138.973, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 355.978, "r_x1": 257.379, "r_y1": 355.978, "r_x2": 257.379, "r_y2": 347.171, "r_x3": 151.701, "r_y3": 347.171, "coord_origin": "TOPLEFT"}, "text": "Left-looking cell rule", "orig": "Left-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.383, "r_y0": 355.978, "r_x1": 260.15, "r_y1": 355.978, "r_x2": 260.15, "r_y2": 347.181, "r_x3": 257.383, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.184, "r_y0": 355.978, "r_x1": 480.589, "r_y1": 355.978, "r_x2": 480.589, "r_y2": 347.181, "r_x3": 264.184, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"L\" cell must be either", "orig": "The left neighbour of an \"L\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 367.934, "r_x1": 283.594, "r_y1": 367.934, "r_x2": 283.594, "r_y2": 359.137, "r_x3": 151.701, "r_y3": 359.137, "coord_origin": "TOPLEFT"}, "text": "another \"L\" cell or a \"C\" cell.", "orig": "another \"L\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.973, "t": 371.085, "r": 480.592, "b": 391.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 379.892, "r_x1": 146.72, "r_y1": 379.892, "r_x2": 146.72, "r_y2": 371.095, "r_x3": 138.973, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 379.892, "r_x1": 252.112, "r_y1": 379.892, "r_x2": 252.112, "r_y2": 371.085, "r_x3": 151.701, "r_y3": 371.085, "coord_origin": "TOPLEFT"}, "text": "Up-looking cell rule", "orig": "Up-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.112, "r_y0": 379.892, "r_x1": 254.879, "r_y1": 379.892, "r_x2": 254.879, "r_y2": 371.095, "r_x3": 252.112, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.764, "r_y0": 379.892, "r_x1": 480.592, "r_y1": 379.892, "r_x2": 480.592, "r_y2": 371.095, "r_x3": 258.764, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": "The upper neighbour of a \"U\" cell must be either", "orig": "The upper neighbour of a \"U\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 391.847, "r_x1": 284.839, "r_y1": 391.847, "r_x2": 284.839, "r_y2": 383.05, "r_x3": 151.701, "r_y3": 383.05, "coord_origin": "TOPLEFT"}, "text": "another \"U\" cell or a \"C\" cell.", "orig": "another \"U\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.973, "t": 394.998, "r": 226.074, "b": 403.805, "coord_origin": "TOPLEFT"}, "confidence": 0.6506174206733704, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 403.805, "r_x1": 146.72, "r_y1": 403.805, "r_x2": 146.72, "r_y2": 395.008, "r_x3": 138.973, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 403.805, "r_x1": 223.304, "r_y1": 403.805, "r_x2": 223.304, "r_y2": 394.998, "r_x3": 151.701, "r_y3": 394.998, "coord_origin": "TOPLEFT"}, "text": "Cross cell rule", "orig": "Cross cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 223.307, "r_y0": 403.805, "r_x1": 226.074, "r_y1": 403.805, "r_x2": 226.074, "r_y2": 395.008, "r_x3": 223.307, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.701, "t": 406.967, "r": 480.592, "b": 439.674, "coord_origin": "TOPLEFT"}, "confidence": 0.7247239947319031, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 415.764, "r_x1": 480.592, "r_y1": 415.764, "r_x2": 480.592, "r_y2": 406.967, "r_x3": 151.701, "r_y3": 406.967, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 427.719, "r_x1": 480.592, "r_y1": 427.719, "r_x2": 480.592, "r_y2": 418.922, "r_x3": 151.701, "r_y3": 418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.973, "t": 442.826, "r": 474.59, "b": 451.633, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, "r_y2": 442.836, "r_x3": 138.973, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 451.633, "r_x1": 221.323, "r_y1": 451.633, "r_x2": 221.323, "r_y2": 442.826, "r_x3": 151.701, "r_y3": 442.826, "coord_origin": "TOPLEFT"}, "text": "First row rule", "orig": "First row rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.327, "r_y0": 451.633, "r_x1": 224.094, "r_y1": 451.633, "r_x2": 224.094, "r_y2": 442.836, "r_x3": 221.327, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.411, "r_y0": 451.633, "r_x1": 474.59, "r_y1": 451.633, "r_x2": 474.59, "r_y2": 442.836, "r_x3": 227.411, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "orig": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.973, "t": 454.784, "r": 480.588, "b": 475.546, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 463.591, "r_x1": 146.72, "r_y1": 463.591, "r_x2": 146.72, "r_y2": 454.794, "r_x3": 138.973, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 463.591, "r_x1": 240.72, "r_y1": 463.591, "r_x2": 240.72, "r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.973, "t": 478.697, "r": 480.595, "b": 499.46, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 512.5930000000001, "r": 480.596, "b": 640.942, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "body": [{"label": "caption", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.587, "b": 156.79100000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9333080053329468, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 3.", "orig": "Fig. 3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 166.276, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 166.276, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "OTSL description of table structure: A - table example; B - graphical repre-", "orig": "OTSL description of table structure: A - table example; B - graphical repre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 171.412, "r_y1": 145.832, "r_x2": 171.412, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "sentation", "orig": "sentation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 175.546, "r_y0": 145.832, "r_x1": 182.968, "r_y1": 145.832, "r_x2": 182.968, "r_y2": 134.543, "r_x3": 175.546, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "of", "orig": "of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 187.093, "r_y0": 145.832, "r_x1": 207.058, "r_y1": 145.832, "r_x2": 207.058, "r_y2": 134.543, "r_x3": 187.093, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table", "orig": "table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 211.192, "r_y0": 145.832, "r_x1": 250.188, "r_y1": 145.832, "r_x2": 250.188, "r_y2": 134.543, "r_x3": 211.192, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "structure;", "orig": "structure;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 254.312, "r_y0": 145.832, "r_x1": 480.587, "r_y1": 145.832, "r_x2": 480.587, "r_y2": 134.543, "r_x3": 254.312, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "C - mapping structure on a grid; D - OTSL structure", "orig": "C - mapping structure on a grid; D - OTSL structure", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 156.79100000000005, "r_x1": 306.116, "r_y1": 156.79100000000005, "r_x2": 306.116, "r_y2": 145.50199999999995, "r_x3": 134.765, "r_y3": 145.50199999999995, "coord_origin": "TOPLEFT"}, "text": "encoding; E - explanation on cell encoding", "orig": "encoding; E - explanation on cell encoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding"}, {"label": "picture", "id": 12, "page_no": 6, "cluster": {"id": 12, "label": "picture", "bbox": {"l": 164.65028381347656, "t": 163.79708862304688, "r": 449.5505676269531, "b": 280.3409423828125, "coord_origin": "TOPLEFT"}, "confidence": 0.7868534922599792, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 374.493, "r_y0": 177.88099999999997, "r_x1": 428.861, "r_y1": 177.22199999999998, "r_x2": 428.861, "r_y2": 168.05399999999997, "r_x3": 374.493, "r_y3": 168.71299999999997, "coord_origin": "TOPLEFT"}, "text": "C L C L L", "orig": "C L C L L", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 373.769, "r_y0": 202.21299999999997, "r_x1": 448.17, "r_y1": 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"coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 355.978, "r_x1": 257.379, "r_y1": 355.978, "r_x2": 257.379, "r_y2": 347.171, "r_x3": 151.701, "r_y3": 347.171, "coord_origin": "TOPLEFT"}, "text": "Left-looking cell rule", "orig": "Left-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.383, "r_y0": 355.978, "r_x1": 260.15, "r_y1": 355.978, "r_x2": 260.15, "r_y2": 347.181, "r_x3": 257.383, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.184, "r_y0": 355.978, "r_x1": 480.589, "r_y1": 355.978, "r_x2": 480.589, "r_y2": 347.181, "r_x3": 264.184, "r_y3": 347.181, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"L\" cell must be either", "orig": "The left neighbour of an \"L\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 367.934, "r_x1": 283.594, "r_y1": 367.934, "r_x2": 283.594, "r_y2": 359.137, "r_x3": 151.701, "r_y3": 359.137, "coord_origin": "TOPLEFT"}, "text": "another \"L\" cell or a \"C\" cell.", "orig": "another \"L\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."}, {"label": "list_item", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 138.973, "t": 371.085, "r": 480.592, "b": 391.847, "coord_origin": "TOPLEFT"}, "confidence": 0.9585386514663696, "cells": [{"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 379.892, "r_x1": 146.72, "r_y1": 379.892, "r_x2": 146.72, "r_y2": 371.095, "r_x3": 138.973, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 379.892, "r_x1": 252.112, "r_y1": 379.892, "r_x2": 252.112, "r_y2": 371.085, "r_x3": 151.701, "r_y3": 371.085, "coord_origin": "TOPLEFT"}, "text": "Up-looking cell rule", "orig": "Up-looking cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 252.112, "r_y0": 379.892, "r_x1": 254.879, "r_y1": 379.892, "r_x2": 254.879, "r_y2": 371.095, "r_x3": 252.112, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 258.764, "r_y0": 379.892, "r_x1": 480.592, "r_y1": 379.892, "r_x2": 480.592, "r_y2": 371.095, "r_x3": 258.764, "r_y3": 371.095, "coord_origin": "TOPLEFT"}, "text": "The upper neighbour of a \"U\" cell must be either", "orig": "The upper neighbour of a \"U\" cell must be either", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 391.847, "r_x1": 284.839, "r_y1": 391.847, "r_x2": 284.839, "r_y2": 383.05, "r_x3": 151.701, "r_y3": 383.05, "coord_origin": "TOPLEFT"}, "text": "another \"U\" cell or a \"C\" cell.", "orig": "another \"U\" cell or a \"C\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."}, {"label": "section_header", "id": 14, "page_no": 6, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 138.973, "t": 394.998, "r": 226.074, "b": 403.805, "coord_origin": "TOPLEFT"}, "confidence": 0.6506174206733704, "cells": [{"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 403.805, "r_x1": 146.72, "r_y1": 403.805, "r_x2": 146.72, "r_y2": 395.008, "r_x3": 138.973, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 403.805, "r_x1": 223.304, "r_y1": 403.805, "r_x2": 223.304, "r_y2": 394.998, "r_x3": 151.701, "r_y3": 394.998, "coord_origin": "TOPLEFT"}, "text": "Cross cell rule", "orig": "Cross cell rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 223.307, "r_y0": 403.805, "r_x1": 226.074, "r_y1": 403.805, "r_x2": 226.074, "r_y2": 395.008, "r_x3": 223.307, "r_y3": 395.008, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Cross cell rule :"}, {"label": "list_item", "id": 13, "page_no": 6, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 151.701, "t": 406.967, "r": 480.592, "b": 439.674, "coord_origin": "TOPLEFT"}, "confidence": 0.7247239947319031, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 415.764, "r_x1": 480.592, "r_y1": 415.764, "r_x2": 480.592, "r_y2": 406.967, "r_x3": 151.701, "r_y3": 406.967, "coord_origin": "TOPLEFT"}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 427.719, "r_x1": 480.592, "r_y1": 427.719, "r_x2": 480.592, "r_y2": 418.922, "r_x3": 151.701, "r_y3": 418.922, "coord_origin": "TOPLEFT"}, "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 439.674, "r_x1": 214.397, "r_y1": 439.674, "r_x2": 214.397, "r_y2": 430.877, "r_x3": 151.701, "r_y3": 430.877, "coord_origin": "TOPLEFT"}, "text": "or an \"L\" cell.", "orig": "or an \"L\" cell.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."}, {"label": "list_item", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 138.973, "t": 442.826, "r": 474.59, "b": 451.633, "coord_origin": "TOPLEFT"}, "confidence": 0.9259927272796631, "cells": [{"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 451.633, "r_x1": 146.72, "r_y1": 451.633, "r_x2": 146.72, "r_y2": 442.836, "r_x3": 138.973, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "4.", "orig": "4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 451.633, "r_x1": 221.323, "r_y1": 451.633, "r_x2": 221.323, "r_y2": 442.826, "r_x3": 151.701, "r_y3": 442.826, "coord_origin": "TOPLEFT"}, "text": "First row rule", "orig": "First row rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 221.327, "r_y0": 451.633, "r_x1": 224.094, "r_y1": 451.633, "r_x2": 224.094, "r_y2": 442.836, "r_x3": 221.327, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 227.411, "r_y0": 451.633, "r_x1": 474.59, "r_y1": 451.633, "r_x2": 474.59, "r_y2": 442.836, "r_x3": 227.411, "r_y3": 442.836, "coord_origin": "TOPLEFT"}, "text": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "orig": "Only \"L\" cells and \"C\" cells are allowed in the first row.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."}, {"label": "list_item", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 138.973, "t": 454.784, "r": 480.588, "b": 475.546, "coord_origin": "TOPLEFT"}, "confidence": 0.9420599937438965, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 463.591, "r_x1": 146.72, "r_y1": 463.591, "r_x2": 146.72, "r_y2": 454.794, "r_x3": 138.973, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "5.", "orig": "5.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 463.591, "r_x1": 240.72, "r_y1": 463.591, "r_x2": 240.72, "r_y2": 454.784, "r_x3": 151.701, "r_y3": 454.784, "coord_origin": "TOPLEFT"}, "text": "First column rule", "orig": "First column rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 240.716, "r_y0": 463.591, "r_x1": 243.483, "r_y1": 463.591, "r_x2": 243.483, "r_y2": 454.794, "r_x3": 240.716, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": ":", "orig": ":", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.458, "r_y0": 463.591, "r_x1": 480.588, "r_y1": 463.591, "r_x2": 480.588, "r_y2": 454.794, "r_x3": 247.458, "r_y3": 454.794, "coord_origin": "TOPLEFT"}, "text": "Only \"U\" cells and \"C\" cells are allowed in the first", "orig": "Only \"U\" cells and \"C\" cells are allowed in the first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 475.546, "r_x1": 186.007, "r_y1": 475.546, "r_x2": 186.007, "r_y2": 466.749, "r_x3": 151.701, "r_y3": 466.749, "coord_origin": "TOPLEFT"}, "text": "column.", "orig": "column.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5. First column rule : Only \"U\" cells and \"C\" cells are allowed in the first column."}, {"label": "list_item", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "list_item", "bbox": {"l": 138.973, "t": 478.697, "r": 480.595, "b": 499.46, "coord_origin": "TOPLEFT"}, "confidence": 0.9617829918861389, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.973, "r_y0": 487.504, "r_x1": 146.72, "r_y1": 487.504, "r_x2": 146.72, "r_y2": 478.707, "r_x3": 138.973, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": "6.", "orig": "6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 487.504, "r_x1": 235.158, "r_y1": 487.504, "r_x2": 235.158, "r_y2": 478.697, "r_x3": 151.701, "r_y3": 478.697, "coord_origin": "TOPLEFT"}, "text": "Rectangular rule", "orig": "Rectangular rule", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.157, "r_y0": 487.504, "r_x1": 480.595, "r_y1": 487.504, "r_x2": 480.595, "r_y2": 478.707, "r_x3": 235.157, "r_y3": 478.707, "coord_origin": "TOPLEFT"}, "text": ": The table representation is always rectangular - all rows", "orig": ": The table representation is always rectangular - all rows", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.701, "r_y0": 499.46, "r_x1": 448.042, "r_y1": 499.46, "r_x2": 448.042, "r_y2": 490.663, "r_x3": 151.701, "r_y3": 490.663, "coord_origin": "TOPLEFT"}, "text": "must have an equal number of tokens, terminated with \"NL\" token.", "orig": "must have an equal number of tokens, terminated with \"NL\" token.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with \"NL\" token."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 512.5930000000001, "r": 480.596, "b": 640.942, "coord_origin": "TOPLEFT"}, "confidence": 0.9785562753677368, "cells": [{"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 521.39, "r_x1": 480.596, "r_y1": 521.39, "r_x2": 480.596, "r_y2": 512.5930000000001, "r_x3": 149.709, "r_y3": 512.5930000000001, "coord_origin": "TOPLEFT"}, "text": "The application of these rules gives OTSL a set of unique properties. First", "orig": "The application of these rules gives OTSL a set of unique properties. First", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.345, "r_x1": 480.591, "r_y1": 533.345, "r_x2": 480.591, "r_y2": 524.548, "r_x3": 134.765, "r_y3": 524.548, "coord_origin": "TOPLEFT"}, "text": "of all, the OTSL enforces a strictly rectangular structure representation, where", "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.3, "r_x1": 480.595, "r_y1": 545.3, "r_x2": 480.595, "r_y2": 536.5029999999999, "r_x3": 134.765, "r_y3": 536.5029999999999, "coord_origin": "TOPLEFT"}, "text": "every new-line token starts a new row. As a consequence, all rows and all columns", "orig": "every new-line token starts a new row. As a consequence, all rows and all columns", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.256, "r_x1": 480.589, "r_y1": 557.256, "r_x2": 480.589, "r_y2": 548.4590000000001, "r_x3": 134.765, "r_y3": 548.4590000000001, "coord_origin": "TOPLEFT"}, "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "orig": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.211, "r_x1": 480.594, "r_y1": 569.211, "r_x2": 480.594, "r_y2": 560.414, "r_x3": 134.765, "r_y3": 560.414, "coord_origin": "TOPLEFT"}, "text": "OTSL representation is unambiguous: Every table structure is represented in one", "orig": "OTSL representation is unambiguous: Every table structure is represented in one", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.1659999999999, "r_x1": 480.587, "r_y1": 581.1659999999999, "r_x2": 480.587, "r_y2": 572.369, "r_x3": 134.765, "r_y3": 572.369, "coord_origin": "TOPLEFT"}, "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "orig": "way. In this representation every table cell corresponds to a \"C\"-cell token, which", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.121, "r_x1": 480.592, "r_y1": 593.121, "r_x2": 480.592, "r_y2": 584.3240000000001, "r_x3": 134.765, "r_y3": 584.3240000000001, "coord_origin": "TOPLEFT"}, "text": "in case of spans is always located in the top-left corner of the table cell definition.", "orig": "in case of spans is always located in the top-left corner of the table cell definition.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.076, "r_x1": 480.592, "r_y1": 605.076, "r_x2": 480.592, "r_y2": 596.279, "r_x3": 134.765, "r_y3": 596.279, "coord_origin": "TOPLEFT"}, "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "orig": "Third, OTSL syntax rules are only backward-looking. As a consequence, every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.032, "r_x1": 480.594, "r_y1": 617.032, "r_x2": 480.594, "r_y2": 608.235, "r_x3": 134.765, "r_y3": 608.235, "coord_origin": "TOPLEFT"}, "text": "predicted token can be validated straight during sequence generation by looking", "orig": "predicted token can be validated straight during sequence generation by looking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 628.987, "r_x1": 480.591, "r_y1": 628.987, "r_x2": 480.591, "r_y2": 620.19, "r_x3": 134.765, "r_y3": 620.19, "coord_origin": "TOPLEFT"}, "text": "at the previously predicted sequence. As such, OTSL can guarantee that every", "orig": "at the previously predicted sequence. As such, OTSL can guarantee that every", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.942, "r_x1": 311.198, "r_y1": 640.942, "r_x2": 311.198, "r_y2": 632.145, "r_x3": 134.765, "r_y3": 632.145, "coord_origin": "TOPLEFT"}, "text": "predicted sequence is syntactically valid.", "orig": "predicted sequence is syntactically valid.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a \"C\"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid."}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 644.1030000000001, "r": 480.593, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9590610861778259, "cells": [{"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 149.709, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "These characteristics can be easily learned by sequence generator networks,", "orig": "These characteristics can be easily learned by sequence generator networks,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 480.593, "r_y1": 664.855, "r_x2": 480.593, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "as we demonstrate further below. We find strong indications that this pattern", "orig": "as we demonstrate further below. We find strong indications that this pattern", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern"}], "headers": [{"label": "page_header", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.932651162147522, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 11, "page_no": 6, "cluster": {"id": 11, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8749732375144958, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "7", "orig": "7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, 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Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. 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The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.589, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55700000000002, "r": 319.347, "b": 170.36400000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28200000000004, "r": 480.596, "b": 298.676, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.035, "b": 331.732, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.25, "r": 480.595, "b": 451.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, "r_y0": 403.867, "r_x1": 480.595, "r_y1": 403.867, "r_x2": 480.595, "r_y2": 395.07, "r_x3": 195.902, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. Secondly we pick the best hyper-parameters", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.822, "r_x1": 172.59, "r_y1": 415.822, "r_x2": 172.59, "r_y2": 407.025, "r_x3": 134.765, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "found in", "orig": "found in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 177.492, "r_y0": 415.822, "r_x1": 191.325, "r_y1": 415.822, "r_x2": 191.325, "r_y2": 407.025, "r_x3": 177.492, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.226, "r_y0": 415.822, "r_x1": 213.463, "r_y1": 415.822, "r_x2": 213.463, "r_y2": 407.025, "r_x3": 196.226, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "first", "orig": "first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 218.364, "r_y0": 415.822, "r_x1": 236.125, "r_y1": 415.822, "r_x2": 236.125, "r_y2": 407.025, "r_x3": 218.364, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "step", "orig": "step", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.027, "r_y0": 415.822, "r_x1": 297.661, "r_y1": 415.822, "r_x2": 297.661, "r_y2": 407.025, "r_x3": 241.027, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "and evaluate", "orig": "and evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 302.563, "r_y0": 415.822, "r_x1": 480.593, "r_y1": 415.822, "r_x2": 480.593, "r_y2": 407.025, "r_x3": 302.563, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "how OTSL impacts the performance of", "orig": "how OTSL impacts the performance of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.778, "r_x1": 377.321, "r_y1": 427.778, "r_x2": 377.321, "r_y2": 418.981, "r_x3": 134.765, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "TableFormer after training on other publicly available", "orig": "TableFormer after training on other publicly available", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.874, "r_y0": 427.778, "r_x1": 421.951, "r_y1": 427.778, "r_x2": 421.951, "r_y2": 418.981, "r_x3": 381.874, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "data sets", "orig": "data sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.504, "r_y0": 427.778, "r_x1": 480.595, "r_y1": 427.778, "r_x2": 480.595, "r_y2": 418.981, "r_x3": 426.504, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "(FinTabNet,", "orig": "(FinTabNet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.733, "r_x1": 480.592, "r_y1": 439.733, "r_x2": 480.592, "r_y2": 430.936, "r_x3": 134.765, "r_y3": 430.936, "coord_origin": "TOPLEFT"}, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 451.688, "r_x1": 479.303, "r_y1": 451.688, "r_x2": 479.303, "r_y2": 442.891, "r_x3": 134.765, "r_y3": 442.891, "coord_origin": "TOPLEFT"}, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "orig": "verted into OTSL format for this purpose, and will be made publicly available.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 482.433, "r": 480.591, "b": 504.681, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 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"rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "orig": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "orig": "section Over Union (IOU) threshold for the bounding-box predictions of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 155.846, "r_y1": 664.855, "r_x2": 155.846, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.442, "r_y0": 664.855, "r_x1": 480.59, "r_y1": 664.855, "r_x2": 480.59, "r_y2": 656.058, "r_x3": 159.442, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "The predicted OTSL structures were converted back to HTML format in", "orig": "The predicted OTSL structures were converted back to HTML format in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8301323056221008, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8631826043128967, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}, {"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.589, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55700000000002, "r": 319.347, "b": 170.36400000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28200000000004, "r": 480.596, "b": 298.676, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.035, "b": 331.732, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.25, "r": 480.595, "b": 451.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, "r_y0": 403.867, "r_x1": 480.595, "r_y1": 403.867, "r_x2": 480.595, "r_y2": 395.07, "r_x3": 195.902, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. Secondly we pick the best hyper-parameters", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.822, "r_x1": 172.59, "r_y1": 415.822, "r_x2": 172.59, "r_y2": 407.025, "r_x3": 134.765, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "found in", "orig": "found in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 177.492, "r_y0": 415.822, "r_x1": 191.325, "r_y1": 415.822, "r_x2": 191.325, "r_y2": 407.025, "r_x3": 177.492, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.226, "r_y0": 415.822, "r_x1": 213.463, "r_y1": 415.822, "r_x2": 213.463, "r_y2": 407.025, "r_x3": 196.226, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "first", "orig": "first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 218.364, "r_y0": 415.822, "r_x1": 236.125, "r_y1": 415.822, "r_x2": 236.125, "r_y2": 407.025, "r_x3": 218.364, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "step", "orig": "step", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.027, "r_y0": 415.822, "r_x1": 297.661, "r_y1": 415.822, "r_x2": 297.661, "r_y2": 407.025, "r_x3": 241.027, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "and evaluate", "orig": "and evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 302.563, "r_y0": 415.822, "r_x1": 480.593, "r_y1": 415.822, "r_x2": 480.593, "r_y2": 407.025, "r_x3": 302.563, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "how OTSL impacts the performance of", "orig": "how OTSL impacts the performance of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.778, "r_x1": 377.321, "r_y1": 427.778, "r_x2": 377.321, "r_y2": 418.981, "r_x3": 134.765, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "TableFormer after training on other publicly available", "orig": "TableFormer after training on other publicly available", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.874, "r_y0": 427.778, "r_x1": 421.951, "r_y1": 427.778, "r_x2": 421.951, "r_y2": 418.981, "r_x3": 381.874, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "data sets", "orig": "data sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.504, "r_y0": 427.778, "r_x1": 480.595, "r_y1": 427.778, "r_x2": 480.595, "r_y2": 418.981, "r_x3": 426.504, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "(FinTabNet,", "orig": "(FinTabNet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.733, "r_x1": 480.592, "r_y1": 439.733, "r_x2": 480.592, "r_y2": 430.936, "r_x3": 134.765, "r_y3": 430.936, "coord_origin": "TOPLEFT"}, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 451.688, "r_x1": 479.303, "r_y1": 451.688, "r_x2": 479.303, "r_y2": 442.891, "r_x3": 134.765, "r_y3": 442.891, "coord_origin": "TOPLEFT"}, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "orig": "verted into OTSL format for this purpose, and will be made publicly available.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 482.433, "r": 480.591, "b": 504.681, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 484.648, "coord_origin": "TOPLEFT"}, "text": "Fig. 4.", "orig": "Fig. 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.196, "r_y0": 493.722, "r_x1": 480.591, "r_y1": 493.722, "r_x2": 480.591, "r_y2": 482.433, "r_x3": 165.196, "r_y3": 482.433, "coord_origin": "TOPLEFT"}, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "orig": "Architecture sketch of the TableFormer model, which is a representative for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.681, "r_x1": 206.702, "r_y1": 504.681, "r_x2": 206.702, "r_y2": 493.392, "r_x3": 134.765, "r_y3": 493.392, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approach.", "orig": "Im2Seq approach.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.70968627929688, "t": 508.0638427734375, "r": 472.73382568359375, "b": 593.6771850585938, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.3, "r_y0": 543.091, "r_x1": 149.407, "r_y1": 543.091, "r_x2": 149.407, "r_y2": 540.762, "r_x3": 147.3, "r_y3": 540.762, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.809, "r_y0": 543.091, "r_x1": 155.721, "r_y1": 543.091, "r_x2": 155.721, "r_y2": 540.762, "r_x3": 150.809, "r_y3": 540.762, "coord_origin": "TOPLEFT"}, "text": "Item", "orig": "Item", "text_direction": "left_to_right", 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"r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.589, "r_y1": 640.9449999999999, "r_x2": 480.589, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "orig": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.592, "r_y1": 652.9, "r_x2": 480.592, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "orig": "section Over Union (IOU) threshold for the bounding-box predictions of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 155.846, "r_y1": 664.855, "r_x2": 155.846, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.442, "r_y0": 664.855, "r_x1": 480.59, "r_y1": 664.855, "r_x2": 480.59, "r_y2": 656.058, "r_x3": 159.442, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "The predicted OTSL structures were converted back to HTML format in", "orig": "The predicted OTSL structures were converted back to HTML format in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "body": [{"label": "text", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.589, "b": 139.68600000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9464744925498962, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.589, "r_y1": 127.731, "r_x2": 480.589, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "reduces significantly the column drift seen in the HTML based models (see Fig-", "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 163.564, "r_y1": 139.68600000000004, "r_x2": 163.564, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "ure 5).", "orig": "ure 5).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "reduces significantly the column drift seen in the HTML based models (see Figure 5)."}, {"label": "section_header", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 134.765, "t": 161.55700000000002, "r": 319.347, "b": 170.36400000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.9549514651298523, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 170.36400000000003, "r_x1": 149.402, "r_y1": 170.36400000000003, "r_x2": 149.402, "r_y2": 161.55700000000002, "r_x3": 134.765, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "4.3", "orig": "4.3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 170.36400000000003, "r_x1": 319.347, "r_y1": 170.36400000000003, "r_x2": 319.347, "r_y2": 161.55700000000002, "r_x3": 160.859, "r_y3": 161.55700000000002, "coord_origin": "TOPLEFT"}, "text": "Error-detection and -mitigation", "orig": "Error-detection and -mitigation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4.3 Error-detection and -mitigation"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 182.28200000000004, "r": 480.596, "b": 298.676, "coord_origin": "TOPLEFT"}, "confidence": 0.9879790544509888, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.07899999999995, "r_x1": 480.596, "r_y1": 191.07899999999995, "r_x2": 480.596, "r_y2": 182.28200000000004, "r_x3": 134.765, "r_y3": 182.28200000000004, "coord_origin": "TOPLEFT"}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished", "orig": "The design of OTSL allows to validate a table structure easily on an unfinished", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.03499999999997, "r_x1": 480.595, "r_y1": 203.03499999999997, "r_x2": 480.595, "r_y2": 194.23800000000006, "r_x3": 134.765, "r_y3": 194.23800000000006, "coord_origin": "TOPLEFT"}, "text": "sequence. The detection of an invalid sequence token is a clear indication of a", "orig": "sequence. The detection of an invalid sequence token is a clear indication of a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 214.99, "r_x1": 480.587, "r_y1": 214.99, "r_x2": 480.587, "r_y2": 206.19299999999998, "r_x3": 134.765, "r_y3": 206.19299999999998, "coord_origin": "TOPLEFT"}, "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 226.94500000000005, "r_x1": 480.592, "r_y1": 226.94500000000005, "r_x2": 480.592, "r_y2": 218.14800000000002, "r_x3": 134.765, "r_y3": 218.14800000000002, "coord_origin": "TOPLEFT"}, "text": "diction correctness. Different heuristics can be used to correct token errors in", "orig": "diction correctness. Different heuristics can be used to correct token errors in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 238.89999999999998, "r_x1": 480.588, "r_y1": 238.89999999999998, "r_x2": 480.588, "r_y2": 230.10299999999995, "r_x3": 134.765, "r_y3": 230.10299999999995, "coord_origin": "TOPLEFT"}, "text": "an invalid sequence and thus increase the chances for accurate predictions. Such", "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 250.85500000000002, "r_x1": 480.587, "r_y1": 250.85500000000002, "r_x2": 480.587, "r_y2": 242.058, "r_x3": 134.765, "r_y3": 242.058, "coord_origin": "TOPLEFT"}, "text": "heuristics can be applied either after the prediction of each token, or at the end", "orig": "heuristics can be applied either after the prediction of each token, or at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 262.80999999999995, "r_x1": 480.594, "r_y1": 262.80999999999995, "r_x2": 480.594, "r_y2": 254.01300000000003, "r_x3": 134.765, "r_y3": 254.01300000000003, "coord_origin": "TOPLEFT"}, "text": "on the entire predicted sequence. For example a simple heuristic which can cor-", "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 274.76599999999996, "r_x1": 480.591, "r_y1": 274.76599999999996, "r_x2": 480.591, "r_y2": 265.96900000000005, "r_x3": 134.765, "r_y3": 265.96900000000005, "coord_origin": "TOPLEFT"}, "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 286.721, "r_x1": 480.596, "r_y1": 286.721, "r_x2": 480.596, "r_y2": 277.924, "r_x3": 134.765, "r_y3": 277.924, "coord_origin": "TOPLEFT"}, "text": "highest prediction confidence invalidates the predicted sequence, and replace it", "orig": "highest prediction confidence invalidates the predicted sequence, and replace it", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 298.676, "r_x1": 469.404, "r_y1": 298.676, "r_x2": 469.404, "r_y2": 289.879, "r_x3": 134.765, "r_y3": 289.879, "coord_origin": "TOPLEFT"}, "text": "by the token with the next highest confidence until OTSL rules are satisfied.", "orig": "by the token with the next highest confidence until OTSL rules are satisfied.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied."}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 134.765, "t": 321.164, "r": 229.035, "b": 331.732, "coord_origin": "TOPLEFT"}, "confidence": 0.95152747631073, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 331.732, "r_x1": 141.489, "r_y1": 331.732, "r_x2": 141.489, "r_y2": 321.164, "r_x3": 134.765, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "5", "orig": "5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 331.732, "r_x1": 229.035, "r_y1": 331.732, "r_x2": 229.035, "r_y2": 321.164, "r_x3": 154.938, "r_y3": 321.164, "coord_origin": "TOPLEFT"}, "text": "Experiments", "orig": "Experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5 Experiments"}, {"label": "text", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 347.25, "r": 480.595, "b": 451.688, "coord_origin": "TOPLEFT"}, "confidence": 0.9877985119819641, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 356.047, "r_x1": 480.594, "r_y1": 356.047, "r_x2": 480.594, "r_y2": 347.25, "r_x3": 134.765, "r_y3": 347.25, "coord_origin": "TOPLEFT"}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 368.002, "r_x1": 480.595, "r_y1": 368.002, "r_x2": 480.595, "r_y2": 359.205, "r_x3": 134.765, "r_y3": 359.205, "coord_origin": "TOPLEFT"}, "text": "conducted a series of experiments based on the TableFormer model (Figure 4)", "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 379.957, "r_x1": 480.588, "r_y1": 379.957, "r_x2": 480.588, "r_y2": 371.16, "r_x3": 134.765, "r_y3": 371.16, "coord_origin": "TOPLEFT"}, "text": "with two objectives: Firstly we evaluate the prediction quality and performance", "orig": "with two objectives: Firstly we evaluate the prediction quality and performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 391.912, "r_x1": 480.588, "r_y1": 391.912, "r_x2": 480.588, "r_y2": 383.115, "r_x3": 134.765, "r_y3": 383.115, "coord_origin": "TOPLEFT"}, "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 403.867, "r_x1": 148.598, "r_y1": 403.867, "r_x2": 148.598, "r_y2": 395.07, "r_x3": 134.765, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.627, "r_y0": 403.867, "r_x1": 191.847, "r_y1": 403.867, "r_x2": 191.847, "r_y2": 395.07, "r_x3": 151.627, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "canonical", "orig": "canonical", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 195.902, "r_y0": 403.867, "r_x1": 480.595, "r_y1": 403.867, "r_x2": 480.595, "r_y2": 395.07, "r_x3": 195.902, "r_y3": 395.07, "coord_origin": "TOPLEFT"}, "text": "PubTabNet data set. Secondly we pick the best hyper-parameters", "orig": "PubTabNet data set. Secondly we pick the best hyper-parameters", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 415.822, "r_x1": 172.59, "r_y1": 415.822, "r_x2": 172.59, "r_y2": 407.025, "r_x3": 134.765, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "found in", "orig": "found in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 177.492, "r_y0": 415.822, "r_x1": 191.325, "r_y1": 415.822, "r_x2": 191.325, "r_y2": 407.025, "r_x3": 177.492, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 196.226, "r_y0": 415.822, "r_x1": 213.463, "r_y1": 415.822, "r_x2": 213.463, "r_y2": 407.025, "r_x3": 196.226, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "first", "orig": "first", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 218.364, "r_y0": 415.822, "r_x1": 236.125, "r_y1": 415.822, "r_x2": 236.125, "r_y2": 407.025, "r_x3": 218.364, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "step", "orig": "step", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 241.027, "r_y0": 415.822, "r_x1": 297.661, "r_y1": 415.822, "r_x2": 297.661, "r_y2": 407.025, "r_x3": 241.027, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "and evaluate", "orig": "and evaluate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 302.563, "r_y0": 415.822, "r_x1": 480.593, "r_y1": 415.822, "r_x2": 480.593, "r_y2": 407.025, "r_x3": 302.563, "r_y3": 407.025, "coord_origin": "TOPLEFT"}, "text": "how OTSL impacts the performance of", "orig": "how OTSL impacts the performance of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 427.778, "r_x1": 377.321, "r_y1": 427.778, "r_x2": 377.321, "r_y2": 418.981, "r_x3": 134.765, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "TableFormer after training on other publicly available", "orig": "TableFormer after training on other publicly available", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.874, "r_y0": 427.778, "r_x1": 421.951, "r_y1": 427.778, "r_x2": 421.951, "r_y2": 418.981, "r_x3": 381.874, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "data sets", "orig": "data sets", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.504, "r_y0": 427.778, "r_x1": 480.595, "r_y1": 427.778, "r_x2": 480.595, "r_y2": 418.981, "r_x3": 426.504, "r_y3": 418.981, "coord_origin": "TOPLEFT"}, "text": "(FinTabNet,", "orig": "(FinTabNet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 439.733, "r_x1": 480.592, "r_y1": 439.733, "r_x2": 480.592, "r_y2": 430.936, "r_x3": 134.765, "r_y3": 430.936, "coord_origin": "TOPLEFT"}, "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 451.688, "r_x1": 479.303, "r_y1": 451.688, "r_x2": 479.303, "r_y2": 442.891, "r_x3": 134.765, "r_y3": 442.891, "coord_origin": "TOPLEFT"}, "text": "verted into OTSL format for this purpose, and will be made publicly available.", "orig": "verted into OTSL format for this purpose, and will be made publicly available.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."}, {"label": "caption", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 482.433, "r": 480.591, "b": 504.681, "coord_origin": "TOPLEFT"}, "confidence": 0.9297956228256226, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 492.574, "r_x1": 162.644, "r_y1": 492.574, "r_x2": 162.644, "r_y2": 484.648, "r_x3": 134.765, "r_y3": 484.648, "coord_origin": "TOPLEFT"}, "text": "Fig. 4.", "orig": "Fig. 4.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.196, "r_y0": 493.722, "r_x1": 480.591, "r_y1": 493.722, "r_x2": 480.591, "r_y2": 482.433, "r_x3": 165.196, "r_y3": 482.433, "coord_origin": "TOPLEFT"}, "text": "Architecture sketch of the TableFormer model, which is a representative for the", "orig": "Architecture sketch of the TableFormer model, which is a representative for the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.681, "r_x1": 206.702, "r_y1": 504.681, "r_x2": 206.702, "r_y2": 493.392, "r_x3": 134.765, "r_y3": 493.392, "coord_origin": "TOPLEFT"}, "text": "Im2Seq approach.", "orig": "Im2Seq approach.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."}, {"label": "picture", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "picture", "bbox": {"l": 140.70968627929688, "t": 508.0638427734375, "r": 472.73382568359375, "b": 593.6771850585938, "coord_origin": "TOPLEFT"}, "confidence": 0.9303396344184875, "cells": [{"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 147.3, "r_y0": 543.091, "r_x1": 149.407, "r_y1": 543.091, "r_x2": 149.407, "r_y2": 540.762, "r_x3": 147.3, "r_y3": 540.762, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.809, "r_y0": 543.091, "r_x1": 155.721, "r_y1": 543.091, "r_x2": 155.721, "r_y2": 540.762, "r_x3": 150.809, "r_y3": 540.762, "coord_origin": "TOPLEFT"}, "text": "Item", "orig": "Item", "text_direction": "left_to_right", 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"r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "section Over Union (IOU) threshold for the bounding-box predictions of table", "orig": "section Over Union (IOU) threshold for the bounding-box predictions of table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 155.846, "r_y1": 664.855, "r_x2": 155.846, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "cells.", "orig": "cells.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 159.442, "r_y0": 664.855, "r_x1": 480.59, "r_y1": 664.855, "r_x2": 480.59, "r_y2": 656.058, "r_x3": 159.442, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "The predicted OTSL structures were converted back to HTML format in", "orig": "The predicted OTSL structures were converted back to HTML format in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in"}], "headers": [{"label": "page_header", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 139.372, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8301323056221008, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 139.372, "r_y1": 102.78200000000004, "r_x2": 139.372, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "8"}, {"label": "page_header", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 167.813, "t": 91.49300000000005, "r": 231.722, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8631826043128967, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.813, "r_y0": 102.78200000000004, "r_x1": 231.722, "r_y1": 102.78200000000004, "r_x2": 231.722, "r_y2": 91.49300000000005, "r_x3": 167.813, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378753662109, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8857625722885132, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 151.64200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 318.448, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.593, "b": 254.159, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 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"r_x2": 300.652, "r_y2": 272.85699999999997, "r_x3": 275.957, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 284.146, "r_x1": 321.763, "r_y1": 284.146, "r_x2": 321.763, "r_y2": 272.85699999999997, "r_x3": 306.92, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.03, "r_y0": 284.146, "r_x1": 419.24, "r_y1": 284.146, "r_x2": 419.24, "r_y2": 272.85699999999997, "r_x3": 328.03, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 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"confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.105, "r_x1": 480.595, "r_y1": 295.105, "r_x2": 480.595, "r_y2": 283.816, "r_x3": 134.765, "r_y3": 283.816, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. 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"text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 164.921, "r_y1": 545.304, "r_x2": 164.921, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.193, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {"0": {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 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Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 318.448, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.593, "b": 254.159, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 245.36199999999997, "r_x3": 149.149, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. 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Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 306.064, "r_x1": 480.595, "r_y1": 306.064, "r_x2": 480.595, "r_y2": 294.775, "r_x3": 134.765, "r_y3": 294.775, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 317.023, "r_x1": 163.504, "r_y1": 317.023, "r_x2": 163.504, "r_y2": 305.734, "r_x3": 134.765, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 317.023, "r_x1": 443.264, "r_y1": 317.023, "r_x2": 443.264, "r_y2": 305.734, "r_x3": 167.817, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 317.023, "r_x1": 480.595, "r_y1": 317.023, "r_x2": 480.595, "r_y2": 305.734, "r_x3": 447.577, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.982, "r_x1": 480.595, "r_y1": 327.982, "r_x2": 480.595, "r_y2": 316.693, "r_x3": 134.765, "r_y3": 316.693, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.746, "r_x1": 168.048, "r_y1": 350.746, "r_x2": 168.048, "r_y2": 339.457, "r_x3": 160.37, "r_y3": 339.457, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 363.697, "r_x1": 183.828, "r_y1": 363.697, "r_x2": 183.828, "r_y2": 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264.403, "b": 516.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 516.9590000000001, "r_x1": 149.402, "r_y1": 516.9590000000001, "r_x2": 149.402, "r_y2": 508.152, "r_x3": 134.765, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 516.9590000000001, "r_x1": 264.403, "r_y1": 516.9590000000001, "r_x2": 264.403, "r_y2": 508.152, "r_x3": 160.859, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.596, "b": 617.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.348, "r_x1": 480.591, "r_y1": 533.348, "r_x2": 480.591, "r_y2": 524.5509999999999, "r_x3": 134.765, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 164.921, "r_y1": 545.304, "r_x2": 164.921, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.193, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "body": [{"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 134.765, "t": 118.93399999999997, "r": 480.596, "b": 151.64200000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9805440306663513, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 127.731, "r_x1": 480.594, "r_y1": 127.731, "r_x2": 480.594, "r_y2": 118.93399999999997, "r_x3": 134.765, "r_y3": 118.93399999999997, "coord_origin": "TOPLEFT"}, "text": "order to compute the TED score. Inference timing results for all experiments", "orig": "order to compute the TED score. Inference timing results for all experiments", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 139.68600000000004, "r_x1": 480.596, "r_y1": 139.68600000000004, "r_x2": 480.596, "r_y2": 130.889, "r_x3": 134.765, "r_y3": 130.889, "coord_origin": "TOPLEFT"}, "text": "were obtained from the same machine on a single core with AMD EPYC 7763", "orig": "were obtained from the same machine on a single core with AMD EPYC 7763", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 151.64200000000005, "r_x1": 210.785, "r_y1": 151.64200000000005, "r_x2": 210.785, "r_y2": 142.84500000000003, "r_x3": 134.765, "r_y3": 142.84500000000003, "coord_origin": "TOPLEFT"}, "text": "CPU @2.45 GHz.", "orig": "CPU @2.45 GHz.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz."}, {"label": "section_header", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 134.765, "t": 169.18600000000004, "r": 318.448, "b": 177.99300000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9579681158065796, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 177.99300000000005, "r_x1": 149.402, "r_y1": 177.99300000000005, "r_x2": 149.402, "r_y2": 169.18600000000004, "r_x3": 134.765, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "5.1", "orig": "5.1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 177.99300000000005, "r_x1": 318.448, "r_y1": 177.99300000000005, "r_x2": 318.448, "r_y2": 169.18600000000004, "r_x3": 160.859, "r_y3": 169.18600000000004, "coord_origin": "TOPLEFT"}, "text": "Hyper Parameter Optimization", "orig": "Hyper Parameter Optimization", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.1 Hyper Parameter Optimization"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 134.765, "t": 185.586, "r": 480.593, "b": 254.159, "coord_origin": "TOPLEFT"}, "confidence": 0.9842326641082764, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 194.38300000000004, "r_x1": 480.592, "r_y1": 194.38300000000004, "r_x2": 480.592, "r_y2": 185.586, "r_x3": 134.765, "r_y3": 185.586, "coord_origin": "TOPLEFT"}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "orig": "We have chosen the PubTabNet data set to perform HPO, since it includes a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 206.33799999999997, "r_x1": 480.592, "r_y1": 206.33799999999997, "r_x2": 480.592, "r_y2": 197.54100000000005, "r_x3": 134.765, "r_y3": 197.54100000000005, "coord_origin": "TOPLEFT"}, "text": "highly diverse set of tables. Also we report TED scores separately for simple and", "orig": "highly diverse set of tables. Also we report TED scores separately for simple and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 218.293, "r_x1": 480.592, "r_y1": 218.293, "r_x2": 480.592, "r_y2": 209.49599999999998, "r_x3": 134.765, "r_y3": 209.49599999999998, "coord_origin": "TOPLEFT"}, "text": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "orig": "complex tables (tables with cell spans). Results are presented in Table. 1. It is", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 230.24800000000005, "r_x1": 480.593, "r_y1": 230.24800000000005, "r_x2": 480.593, "r_y2": 221.45100000000002, "r_x3": 134.765, "r_y3": 221.45100000000002, "coord_origin": "TOPLEFT"}, "text": "evident that with OTSL, our model achieves the same TED score and slightly", "orig": "evident that with OTSL, our model achieves the same TED score and slightly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 242.20299999999997, "r_x1": 440.942, "r_y1": 242.20299999999997, "r_x2": 440.942, "r_y2": 233.40599999999995, "r_x3": 134.765, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "better mAP scores in comparison to HTML. However OTSL yields a", "orig": "better mAP scores in comparison to HTML. However OTSL yields a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.868, "r_y0": 242.20299999999997, "r_x1": 480.588, "r_y1": 242.20299999999997, "r_x2": 480.588, "r_y2": 233.40599999999995, "r_x3": 444.868, "r_y3": 233.40599999999995, "coord_origin": "TOPLEFT"}, "text": "2x speed", "orig": "2x speed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 254.159, "r_x1": 145.201, "r_y1": 254.159, "r_x2": 145.201, "r_y2": 245.36199999999997, "r_x3": 134.765, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "up", "orig": "up", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.149, "r_y0": 254.159, "r_x1": 311.22, "r_y1": 254.159, "r_x2": 311.22, "r_y2": 245.36199999999997, "r_x3": 149.149, "r_y3": 245.36199999999997, "coord_origin": "TOPLEFT"}, "text": "in the inference runtime over HTML.", "orig": "in the inference runtime over HTML.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML."}, {"label": "caption", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 134.765, "t": 272.85699999999997, "r": 480.595, "b": 327.982, "coord_origin": "TOPLEFT"}, "confidence": 0.9570781588554382, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 282.998, "r_x1": 160.118, "r_y1": 282.998, "r_x2": 160.118, "r_y2": 275.072, "r_x3": 134.765, "r_y3": 275.072, "coord_origin": "TOPLEFT"}, "text": "Table", "orig": "Table", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.345, "r_y0": 282.998, "r_x1": 175.595, "r_y1": 282.998, "r_x2": 175.595, "r_y2": 275.072, "r_x3": 167.345, "r_y3": 275.072, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 188.133, "r_y0": 284.146, "r_x1": 255.736, "r_y1": 284.146, "r_x2": 255.736, "r_y2": 272.85699999999997, "r_x3": 188.133, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "HPO performed", "orig": "HPO performed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.013, "r_y0": 284.146, "r_x1": 269.69, "r_y1": 284.146, "r_x2": 269.69, "r_y2": 272.85699999999997, "r_x3": 262.013, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "in", "orig": "in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 275.957, "r_y0": 284.146, "r_x1": 300.652, "r_y1": 284.146, "r_x2": 300.652, "r_y2": 272.85699999999997, "r_x3": 275.957, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 306.92, "r_y0": 284.146, "r_x1": 321.763, "r_y1": 284.146, "r_x2": 321.763, "r_y2": 272.85699999999997, "r_x3": 306.92, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "and", "orig": "and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 328.03, "r_y0": 284.146, "r_x1": 419.24, "r_y1": 284.146, "r_x2": 419.24, "r_y2": 272.85699999999997, "r_x3": 328.03, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "HTML representation", "orig": "HTML representation", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.508, "r_y0": 284.146, "r_x1": 435.233, "r_y1": 284.146, "r_x2": 435.233, "r_y2": 272.85699999999997, "r_x3": 425.508, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "on", "orig": "on", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 441.509, "r_y0": 284.146, "r_x1": 454.309, "r_y1": 284.146, "r_x2": 454.309, "r_y2": 272.85699999999997, "r_x3": 441.509, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 460.576, "r_y0": 284.146, "r_x1": 480.594, "r_y1": 284.146, "r_x2": 480.594, "r_y2": 272.85699999999997, "r_x3": 460.576, "r_y3": 272.85699999999997, "coord_origin": "TOPLEFT"}, "text": "same", "orig": "same", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 295.105, "r_x1": 480.595, "r_y1": 295.105, "r_x2": 480.595, "r_y2": 283.816, "r_x3": 134.765, "r_y3": 283.816, "coord_origin": "TOPLEFT"}, "text": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "orig": "transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 306.064, "r_x1": 480.595, "r_y1": 306.064, "r_x2": 480.595, "r_y2": 294.775, "r_x3": 134.765, "r_y3": 294.775, "coord_origin": "TOPLEFT"}, "text": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "orig": "fects of reducing the # of layers in encoder and decoder stages of the model show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 317.023, "r_x1": 163.504, "r_y1": 317.023, "r_x2": 163.504, "r_y2": 305.734, "r_x3": 134.765, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "smaller", "orig": "smaller", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.817, "r_y0": 317.023, "r_x1": 443.264, "r_y1": 317.023, "r_x2": 443.264, "r_y2": 305.734, "r_x3": 167.817, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL perform better, especially in recognizing", "orig": "models trained on OTSL perform better, especially in recognizing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 447.577, "r_y0": 317.023, "r_x1": 480.595, "r_y1": 317.023, "r_x2": 480.595, "r_y2": 305.734, "r_x3": 447.577, "r_y3": 305.734, "coord_origin": "TOPLEFT"}, "text": "complex", "orig": "complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 327.982, "r_x1": 480.595, "r_y1": 327.982, "r_x2": 480.595, "r_y2": 316.693, "r_x3": 134.765, "r_y3": 316.693, "coord_origin": "TOPLEFT"}, "text": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "orig": "table structures, and maintain a much higher mAP score than the HTML counterpart.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart."}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 139.66845703125, "t": 337.5747985839844, "r": 475.00372314453125, "b": 469.4721374511719, "coord_origin": "TOPLEFT"}, "confidence": 0.9901032447814941, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.37, "r_y0": 350.746, "r_x1": 168.048, "r_y1": 350.746, "r_x2": 168.048, "r_y2": 339.457, "r_x3": 160.37, "r_y3": 339.457, "coord_origin": "TOPLEFT"}, "text": "#", "orig": "#", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 144.592, "r_y0": 363.697, "r_x1": 183.828, "r_y1": 363.697, "r_x2": 183.828, "r_y2": 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264.403, "b": 516.9590000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9592539668083191, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 516.9590000000001, "r_x1": 149.402, "r_y1": 516.9590000000001, "r_x2": 149.402, "r_y2": 508.152, "r_x3": 134.765, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "5.2", "orig": "5.2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 160.859, "r_y0": 516.9590000000001, "r_x1": 264.403, "r_y1": 516.9590000000001, "r_x2": 264.403, "r_y2": 508.152, "r_x3": 160.859, "r_y3": 508.152, "coord_origin": "TOPLEFT"}, "text": "Quantitative Results", "orig": "Quantitative Results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "5.2 Quantitative Results"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 524.5509999999999, "r": 480.596, "b": 617.035, "coord_origin": "TOPLEFT"}, "confidence": 0.9854757189750671, "cells": [{"index": 102, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 533.348, "r_x1": 480.591, "r_y1": 533.348, "r_x2": 480.591, "r_y2": 524.5509999999999, "r_x3": 134.765, "r_y3": 524.5509999999999, "coord_origin": "TOPLEFT"}, "text": "We picked the model parameter configuration that produced the best prediction", "orig": "We picked the model parameter configuration that produced the best prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 103, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 545.304, "r_x1": 164.921, "r_y1": 545.304, "r_x2": 164.921, "r_y2": 536.5070000000001, "r_x3": 134.765, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "quality", "orig": "quality", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 104, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 169.324, "r_y0": 545.304, "r_x1": 480.587, "r_y1": 545.304, "r_x2": 480.587, "r_y2": 536.5070000000001, "r_x3": 169.324, "r_y3": 536.5070000000001, "coord_origin": "TOPLEFT"}, "text": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "orig": "(enc=6, dec=6, heads=8) with PubTabNet alone, then independently", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 105, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 557.259, "r_x1": 480.596, "r_y1": 557.259, "r_x2": 480.596, "r_y2": 548.462, "r_x3": 134.765, "r_y3": 548.462, "coord_origin": "TOPLEFT"}, "text": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "orig": "trained and evaluated it on three publicly available data sets: PubTabNet (395k", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 106, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 569.2139999999999, "r_x1": 480.592, "r_y1": 569.2139999999999, "r_x2": 480.592, "r_y2": 560.417, "r_x3": 134.765, "r_y3": 560.417, "coord_origin": "TOPLEFT"}, "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "orig": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 107, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 581.169, "r_x1": 480.591, "r_y1": 581.169, "r_x2": 480.591, "r_y2": 572.3720000000001, "r_x3": 134.765, "r_y3": 572.3720000000001, "coord_origin": "TOPLEFT"}, "text": "Performance results are presented in Table. 2. It is clearly evident that the model", "orig": "Performance results are presented in Table. 2. It is clearly evident that the model", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 108, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 593.124, "r_x1": 480.596, "r_y1": 593.124, "r_x2": 480.596, "r_y2": 584.327, "r_x3": 134.765, "r_y3": 584.327, "coord_origin": "TOPLEFT"}, "text": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "orig": "trained on OTSL outperforms HTML across the board, keeping high TEDs and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 109, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 605.08, "r_x1": 480.588, "r_y1": 605.08, "r_x2": 480.588, "r_y2": 596.283, "r_x3": 134.765, "r_y3": 596.283, "coord_origin": "TOPLEFT"}, "text": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "orig": "mAP scores even on difficult financial tables (FinTabNet) that contain sparse", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 110, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 617.035, "r_x1": 206.787, "r_y1": 617.035, "r_x2": 206.787, "r_y2": 608.238, "r_x3": 134.765, "r_y3": 608.238, "coord_origin": "TOPLEFT"}, "text": "and large tables.", "orig": "and large tables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables."}, {"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 620.193, "r": 480.596, "b": 664.855, "coord_origin": "TOPLEFT"}, "confidence": 0.9851234555244446, "cells": [{"index": 111, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 628.99, "r_x1": 205.597, "r_y1": 628.99, "r_x2": 205.597, "r_y2": 620.193, "r_x3": 149.709, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "Additionally,", "orig": "Additionally,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 210.578, "r_y0": 628.99, "r_x1": 224.412, "r_y1": 628.99, "r_x2": 224.412, "r_y2": 620.193, "r_x3": 210.578, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "the", "orig": "the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 229.383, "r_y0": 628.99, "r_x1": 257.74, "r_y1": 628.99, "r_x2": 257.74, "r_y2": 620.193, "r_x3": 229.383, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "results", "orig": "results", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.722, "r_y0": 628.99, "r_x1": 307.319, "r_y1": 628.99, "r_x2": 307.319, "r_y2": 620.193, "r_x3": 262.722, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "show that", "orig": "show that", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 312.301, "r_y0": 628.99, "r_x1": 480.593, "r_y1": 628.99, "r_x2": 480.593, "r_y2": 620.193, "r_x3": 312.301, "r_y3": 620.193, "coord_origin": "TOPLEFT"}, "text": "OTSL has an advantage over HTML", "orig": "OTSL has an advantage over HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 116, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 640.9449999999999, "r_x1": 480.596, "r_y1": 640.9449999999999, "r_x2": 480.596, "r_y2": 632.148, "r_x3": 134.765, "r_y3": 632.148, "coord_origin": "TOPLEFT"}, "text": "when applied on a bigger data set like PubTables-1M and achieves significantly", "orig": "when applied on a bigger data set like PubTables-1M and achieves significantly", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 117, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 652.9, "r_x1": 480.593, "r_y1": 652.9, "r_x2": 480.593, "r_y2": 644.1030000000001, "r_x3": 134.765, "r_y3": 644.1030000000001, "coord_origin": "TOPLEFT"}, "text": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "orig": "improved scores. Finally, OTSL achieves faster inference due to fewer decoding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 118, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 664.855, "r_x1": 405.797, "r_y1": 664.855, "r_x2": 405.797, "r_y2": 656.058, "r_x3": 134.765, "r_y3": 656.058, "coord_origin": "TOPLEFT"}, "text": "steps which is a result of the reduced sequence representation.", "orig": "steps which is a result of the reduced sequence representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation."}], "headers": [{"label": "page_header", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9373378753662109, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_header", "bbox": {"l": 475.984, "t": 91.49300000000005, "r": 480.591, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.8857625722885132, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 475.984, "r_y0": 102.78200000000004, "r_x1": 480.591, "r_y1": 102.78200000000004, "r_x2": 480.591, "r_y2": 91.49300000000005, "r_x3": 475.984, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "9", "orig": "9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "9"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 143.979, "r_y1": 102.78200000000004, "r_x2": 143.979, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "10", "orig": "10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.821, "r_y0": 102.78200000000004, "r_x1": 231.72, "r_y1": 102.78200000000004, "r_x2": 231.72, "r_y2": 91.49300000000005, "r_x3": 167.821, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. 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In", "orig": "demonstrates less overlap and more accurate bounding boxes with OTSL. In", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 342.568, "r_x1": 480.587, "r_y1": 342.568, "r_x2": 480.587, "r_y2": 333.771, "r_x3": 134.765, "r_y3": 333.771, "coord_origin": "TOPLEFT"}, "text": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "orig": "Figure 6, OTSL proves to be more effective in handling tables with longer to-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 354.523, "r_x1": 480.59, "r_y1": 354.523, "r_x2": 480.59, "r_y2": 345.726, "r_x3": 134.765, "r_y3": 345.726, "coord_origin": "TOPLEFT"}, "text": "ken sequences, resulting in even more precise structure prediction and bounding", "orig": "ken sequences, resulting in even more precise structure prediction and bounding", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 366.478, "r_x1": 161.657, "r_y1": 366.478, "r_x2": 161.657, "r_y2": 357.681, "r_x3": 134.765, "r_y3": 357.681, "coord_origin": "TOPLEFT"}, "text": "boxes.", "orig": "boxes.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. 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"confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 578.748, "r_x1": 180.174, "r_y1": 555.783, "r_x2": 172.391, "r_y2": 555.777, "r_x3": 172.374, "r_y3": 578.742, "coord_origin": "TOPLEFT"}, "text": "HTML", "orig": "HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 194.478, "t": 91.49300000000005, "r": 447.543, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9301655888557434, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.478, "r_y0": 102.78200000000004, "r_x1": 447.543, "r_y1": 102.78200000000004, "r_x2": 447.543, "r_y2": 91.49300000000005, "r_x3": 194.478, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "Optimized Table Tokenization for Table Structure Recognition", "orig": "Optimized Table Tokenization for Table Structure Recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Optimized Table Tokenization for Table Structure Recognition"}, {"label": "page_header", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "page_header", "bbox": {"l": 471.376, "t": 91.49300000000005, "r": 480.59, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9007201790809631, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 471.376, "r_y0": 102.78200000000004, "r_x1": 480.59, "r_y1": 102.78200000000004, "r_x2": 480.59, "r_y2": 91.49300000000005, "r_x3": 471.376, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "11"}, {"label": "caption", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.588, "b": 178.70899999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8657060861587524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 6.", "orig": "Fig. 6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.215, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 165.215, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "Visualization of predicted structure and detected bounding boxes on a complex", "orig": "Visualization of predicted structure and detected bounding boxes on a complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 480.588, "r_y1": 145.832, "r_x2": 480.588, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "orig": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 156.79100000000005, "r_x1": 480.588, "r_y1": 156.79100000000005, "r_x2": 480.588, "r_y2": 145.50199999999995, "r_x3": 134.765, "r_y3": 145.50199999999995, "coord_origin": "TOPLEFT"}, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "orig": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.75, "r_x1": 480.588, "r_y1": 167.75, "r_x2": 480.588, "r_y2": 156.461, "r_x3": 134.765, "r_y3": 156.461, "coord_origin": "TOPLEFT"}, "text": "didn't complete the HTML sequence correctly and displayed a lot more of drift and", "orig": "didn't complete the HTML sequence correctly and displayed a lot more of drift and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.70899999999995, "r_x1": 415.845, "r_y1": 178.70899999999995, "r_x2": 415.845, "r_y2": 167.41999999999996, "r_x3": 134.765, "r_y3": 167.41999999999996, "coord_origin": "TOPLEFT"}, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "orig": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39285278320312, "t": 181.9665069580078, "r": 447.35137939453125, "b": 634.0056762695312, "coord_origin": "TOPLEFT"}, "confidence": 0.7612658739089966, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.505, "r_y0": 320.335, "r_x1": 177.596, "r_y1": 320.335, "r_x2": 177.596, "r_y2": 312.552, "r_x3": 171.505, "r_y3": 312.552, "coord_origin": "TOPLEFT"}, "text": "B", "orig": "B", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.058, "r_y0": 500.537, "r_x1": 177.149, "r_y1": 500.537, "r_x2": 177.149, "r_y2": 492.754, "r_x3": 171.058, "r_y3": 492.754, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 633.395, "r_x1": 374.963, "r_y1": 633.395, "r_x2": 374.963, "r_y2": 627.558, "r_x3": 283.047, "r_y3": 627.558, "coord_origin": "TOPLEFT"}, "text": "Incorrect end of HTML sequence", "orig": "Incorrect end of HTML sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 623.271, "r_x1": 398.06, "r_y1": 623.271, "r_x2": 398.06, "r_y2": 617.434, "r_x3": 283.047, "r_y3": 617.434, "coord_origin": "TOPLEFT"}, "text": "Horizontally merged cells are not present", "orig": "Horizontally merged cells are not present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.642, "r_y0": 471.511, 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cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.834, "r_y0": 194.32600000000002, "r_x1": 253.613, "r_y1": 194.32600000000002, "r_x2": 253.613, "r_y2": 186.962, "r_x3": 247.834, "r_y3": 186.962, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.19, "r_y0": 613.719, "r_x1": 381.547, "r_y1": 613.719, "r_x2": 381.547, "r_y2": 607.8820000000001, "r_x3": 292.19, "r_y3": 607.8820000000001, "coord_origin": "TOPLEFT"}, "text": "Bounding box drifting at the end", "orig": "Bounding box drifting at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 403.401, "r_x1": 180.173, "r_y1": 381.368, "r_x2": 172.39, "r_y2": 381.363, "r_x3": 172.374, "r_y3": 403.395, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 578.748, "r_x1": 180.174, "r_y1": 555.783, "r_x2": 172.391, "r_y2": 555.777, "r_x3": 172.374, "r_y3": 578.742, "coord_origin": "TOPLEFT"}, "text": "HTML", "orig": "HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 18, "label": "text", "bbox": {"l": 171.505, "t": 312.552, "r": 177.596, "b": 320.335, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.505, "r_y0": 320.335, "r_x1": 177.596, "r_y1": 320.335, "r_x2": 177.596, "r_y2": 312.552, "r_x3": 171.505, "r_y3": 312.552, "coord_origin": "TOPLEFT"}, "text": "B", "orig": "B", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 171.058, "t": 492.754, "r": 177.149, "b": 500.537, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.058, "r_y0": 500.537, "r_x1": 177.149, "r_y1": 500.537, "r_x2": 177.149, "r_y2": 492.754, "r_x3": 171.058, "r_y3": 492.754, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 283.047, "t": 627.558, "r": 374.963, "b": 633.395, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 633.395, "r_x1": 374.963, "r_y1": 633.395, "r_x2": 374.963, "r_y2": 627.558, "r_x3": 283.047, "r_y3": 627.558, "coord_origin": "TOPLEFT"}, "text": "Incorrect end of HTML sequence", "orig": "Incorrect end of HTML sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 283.047, "t": 617.434, "r": 398.06, "b": 623.271, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 623.271, "r_x1": 398.06, "r_y1": 623.271, "r_x2": 398.06, "r_y2": 617.434, "r_x3": 283.047, "r_y3": 617.434, "coord_origin": "TOPLEFT"}, "text": "Horizontally merged cells are not present", "orig": "Horizontally merged cells are not present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 293.642, "t": 465.674, "r": 437.508, "b": 471.511, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.642, "r_y0": 471.511, "r_x1": 437.508, "r_y1": 471.511, "r_x2": 437.508, "r_y2": 465.674, "r_x3": 293.642, "r_y3": 465.674, "coord_origin": "TOPLEFT"}, "text": "Repeating pattern is well represented in predictions", "orig": "Repeating pattern is well represented in predictions", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 23, "label": "text", "bbox": {"l": 181.891, "t": 288.436, "r": 239.235, "b": 294.273, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.891, "r_y0": 294.273, "r_x1": 239.235, "r_y1": 294.273, "r_x2": 239.235, "r_y2": 288.436, "r_x3": 181.891, "r_y3": 288.436, "coord_origin": "TOPLEFT"}, "text": "Repeating pattern of", "orig": "Repeating pattern of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 181.891, "t": 294.97, "r": 251.529, "b": 300.807, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.891, "r_y0": 300.807, "r_x1": 251.529, "r_y1": 300.807, "r_x2": 251.529, "r_y2": 294.97, "r_x3": 181.891, "r_y3": 294.97, "coord_origin": "TOPLEFT"}, "text": "horizontally merged cells", "orig": "horizontally merged cells", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 247.834, "t": 186.962, "r": 253.613, "b": 194.32600000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 247.834, "r_y0": 194.32600000000002, "r_x1": 253.613, "r_y1": 194.32600000000002, "r_x2": 253.613, "r_y2": 186.962, "r_x3": 247.834, "r_y3": 186.962, "coord_origin": "TOPLEFT"}, "text": "A", "orig": "A", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 292.19, "t": 607.8820000000001, "r": 381.547, "b": 613.719, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.19, "r_y0": 613.719, "r_x1": 381.547, "r_y1": 613.719, "r_x2": 381.547, "r_y2": 607.8820000000001, "r_x3": 292.19, "r_y3": 607.8820000000001, "coord_origin": "TOPLEFT"}, "text": "Bounding box drifting at the end", "orig": "Bounding box drifting at the end", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 172.374, "t": 381.363, "r": 180.173, "b": 403.401, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 403.401, "r_x1": 180.173, "r_y1": 381.368, "r_x2": 172.39, "r_y2": 381.363, "r_x3": 172.374, "r_y3": 403.395, "coord_origin": "TOPLEFT"}, "text": "OTSL", "orig": "OTSL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 28, "label": "text", "bbox": {"l": 172.374, "t": 555.777, "r": 180.174, "b": 578.748, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.157, "r_y0": 578.748, "r_x1": 180.174, "r_y1": 555.783, "r_x2": 172.391, "r_y2": 555.777, "r_x3": 172.374, "r_y3": 578.742, "coord_origin": "TOPLEFT"}, "text": "HTML", "orig": "HTML", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "caption", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "caption", "bbox": {"l": 134.765, "t": 123.58399999999995, "r": 480.588, "b": 178.70899999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.8657060861587524, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 133.72500000000002, "r_x1": 162.644, "r_y1": 133.72500000000002, "r_x2": 162.644, "r_y2": 125.79899999999998, "r_x3": 134.765, "r_y3": 125.79899999999998, "coord_origin": "TOPLEFT"}, "text": "Fig. 6.", "orig": "Fig. 6.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.215, "r_y0": 134.87300000000005, "r_x1": 480.587, "r_y1": 134.87300000000005, "r_x2": 480.587, "r_y2": 123.58399999999995, "r_x3": 165.215, "r_y3": 123.58399999999995, "coord_origin": "TOPLEFT"}, "text": "Visualization of predicted structure and detected bounding boxes on a complex", "orig": "Visualization of predicted structure and detected bounding boxes on a complex", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 145.832, "r_x1": 480.588, "r_y1": 145.832, "r_x2": 480.588, "r_y2": 134.543, "r_x3": 134.765, "r_y3": 134.543, "coord_origin": "TOPLEFT"}, "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "orig": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 156.79100000000005, "r_x1": 480.588, "r_y1": 156.79100000000005, "r_x2": 480.588, "r_y2": 145.50199999999995, "r_x3": 134.765, "r_y3": 145.50199999999995, "coord_origin": "TOPLEFT"}, "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "orig": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.75, "r_x1": 480.588, "r_y1": 167.75, "r_x2": 480.588, "r_y2": 156.461, "r_x3": 134.765, "r_y3": 156.461, "coord_origin": "TOPLEFT"}, "text": "didn't complete the HTML sequence correctly and displayed a lot more of drift and", "orig": "didn't complete the HTML sequence correctly and displayed a lot more of drift and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 178.70899999999995, "r_x1": 415.845, "r_y1": 178.70899999999995, "r_x2": 415.845, "r_y2": 167.41999999999996, "r_x3": 134.765, "r_y3": 167.41999999999996, "coord_origin": "TOPLEFT"}, "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "orig": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."}, {"label": "picture", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 168.39285278320312, "t": 181.9665069580078, "r": 447.35137939453125, "b": 634.0056762695312, "coord_origin": "TOPLEFT"}, "confidence": 0.7612658739089966, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.505, "r_y0": 320.335, "r_x1": 177.596, "r_y1": 320.335, "r_x2": 177.596, "r_y2": 312.552, "r_x3": 171.505, "r_y3": 312.552, "coord_origin": "TOPLEFT"}, "text": "B", "orig": "B", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.058, "r_y0": 500.537, "r_x1": 177.149, "r_y1": 500.537, "r_x2": 177.149, "r_y2": 492.754, "r_x3": 171.058, "r_y3": 492.754, "coord_origin": "TOPLEFT"}, "text": "C", "orig": "C", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 633.395, "r_x1": 374.963, "r_y1": 633.395, "r_x2": 374.963, "r_y2": 627.558, "r_x3": 283.047, "r_y3": 627.558, "coord_origin": "TOPLEFT"}, "text": "Incorrect end of HTML sequence", "orig": "Incorrect end of HTML sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 283.047, "r_y0": 623.271, "r_x1": 398.06, "r_y1": 623.271, "r_x2": 398.06, "r_y2": 617.434, "r_x3": 283.047, "r_y3": 617.434, "coord_origin": "TOPLEFT"}, "text": "Horizontally merged cells are not present", "orig": "Horizontally merged cells are not present", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 293.642, "r_y0": 471.511, 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Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.11699999999996, "r_x1": 141.489, "r_y1": 128.11699999999996, "r_x2": 141.489, "r_y2": 117.54899999999998, "r_x3": 134.765, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 128.11699999999996, "r_x1": 219.255, "r_y1": 128.11699999999996, "r_x2": 219.255, "r_y2": 117.54899999999998, "r_x3": 154.938, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "Conclusion", "orig": "Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 155.66099999999994, "r_x1": 480.595, "r_y1": 155.66099999999994, "r_x2": 480.595, "r_y2": 146.86400000000003, "r_x3": 134.765, "r_y3": 146.86400000000003, "coord_origin": "TOPLEFT"}, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.61599999999999, "r_x1": 152.499, "r_y1": 167.61599999999999, "r_x2": 152.499, "r_y2": 158.81899999999996, "r_x3": 134.765, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "ture", "orig": "ture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.963, "r_y0": 167.61599999999999, "r_x1": 205.131, "r_y1": 167.61599999999999, "r_x2": 205.131, "r_y2": 158.81899999999996, "r_x3": 156.963, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "recognition", "orig": "recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 209.594, "r_y0": 167.61599999999999, "r_x1": 480.595, "r_y1": 167.61599999999999, "r_x2": 480.595, "r_y2": 158.81899999999996, "r_x3": 209.594, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "with Im2Seq models is ill-suited and has serious limitations.", "orig": "with Im2Seq models is ill-suited and has serious limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly used general purpose languages,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.48199999999997, "r_x1": 239.539, "r_y1": 203.48199999999997, "r_x2": 239.539, "r_y2": 194.68499999999995, "r_x3": 134.765, "r_y3": 194.68499999999995, "coord_origin": "TOPLEFT"}, "text": "has several key benefits.", "orig": "has several key benefits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 216.24099999999999, "r_x1": 480.593, "r_y1": 216.24099999999999, "r_x2": 480.593, "r_y2": 207.44399999999996, "r_x3": 149.709, "r_y3": 207.44399999999996, "coord_origin": "TOPLEFT"}, "text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.607, "r_y0": 531.8910000000001, "r_x1": 277.21, "r_y1": 531.8910000000001, "r_x2": 277.21, "r_y2": 520.602, "r_x3": 220.607, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "M., Carvalho,", "orig": "M., Carvalho,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.594, "r_y0": 531.8910000000001, "r_x1": 293.621, "r_y1": 531.8910000000001, "r_x2": 293.621, "r_y2": 520.602, "r_x3": 281.594, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "A.,", "orig": "A.,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 297.996, "r_y0": 531.8910000000001, "r_x1": 379.518, "r_y1": 531.8910000000001, "r_x2": 379.518, "r_y2": 520.602, "r_x3": 297.996, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "Ramis, C.B., Staar,", "orig": "Ramis, C.B., Staar,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.894, "r_y0": 531.8910000000001, "r_x1": 413.839, "r_y1": 531.8910000000001, "r_x2": 413.839, "r_y2": 520.602, "r_x3": 383.894, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "P.W.J.:", "orig": "P.W.J.:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 418.214, "r_y0": 531.8910000000001, "r_x1": 459.065, "r_y1": 531.8910000000001, "r_x2": 459.065, "r_y2": 520.602, "r_x3": 418.214, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "Delivering", "orig": "Delivering", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 463.441, "r_y0": 531.8910000000001, "r_x1": 480.592, "r_y1": 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limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly 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"text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.799, "r": 480.595, "b": 468.103, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.821, "r": 197.686, "b": 504.389, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 520.602, "r": 480.592, "b": 563.8720000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.371, "r_y0": 531.8910000000001, "r_x1": 146.537, "r_y1": 531.8910000000001, "r_x2": 146.537, "r_y2": 520.602, "r_x3": 139.371, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.522, "r_y0": 531.8910000000001, "r_x1": 189.958, "r_y1": 531.8910000000001, "r_x2": 189.958, "r_y2": 520.602, "r_x3": 151.522, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "Auer, C.,", "orig": "Auer, C.,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 194.342, "r_y0": 531.8910000000001, "r_x1": 216.222, "r_y1": 531.8910000000001, "r_x2": 216.222, "r_y2": 520.602, "r_x3": 194.342, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "Dolfi,", "orig": "Dolfi,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 220.607, "r_y0": 531.8910000000001, "r_x1": 277.21, "r_y1": 531.8910000000001, "r_x2": 277.21, "r_y2": 520.602, "r_x3": 220.607, "r_y3": 520.602, "coord_origin": "TOPLEFT"}, "text": "M., Carvalho,", "orig": "M., Carvalho,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.594, "r_y0": 531.8910000000001, "r_x1": 293.621, "r_y1": 531.8910000000001, "r_x2": 293.621, "r_y2": 520.602, "r_x3": 281.594, "r_y3": 520.602, 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Lysak, et al."}, {"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54899999999998, "r": 219.255, "b": 128.11699999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.11699999999996, "r_x1": 141.489, "r_y1": 128.11699999999996, "r_x2": 141.489, "r_y2": 117.54899999999998, "r_x3": 134.765, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 128.11699999999996, "r_x1": 219.255, "r_y1": 128.11699999999996, "r_x2": 219.255, "r_y2": 117.54899999999998, "r_x3": 154.938, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "Conclusion", "orig": "Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86400000000003, "r": 480.596, "b": 203.48199999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 155.66099999999994, "r_x1": 480.595, "r_y1": 155.66099999999994, "r_x2": 480.595, "r_y2": 146.86400000000003, "r_x3": 134.765, "r_y3": 146.86400000000003, "coord_origin": "TOPLEFT"}, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.61599999999999, "r_x1": 152.499, "r_y1": 167.61599999999999, "r_x2": 152.499, "r_y2": 158.81899999999996, "r_x3": 134.765, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "ture", "orig": "ture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.963, "r_y0": 167.61599999999999, "r_x1": 205.131, "r_y1": 167.61599999999999, "r_x2": 205.131, "r_y2": 158.81899999999996, "r_x3": 156.963, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "recognition", "orig": "recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 209.594, "r_y0": 167.61599999999999, "r_x1": 480.595, "r_y1": 167.61599999999999, "r_x2": 480.595, "r_y2": 158.81899999999996, "r_x3": 209.594, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "with Im2Seq models is ill-suited and has serious limitations.", "orig": "with Im2Seq models is ill-suited and has serious limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly used general purpose languages,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.48199999999997, "r_x1": 239.539, "r_y1": 203.48199999999997, "r_x2": 239.539, "r_y2": 194.68499999999995, "r_x3": 134.765, "r_y3": 194.68499999999995, "coord_origin": "TOPLEFT"}, "text": "has several key benefits.", "orig": "has several key benefits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44399999999996, "r": 480.595, "b": 323.837, "coord_origin": "TOPLEFT"}, "confidence": 0.9873476028442383, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 216.24099999999999, "r_x1": 480.593, "r_y1": 216.24099999999999, "r_x2": 480.593, "r_y2": 207.44399999999996, "r_x3": 149.709, "r_y3": 207.44399999999996, "coord_origin": "TOPLEFT"}, "text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.799, "r": 480.595, "b": 468.103, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.821, "r": 197.686, "b": 504.389, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 520.602, "r": 480.592, "b": 563.8720000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"index": 39, 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Analysis and", "orig": "2019 International Conference on Document Analysis and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.518, "r_y0": 665.809, "r_x1": 350.111, "r_y1": 665.809, "r_x2": 350.111, "r_y2": 654.52, "r_x3": 151.518, "r_y3": 654.52, "coord_origin": "TOPLEFT"}, "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "orig": "Recognition (ICDAR). pp. 894-901. IEEE (2019)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"}], "body": [{"label": "section_header", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 134.765, "t": 117.54899999999998, "r": 219.255, "b": 128.11699999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.915635347366333, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 128.11699999999996, "r_x1": 141.489, "r_y1": 128.11699999999996, "r_x2": 141.489, "r_y2": 117.54899999999998, "r_x3": 134.765, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 154.938, "r_y0": 128.11699999999996, "r_x1": 219.255, "r_y1": 128.11699999999996, "r_x2": 219.255, "r_y2": 117.54899999999998, "r_x3": 154.938, "r_y3": 117.54899999999998, "coord_origin": "TOPLEFT"}, "text": "Conclusion", "orig": "Conclusion", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "6 Conclusion"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 134.765, "t": 146.86400000000003, "r": 480.596, "b": 203.48199999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.9806171655654907, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 155.66099999999994, "r_x1": 480.595, "r_y1": 155.66099999999994, "r_x2": 480.595, "r_y2": 146.86400000000003, "r_x3": 134.765, "r_y3": 146.86400000000003, "coord_origin": "TOPLEFT"}, "text": "We demonstrated that representing tables in HTML for the task of table struc-", "orig": "We demonstrated that representing tables in HTML for the task of table struc-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 167.61599999999999, "r_x1": 152.499, "r_y1": 167.61599999999999, "r_x2": 152.499, "r_y2": 158.81899999999996, "r_x3": 134.765, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "ture", "orig": "ture", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.963, "r_y0": 167.61599999999999, "r_x1": 205.131, "r_y1": 167.61599999999999, "r_x2": 205.131, "r_y2": 158.81899999999996, "r_x3": 156.963, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "recognition", "orig": "recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 209.594, "r_y0": 167.61599999999999, "r_x1": 480.595, "r_y1": 167.61599999999999, "r_x2": 480.595, "r_y2": 158.81899999999996, "r_x3": 209.594, "r_y3": 158.81899999999996, "coord_origin": "TOPLEFT"}, "text": "with Im2Seq models is ill-suited and has serious limitations.", "orig": "with Im2Seq models is ill-suited and has serious limitations.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 179.57100000000003, "r_x1": 480.59, "r_y1": 179.57100000000003, "r_x2": 480.59, "r_y2": 170.774, "r_x3": 134.765, "r_y3": 170.774, "coord_origin": "TOPLEFT"}, "text": "Furthermore, we presented in this paper an Optimized Table Structure Language", "orig": "Furthermore, we presented in this paper an Optimized Table Structure Language", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 191.52700000000004, "r_x1": 480.596, "r_y1": 191.52700000000004, "r_x2": 480.596, "r_y2": 182.73000000000002, "r_x3": 134.765, "r_y3": 182.73000000000002, "coord_origin": "TOPLEFT"}, "text": "(OTSL) which, when compared to commonly used general purpose languages,", "orig": "(OTSL) which, when compared to commonly used general purpose languages,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 203.48199999999997, "r_x1": 239.539, "r_y1": 203.48199999999997, "r_x2": 239.539, "r_y2": 194.68499999999995, "r_x3": 134.765, "r_y3": 194.68499999999995, "coord_origin": "TOPLEFT"}, "text": "has several key benefits.", "orig": "has several key benefits.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits."}, {"label": "text", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "text", "bbox": {"l": 134.765, "t": 207.44399999999996, "r": 480.595, "b": 323.837, "coord_origin": "TOPLEFT"}, "confidence": 0.9873476028442383, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 216.24099999999999, "r_x1": 480.593, "r_y1": 216.24099999999999, "r_x2": 480.593, "r_y2": 207.44399999999996, "r_x3": 149.709, "r_y3": 207.44399999999996, "coord_origin": "TOPLEFT"}, "text": "First and foremost, given the same network configuration, inference time for", "orig": "First and foremost, given the same network configuration, inference time for", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 228.19600000000003, "r_x1": 480.594, "r_y1": 228.19600000000003, "r_x2": 480.594, "r_y2": 219.399, "r_x3": 134.765, "r_y3": 219.399, "coord_origin": "TOPLEFT"}, "text": "a table-structure prediction is about 2 times faster compared to the conventional", "orig": "a table-structure prediction is about 2 times faster compared to the conventional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 240.15099999999995, "r_x1": 480.591, "r_y1": 240.15099999999995, "r_x2": 480.591, "r_y2": 231.35400000000004, "r_x3": 134.765, "r_y3": 231.35400000000004, "coord_origin": "TOPLEFT"}, "text": "HTML approach. This is primarily owed to the shorter sequence length of the", "orig": "HTML approach. This is primarily owed to the shorter sequence length of the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 252.106, "r_x1": 230.936, "r_y1": 252.106, "r_x2": 230.936, "r_y2": 243.30899999999997, "r_x3": 134.765, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "OTSL representation.", "orig": "OTSL representation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 235.838, "r_y0": 252.106, "r_x1": 281.762, "r_y1": 252.106, "r_x2": 281.762, "r_y2": 243.30899999999997, "r_x3": 235.838, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "Additional", "orig": "Additional", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 286.664, "r_y0": 252.106, "r_x1": 340.395, "r_y1": 252.106, "r_x2": 340.395, "r_y2": 243.30899999999997, "r_x3": 286.664, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "performance", "orig": "performance", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 345.287, "r_y0": 252.106, "r_x1": 378.82, "r_y1": 252.106, "r_x2": 378.82, "r_y2": 243.30899999999997, "r_x3": 345.287, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "benefits", "orig": "benefits", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 383.721, "r_y0": 252.106, "r_x1": 456.32, "r_y1": 252.106, "r_x2": 456.32, "r_y2": 243.30899999999997, "r_x3": 383.721, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "can be obtained", "orig": "can be obtained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 461.221, "r_y0": 252.106, "r_x1": 480.588, "r_y1": 252.106, "r_x2": 480.588, "r_y2": 243.30899999999997, "r_x3": 461.221, "r_y3": 243.30899999999997, "coord_origin": "TOPLEFT"}, "text": "with", "orig": "with", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 264.062, "r_x1": 480.595, "r_y1": 264.062, "r_x2": 480.595, "r_y2": 255.265, "r_x3": 134.765, "r_y3": 255.265, "coord_origin": "TOPLEFT"}, "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "orig": "HPO (hyper parameter optimization). As we demonstrate in our experiments,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 276.01700000000005, "r_x1": 480.588, "r_y1": 276.01700000000005, "r_x2": 480.588, "r_y2": 267.22, "r_x3": 134.765, "r_y3": 267.22, "coord_origin": "TOPLEFT"}, "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "orig": "models trained on OTSL can be significantly smaller, e.g. by reducing the number", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 287.972, "r_x1": 480.593, "r_y1": 287.972, "r_x2": 480.593, "r_y2": 279.17499999999995, "r_x3": 134.765, "r_y3": 279.17499999999995, "coord_origin": "TOPLEFT"}, "text": "of encoder and decoder layers, while preserving comparatively good prediction", "orig": "of encoder and decoder layers, while preserving comparatively good prediction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 299.927, "r_x1": 480.589, "r_y1": 299.927, "r_x2": 480.589, "r_y2": 291.13, "r_x3": 134.765, "r_y3": 291.13, "coord_origin": "TOPLEFT"}, "text": "quality. This can further improve inference performance, yielding 5-6 times faster", "orig": "quality. This can further improve inference performance, yielding 5-6 times faster", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 311.882, "r_x1": 480.594, "r_y1": 311.882, "r_x2": 480.594, "r_y2": 303.085, "r_x3": 134.765, "r_y3": 303.085, "coord_origin": "TOPLEFT"}, "text": "inference speed in OTSL with prediction quality comparable to models trained", "orig": "inference speed in OTSL with prediction quality comparable to models trained", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 323.837, "r_x1": 240.923, "r_y1": 323.837, "r_x2": 240.923, "r_y2": 315.04, "r_x3": 134.765, "r_y3": 315.04, "coord_origin": "TOPLEFT"}, "text": "on HTML (see Table 1).", "orig": "on HTML (see Table 1).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1)."}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 134.765, "t": 327.799, "r": 480.595, "b": 468.103, "coord_origin": "TOPLEFT"}, "confidence": 0.9868747591972351, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 149.709, "r_y0": 336.596, "r_x1": 480.59, "r_y1": 336.596, "r_x2": 480.59, "r_y2": 327.799, "r_x3": 149.709, "r_y3": 327.799, "coord_origin": "TOPLEFT"}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "orig": "Secondly, OTSL has more inherent structure and a significantly restricted vo-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 348.552, "r_x1": 480.595, "r_y1": 348.552, "r_x2": 480.595, "r_y2": 339.755, "r_x3": 134.765, "r_y3": 339.755, "coord_origin": "TOPLEFT"}, "text": "cabulary size. This allows autoregressive models to perform better in the TED", "orig": "cabulary size. This allows autoregressive models to perform better in the TED", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 360.507, "r_x1": 480.587, "r_y1": 360.507, "r_x2": 480.587, "r_y2": 351.71, "r_x3": 134.765, "r_y3": 351.71, "coord_origin": "TOPLEFT"}, "text": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "orig": "metric, but especially with regards to prediction accuracy of the table-cell bound-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 372.462, "r_x1": 480.595, "r_y1": 372.462, "r_x2": 480.595, "r_y2": 363.665, "r_x3": 134.765, "r_y3": 363.665, "coord_origin": "TOPLEFT"}, "text": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "orig": "ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 384.417, "r_x1": 480.59, "r_y1": 384.417, "r_x2": 480.59, "r_y2": 375.62, "r_x3": 134.765, "r_y3": 375.62, "coord_origin": "TOPLEFT"}, "text": "tically reduces the drift for table cell bounding boxes at high row count and in", "orig": "tically reduces the drift for table cell bounding boxes at high row count and in", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 396.372, "r_x1": 480.587, "r_y1": 396.372, "r_x2": 480.587, "r_y2": 387.575, "r_x3": 134.765, "r_y3": 387.575, "coord_origin": "TOPLEFT"}, "text": "sparse tables. This leads to more accurate predictions and a significant reduction", "orig": "sparse tables. This leads to more accurate predictions and a significant reduction", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 408.328, "r_x1": 480.586, "r_y1": 408.328, "r_x2": 480.586, "r_y2": 399.531, "r_x3": 134.765, "r_y3": 399.531, "coord_origin": "TOPLEFT"}, "text": "in post-processing complexity, which is an undesired necessity in HTML-based", "orig": "in post-processing complexity, which is an undesired necessity in HTML-based", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 420.283, "r_x1": 480.587, "r_y1": 420.283, "r_x2": 480.587, "r_y2": 411.486, "r_x3": 134.765, "r_y3": 411.486, "coord_origin": "TOPLEFT"}, "text": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "orig": "Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 432.238, "r_x1": 480.595, "r_y1": 432.238, "r_x2": 480.595, "r_y2": 423.441, "r_x3": 134.765, "r_y3": 423.441, "coord_origin": "TOPLEFT"}, "text": "simple and always backwards looking. Each new token can be validated only by", "orig": "simple and always backwards looking. Each new token can be validated only by", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 444.193, "r_x1": 480.588, "r_y1": 444.193, "r_x2": 480.588, "r_y2": 435.396, "r_x3": 134.765, "r_y3": 435.396, "coord_origin": "TOPLEFT"}, "text": "analyzing the sequence of previous tokens, without requiring the entire sequence", "orig": "analyzing the sequence of previous tokens, without requiring the entire sequence", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 456.148, "r_x1": 480.59, "r_y1": 456.148, "r_x2": 480.59, "r_y2": 447.351, "r_x3": 134.765, "r_y3": 447.351, "coord_origin": "TOPLEFT"}, "text": "to detect mistakes. This in return allows to perform structural error detection", "orig": "to detect mistakes. This in return allows to perform structural error detection", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 468.103, "r_x1": 366.777, "r_y1": 468.103, "r_x2": 366.777, "r_y2": 459.306, "r_x3": 134.765, "r_y3": 459.306, "coord_origin": "TOPLEFT"}, "text": "and correction on-the-fly during sequence generation.", "orig": "and correction on-the-fly during sequence generation.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation."}, {"label": "section_header", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 134.765, "t": 493.821, "r": 197.686, "b": 504.389, "coord_origin": "TOPLEFT"}, "confidence": 0.9403266310691833, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 504.389, "r_x1": 197.686, "r_y1": 504.389, "r_x2": 197.686, "r_y2": 493.821, "r_x3": 134.765, "r_y3": 493.821, "coord_origin": "TOPLEFT"}, "text": "References", "orig": "References", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "References"}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 139.371, "t": 520.602, "r": 480.592, "b": 563.8720000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9698606729507446, "cells": [{"index": 39, 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Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785"}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 139.371, "t": 565.241, "r": 480.592, "b": 609.407, "coord_origin": "TOPLEFT"}, "confidence": 0.9730570316314697, "cells": [{"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 139.371, "r_y0": 576.53, "r_x1": 146.537, "r_y1": 576.53, "r_x2": 146.537, "r_y2": 565.241, "r_x3": 139.371, "r_y3": 565.241, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.522, "r_y0": 576.53, "r_x1": 480.586, "r_y1": 576.53, "r_x2": 480.586, "r_y2": 565.241, "r_x3": 151.522, "r_y3": 565.241, "coord_origin": "TOPLEFT"}, "text": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "orig": "Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.518, "r_y0": 587.489, "r_x1": 480.59, "r_y1": 587.489, "r_x2": 480.59, "r_y2": 576.2, "r_x3": 151.518, "r_y3": 576.2, "coord_origin": "TOPLEFT"}, "text": "in the wild using transformer and identity matrix-based augmentation. 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IEEE (2019)"}], "headers": [{"label": "page_header", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "page_header", "bbox": {"l": 134.765, "t": 91.49300000000005, "r": 143.979, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7012730240821838, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 134.765, "r_y0": 102.78200000000004, "r_x1": 143.979, "r_y1": 102.78200000000004, "r_x2": 143.979, "r_y2": 91.49300000000005, "r_x3": 134.765, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "14", "orig": "14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "14"}, {"label": "page_header", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "page_header", "bbox": {"l": 167.821, "t": 91.49300000000005, "r": 231.72, "b": 102.78200000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.7889755964279175, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.821, "r_y0": 102.78200000000004, "r_x1": 231.72, "r_y1": 102.78200000000004, "r_x2": 231.72, "r_y2": 91.49300000000005, "r_x3": 167.821, "r_y3": 91.49300000000005, "coord_origin": "TOPLEFT"}, "text": "M. Lysak, et al.", "orig": "M. Lysak, et al.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "M. Lysak, et al."}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/amt_handbook_sample.doctags.txt b/tests/data/groundtruth/docling_v2/amt_handbook_sample.doctags.txt index 08fa3242..c7d15484 100644 --- a/tests/data/groundtruth/docling_v2/amt_handbook_sample.doctags.txt +++ b/tests/data/groundtruth/docling_v2/amt_handbook_sample.doctags.txt @@ -1,16 +1,17 @@ -pulleys, provided the inner race of the bearing is clamped -The two general types of self-locking nuts currently in use +pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws. +The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type. Boots Self-Locking Nut -nut is of one piece, all-metal -The spring keeps the locking and load-carrying sections such -The spring, through the medium of the locking section, exerts -Boots self-locking nuts are made with three different spring -Figure 7-26. -the most common ranges in size for No. 6 up to 1 4 inch, the +nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut. +The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly. +The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency. +Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is +Figure 7-26. Self-locking nuts. +the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. +. Stainless Steel Self-Locking Nut -The stainless steel self-locking nut may be spun on and off +The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened. Elastic Stop Nut -The elastic stop nut is a standard nut with the height -Figure 7-27. +The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This +Figure 7-27. Stainless steel self-locking nut. 7-45 \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/amt_handbook_sample.json b/tests/data/groundtruth/docling_v2/amt_handbook_sample.json index 838ad768..c0c4560c 100644 --- a/tests/data/groundtruth/docling_v2/amt_handbook_sample.json +++ b/tests/data/groundtruth/docling_v2/amt_handbook_sample.json @@ -1 +1 @@ -{"schema_name": "DoclingDocument", "version": "1.2.0", "name": "amt_handbook_sample", "origin": {"mimetype": "application/pdf", "binary_hash": 10189692113572347872, "filename": "amt_handbook_sample.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}, {"cref": "#/texts/2"}, {"cref": "#/texts/3"}, {"cref": "#/texts/4"}, {"cref": "#/texts/5"}, {"cref": "#/texts/6"}, {"cref": "#/pictures/0"}, {"cref": 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types of self-locking nuts currently in use"}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 577.876, "r": 167.272, "b": 569.5, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Boots Self-Locking Nut", "text": "Boots Self-Locking Nut", "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 174.212, "t": 563.466, "r": 311.548, "b": 555.001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "nut is of one piece, all-metal", "text": "nut is of one piece, all-metal"}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 475.966, "r": 309.03, "b": 467.501, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "The spring keeps the locking and load-carrying sections such", "text": "The spring keeps the locking and load-carrying sections such"}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 388.466, "r": 309.042, "b": 380.001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "The spring, through the medium of the locking section, exerts", "text": "The spring, through the medium of the locking section, exerts"}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 275.966, "r": 311.502, "b": 267.501, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Boots self-locking nuts are made with three different spring", "text": "Boots self-locking nuts are made with three different spring"}, {"self_ref": "#/texts/7", "parent": {"cref": 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"coord_origin": "BOTTOMLEFT"}, "charspan": [0, 244]}], "orig": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.", "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 665.466, "r": 311.445, "b": 594.501, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 382]}], "orig": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.", "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 577.876, "r": 167.272, "b": 569.5, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "Boots Self-Locking Nut", "text": "Boots Self-Locking Nut", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 563.466, "r": 311.548, "b": 492.501, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 326]}], "orig": "nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.", "text": "nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 475.966, "r": 311.519, "b": 405.001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 332]}], "orig": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.", "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 388.466, "r": 311.492, "b": 292.501, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 477]}], "orig": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.992, "t": 275.966, "r": 311.502, "b": 255.00099999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/7", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 72.0, "t": 69.72299999999996, "r": 184.148, "b": 61.71500000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Figure 7-26. Self-locking nuts.", "text": "Figure 7-26. Self-locking nuts.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/8", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 102.416, "t": 184.01099999999997, "r": 161.319, "b": 177.20299999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Boots aircraft nut", "text": "Boots aircraft nut", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/9", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 91.686, "t": 92.46600000000001, "r": 129.774, "b": 85.65800000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Flexloc nut", "text": "Flexloc nut", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/10", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 162.481, "t": 92.46600000000001, "r": 207.856, "b": 85.65800000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Fiber locknut", "text": "Fiber locknut", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/11", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 237.314, "t": 92.46600000000001, "r": 289.561, "b": 85.65800000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Elastic stop nut", "text": "Elastic stop nut", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/12", "parent": {"cref": "#/pictures/0"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 216.933, "t": 184.01099999999997, "r": 277.797, "b": 177.20299999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Elastic anchor nut", "text": "Elastic anchor nut", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.992, "t": 727.966, "r": 560.517, "b": 657.001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 370]}], "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.", "text": "the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.995, "t": 652.966, "r": 325.995, "b": 644.501, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ".", "text": ".", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 320.995, "t": 640.376, "r": 450.995, "b": 632.0, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "Stainless Steel Self-Locking Nut", "text": "Stainless Steel Self-Locking Nut", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.995, "t": 625.966, "r": 560.513, "b": 417.501, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1024]}], "orig": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.", "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/17", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 320.995, "t": 400.876, "r": 388.505, "b": 392.5, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Elastic Stop Nut", "text": "Elastic Stop Nut", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/18", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 320.995, "t": 386.466, "r": 558.104, "b": 365.501, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 124]}], "orig": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This", "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/19", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 1, "bbox": {"l": 321.0, "t": 71.74299999999994, "r": 481.649, "b": 63.735000000000014, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Figure 7-27. Stainless steel self-locking nut.", "text": "Figure 7-27. Stainless steel self-locking nut.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/20", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 479.136, "t": 98.745, "r": 531.167, "b": 91.99000000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Tightened nut", "text": "Tightened nut", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/21", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 474.37, "t": 239.58799999999997, "r": 535.234, "b": 232.83299999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Untightened nut", "text": "Untightened nut", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/22", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 456.756, "t": 339.779, "r": 487.084, "b": 332.971, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Nut case", "text": "Nut case", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/23", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 434.623, "t": 193.95299999999997, "r": 497.472, "b": 187.14499999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "Threaded nut core", "text": "Threaded nut core", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/24", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 448.551, "t": 218.45500000000004, "r": 507.686, "b": 211.64699999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "Locking shoulder", "text": "Locking shoulder", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/25", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 424.784, "t": 107.66399999999999, "r": 452.103, "b": 100.856, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Keyway", "text": "Keyway", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 1, "bbox": {"l": 537.985, "t": 43.89999999999998, "r": 560.775, "b": 34.39599999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "7-45", "text": "7-45", "formatting": null, "hyperlink": null}], "pictures": [{"self_ref": "#/pictures/0", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/7"}, {"cref": "#/texts/8"}, {"cref": "#/texts/9"}, {"cref": "#/texts/10"}, {"cref": "#/texts/11"}, {"cref": "#/texts/12"}], "content_layer": "body", "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 70.59269714355469, "t": 242.77777099609375, "r": 309.863037109375, "b": 79.6090087890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/7"}], "references": [], "footnotes": [], "image": null, "annotations": []}, {"self_ref": "#/pictures/1", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/19"}, {"cref": "#/texts/20"}, {"cref": "#/texts/21"}, {"cref": "#/texts/22"}, {"cref": "#/texts/23"}, {"cref": "#/texts/24"}, {"cref": "#/texts/25"}], "content_layer": "body", "label": "picture", "prov": [{"page_no": 1, "bbox": {"l": 320.4467468261719, "t": 352.359375, "r": 558.8576049804688, "b": 81.689208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/19"}], "references": [], "footnotes": [], "image": null, "annotations": []}], "tables": [], "key_value_items": [], "form_items": [], "pages": {"1": {"size": {"width": 594.0, "height": 774.0}, "image": null, "page_no": 1}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/amt_handbook_sample.md b/tests/data/groundtruth/docling_v2/amt_handbook_sample.md index a9a182fc..e82e6422 100644 --- a/tests/data/groundtruth/docling_v2/amt_handbook_sample.md +++ b/tests/data/groundtruth/docling_v2/amt_handbook_sample.md @@ -1,31 +1,33 @@ -pulleys, provided the inner race of the bearing is clamped +pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws. -The two general types of self-locking nuts currently in use +The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type. ## Boots Self-Locking Nut -nut is of one piece, all-metal +nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut. -The spring keeps the locking and load-carrying sections such +The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly. -The spring, through the medium of the locking section, exerts +The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency. -Boots self-locking nuts are made with three different spring +Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is -Figure 7-26. +Figure 7-26. Self-locking nuts. -the most common ranges in size for No. 6 up to 1 4 inch, the +the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. + +. ## Stainless Steel Self-Locking Nut -The stainless steel self-locking nut may be spun on and off +The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened. ## Elastic Stop Nut -The elastic stop nut is a standard nut with the height +The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This -Figure 7-27. +Figure 7-27. Stainless steel self-locking nut. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/amt_handbook_sample.pages.json b/tests/data/groundtruth/docling_v2/amt_handbook_sample.pages.json index 441e7a19..bd74e40e 100644 --- a/tests/data/groundtruth/docling_v2/amt_handbook_sample.pages.json +++ b/tests/data/groundtruth/docling_v2/amt_handbook_sample.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 594.0, "height": 774.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 116.902, "r_y0": 711.999, "r_x1": 119.402, "r_y1": 711.999, "r_x2": 119.402, "r_y2": 703.534, "r_x3": 116.902, "r_y3": 703.534, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 365.902, "r_y0": 709.979, "r_x1": 368.402, "r_y1": 709.979, "r_x2": 368.402, "r_y2": 701.514, "r_x3": 365.902, "r_y3": 701.514, 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"r_x1": 560.572, "r_y1": 408.499, "r_x2": 560.572, "r_y2": 400.034, "r_x3": 558.072, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 66.99900000000002, "r_x1": 309.002, "r_y1": 66.99900000000002, "r_x2": 309.002, "r_y2": 58.53399999999999, "r_x3": 71.992, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "to the supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. 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", "orig": "Figure 7-26. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.402, "r_y0": 711.816, "r_x1": 184.148, "r_y1": 711.816, "r_x2": 184.148, "r_y2": 704.277, "r_x3": 119.402, "r_y3": 704.277, "coord_origin": "TOPLEFT"}, "text": "Self-locking nuts. ", "orig": "Self-locking nuts. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.0, "r_y0": 710.265, "r_x1": 368.128, "r_y1": 710.265, "r_x2": 368.128, "r_y2": 702.39, "r_x3": 321.0, "r_y3": 702.39, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27. ", "orig": "Figure 7-27. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.402, "r_y0": 709.796, "r_x1": 481.649, "r_y1": 709.796, "r_x2": 481.649, "r_y2": 702.2570000000001, "r_x3": 368.402, "r_y3": 702.2570000000001, "coord_origin": "TOPLEFT"}, "text": "Stainless steel self-locking nut. ", "orig": "Stainless steel self-locking nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.985, "r_y0": 739.604, "r_x1": 560.775, "r_y1": 739.604, "r_x2": 560.775, "r_y2": 730.1, "r_x3": 537.985, "r_y3": 730.1, "coord_origin": "TOPLEFT"}, "text": "7-45 ", "orig": "7-45 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "text", "bbox": {"l": 71.992, "t": 46.03399999999999, "r": 309.122, "b": 54.499000000000024, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 320.992, "t": 46.03399999999999, "r": 560.508, "b": 54.499000000000024, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 71.992, "t": 108.53399999999999, "r": 309.033, "b": 116.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 320.995, "t": 133.62400000000002, "r": 450.995, "b": 142.0, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 320.995, "t": 148.034, "r": 560.498, "b": 156.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 71.992, "t": 196.12400000000002, "r": 167.272, "b": 204.5, "coord_origin": "TOPLEFT"}, "confidence": 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"confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 71.992, "t": 298.034, "r": 309.03, "b": 306.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 320.995, "t": 373.124, "r": 388.505, "b": 381.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 71.992, "t": 385.534, "r": 309.042, "b": 393.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 320.995, "t": 387.534, "r": 558.104, "b": 395.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 71.992, "t": 498.034, "r": 311.502, "b": 506.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are 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", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. 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", "orig": "Figure 7-26. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.402, "r_y0": 711.816, "r_x1": 184.148, "r_y1": 711.816, "r_x2": 184.148, "r_y2": 704.277, "r_x3": 119.402, "r_y3": 704.277, "coord_origin": "TOPLEFT"}, "text": "Self-locking nuts. ", "orig": "Self-locking nuts. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.0, "r_y0": 710.265, "r_x1": 368.128, "r_y1": 710.265, "r_x2": 368.128, "r_y2": 702.39, "r_x3": 321.0, "r_y3": 702.39, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27. ", "orig": "Figure 7-27. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.402, "r_y0": 709.796, "r_x1": 481.649, "r_y1": 709.796, "r_x2": 481.649, "r_y2": 702.2570000000001, "r_x3": 368.402, "r_y3": 702.2570000000001, "coord_origin": "TOPLEFT"}, "text": "Stainless steel self-locking nut. ", "orig": "Stainless steel self-locking nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.985, "r_y0": 739.604, "r_x1": 560.775, "r_y1": 739.604, "r_x2": 560.775, "r_y2": 730.1, "r_x3": 537.985, "r_y3": 730.1, "coord_origin": "TOPLEFT"}, "text": "7-45 ", "orig": "7-45 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 6, "label": "text", "bbox": {"l": 71.992, "t": 210.534, "r": 311.548, "b": 281.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.212, "r_y0": 218.99900000000002, "r_x1": 311.548, "r_y1": 218.99900000000002, "r_x2": 311.548, "r_y2": 210.534, "r_x3": 174.212, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": " nut is of one piece, all-metal ", "orig": " nut is of one piece, all-metal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. 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", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 71.992, "t": 46.03399999999999, "r": 311.412, "b": 91.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 66.99900000000002, "r_x1": 309.002, "r_y1": 66.99900000000002, "r_x2": 309.002, "r_y2": 58.53399999999999, "r_x3": 71.992, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "to the supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 71.992, "t": 108.53399999999999, "r": 311.445, "b": 179.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "section_header", "bbox": {"l": 71.992, "t": 196.12400000000002, "r": 167.272, "b": 204.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 71.992, "t": 298.034, "r": 311.519, "b": 368.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 71.992, "t": 385.534, "r": 311.492, "b": 481.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 71.992, "t": 498.034, "r": 311.502, "b": 518.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 320.992, "t": 46.03399999999999, "r": 560.517, "b": 116.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 320.995, "t": 121.03399999999999, "r": 325.995, "b": 129.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "section_header", "bbox": {"l": 320.995, "t": 133.62400000000002, "r": 450.995, "b": 142.0, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "text", "bbox": {"l": 320.995, "t": 148.034, "r": 560.513, "b": 356.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "section_header", "bbox": {"l": 320.995, "t": 373.124, "r": 388.505, "b": 381.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "text", "bbox": {"l": 320.995, "t": 387.534, "r": 558.104, "b": 408.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.136, "r_y0": 682.01, "r_x1": 531.167, "r_y1": 682.01, "r_x2": 531.167, "r_y2": 675.255, "r_x3": 479.136, "r_y3": 675.255, "coord_origin": "TOPLEFT"}, "text": "Tightened nut ", "orig": "Tightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.37, "r_y0": 541.167, "r_x1": 535.234, "r_y1": 541.167, "r_x2": 535.234, "r_y2": 534.412, "r_x3": 474.37, "r_y3": 534.412, "coord_origin": "TOPLEFT"}, "text": "Untightened nut ", "orig": "Untightened nut ", "text_direction": "left_to_right", "confidence": 1.0, 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", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.402, "r_y0": 709.796, "r_x1": 481.649, "r_y1": 709.796, "r_x2": 481.649, "r_y2": 702.2570000000001, "r_x3": 368.402, "r_y3": 702.2570000000001, "coord_origin": "TOPLEFT"}, "text": "Stainless steel self-locking nut. ", "orig": "Stainless steel self-locking nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 537.985, "t": 730.1, "r": 560.775, "b": 739.604, "coord_origin": "TOPLEFT"}, "confidence": 0.9368568658828735, "cells": [{"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.985, "r_y0": 739.604, "r_x1": 560.775, "r_y1": 739.604, "r_x2": 560.775, "r_y2": 730.1, "r_x3": 537.985, "r_y3": 730.1, "coord_origin": "TOPLEFT"}, "text": "7-45 ", "orig": "7-45 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "text", "bbox": {"l": 71.992, "t": 210.534, "r": 311.548, "b": 281.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.212, "r_y0": 218.99900000000002, "r_x1": 311.548, "r_y1": 218.99900000000002, "r_x2": 311.548, "r_y2": 210.534, "r_x3": 174.212, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": " nut is of one piece, all-metal ", "orig": " nut is of one piece, all-metal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"label": "text", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "text", "bbox": {"l": 71.992, "t": 46.03399999999999, "r": 311.412, "b": 91.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 66.99900000000002, "r_x1": 309.002, "r_y1": 66.99900000000002, "r_x2": 309.002, "r_y2": 58.53399999999999, "r_x3": 71.992, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "to the supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 71.992, "t": 108.53399999999999, "r": 311.445, "b": 179.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 71.992, "t": 196.12400000000002, "r": 167.272, "b": 204.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Boots Self-Locking Nut"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 71.992, "t": 298.034, "r": 311.519, "b": 368.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 71.992, "t": 385.534, "r": 311.492, "b": 481.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"label": "text", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "text", "bbox": {"l": 71.992, "t": 498.034, "r": 311.502, "b": 518.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"label": "text", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "text", "bbox": {"l": 320.992, "t": 46.03399999999999, "r": 560.517, "b": 116.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"label": "text", "id": 19, "page_no": 0, "cluster": {"id": 19, "label": "text", "bbox": {"l": 320.995, "t": 121.03399999999999, "r": 325.995, "b": 129.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "."}, {"label": "section_header", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 320.995, "t": 133.62400000000002, "r": 450.995, "b": 142.0, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Stainless Steel Self-Locking Nut"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.995, "t": 148.034, "r": 560.513, "b": 356.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"label": "section_header", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 320.995, "t": 373.124, "r": 388.505, "b": 381.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Elastic Stop Nut"}, {"label": "text", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "text", "bbox": {"l": 320.995, "t": 387.534, "r": 558.104, "b": 408.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.136, "r_y0": 682.01, "r_x1": 531.167, "r_y1": 682.01, "r_x2": 531.167, "r_y2": 675.255, "r_x3": 479.136, "r_y3": 675.255, "coord_origin": "TOPLEFT"}, "text": "Tightened nut ", "orig": "Tightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.37, "r_y0": 541.167, "r_x1": 535.234, "r_y1": 541.167, "r_x2": 535.234, "r_y2": 534.412, "r_x3": 474.37, "r_y3": 534.412, "coord_origin": "TOPLEFT"}, "text": "Untightened nut ", "orig": "Untightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": 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", "orig": "Figure 7-26. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 112, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 119.402, "r_y0": 711.816, "r_x1": 184.148, "r_y1": 711.816, "r_x2": 184.148, "r_y2": 704.277, "r_x3": 119.402, "r_y3": 704.277, "coord_origin": "TOPLEFT"}, "text": "Self-locking nuts. ", "orig": "Self-locking nuts. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7-26. Self-locking nuts."}, {"label": "caption", "id": 11, "page_no": 0, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 321.0, "t": 702.2570000000001, "r": 481.649, "b": 710.265, "coord_origin": "TOPLEFT"}, "confidence": 0.9497622847557068, "cells": [{"index": 113, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.0, "r_y0": 710.265, "r_x1": 368.128, "r_y1": 710.265, "r_x2": 368.128, "r_y2": 702.39, "r_x3": 321.0, "r_y3": 702.39, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27. ", "orig": "Figure 7-27. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 114, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 368.402, "r_y0": 709.796, "r_x1": 481.649, "r_y1": 709.796, "r_x2": 481.649, "r_y2": 702.2570000000001, "r_x3": 368.402, "r_y3": 702.2570000000001, "coord_origin": "TOPLEFT"}, "text": "Stainless steel self-locking nut. ", "orig": "Stainless steel self-locking nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 7-27. Stainless steel self-locking nut."}, {"label": "page_footer", "id": 13, "page_no": 0, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 537.985, "t": 730.1, "r": 560.775, "b": 739.604, "coord_origin": "TOPLEFT"}, "confidence": 0.9368568658828735, "cells": [{"index": 115, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 537.985, "r_y0": 739.604, "r_x1": 560.775, "r_y1": 739.604, "r_x2": 560.775, "r_y2": 730.1, "r_x3": 537.985, "r_y3": 730.1, "coord_origin": "TOPLEFT"}, "text": "7-45 ", "orig": "7-45 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "7-45"}], "body": [{"label": "text", "id": 6, "page_no": 0, "cluster": {"id": 6, "label": "text", "bbox": {"l": 71.992, "t": 210.534, "r": 311.548, "b": 281.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9866572022438049, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.212, "r_y0": 218.99900000000002, "r_x1": 311.548, "r_y1": 218.99900000000002, "r_x2": 311.548, "r_y2": 210.534, "r_x3": 174.212, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": " nut is of one piece, all-metal ", "orig": " nut is of one piece, all-metal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 218.99900000000002, "r_x1": 173.838, "r_y1": 218.99900000000002, "r_x2": 173.838, "r_y2": 210.534, "r_x3": 71.992, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "The Boots self-locking", "orig": "The Boots self-locking", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 231.49900000000002, "r_x1": 311.51, "r_y1": 231.49900000000002, "r_x2": 311.51, "r_y2": 223.034, "r_x3": 71.992, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "construction designed to hold tight despite severe vibration. ", "orig": "construction designed to hold tight despite severe vibration. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 243.99900000000002, "r_x1": 103.502, "r_y1": 243.99900000000002, "r_x2": 103.502, "r_y2": 235.534, "r_x3": 71.992, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "Note in ", "orig": "Note in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 102.862, "r_y0": 244.0, "r_x1": 150.172, "r_y1": 244.0, "r_x2": 150.172, "r_y2": 235.62400000000002, "r_x3": 102.862, "r_y3": 235.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Figure 7-26", "orig": "Figure 7-26", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 150.162, "r_y0": 243.99900000000002, "r_x1": 309.082, "r_y1": 243.99900000000002, "r_x2": 309.082, "r_y2": 235.534, "r_x3": 150.162, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": " that it has two sections and is essentially", "orig": " that it has two sections and is essentially", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 256.499, "r_x1": 311.519, "r_y1": 256.499, "r_x2": 311.519, "r_y2": 248.034, "r_x3": 71.992, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "two nuts in one: a locking nut and a load-carrying nut. The ", "orig": "two nuts in one: a locking nut and a load-carrying nut. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 268.999, "r_x1": 309.042, "r_y1": 268.999, "r_x2": 309.042, "r_y2": 260.534, "r_x3": 71.992, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "two sections are connected with a spring, which is an integral", "orig": "two sections are connected with a spring, which is an integral", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 281.499, "r_x1": 133.372, "r_y1": 281.499, "r_x2": 133.372, "r_y2": 273.034, "r_x3": 71.992, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "part of the nut. ", "orig": "part of the nut. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "nut is of one piece, all-metal The Boots self-locking construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut."}, {"label": "text", "id": 8, "page_no": 0, "cluster": {"id": 8, "label": "text", "bbox": {"l": 71.992, "t": 46.03399999999999, "r": 311.412, "b": 91.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9851651191711426, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 54.499000000000024, "r_x1": 309.122, "r_y1": 54.499000000000024, "r_x2": 309.122, "r_y2": 46.03399999999999, "r_x3": 71.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "pulleys, provided the inner race of the bearing is clamped", "orig": "pulleys, provided the inner race of the bearing is clamped", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 66.99900000000002, "r_x1": 309.002, "r_y1": 66.99900000000002, "r_x2": 309.002, "r_y2": 58.53399999999999, "r_x3": 71.992, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "to the supporting structure by the nut and bolt. Plates must", "orig": "to the supporting structure by the nut and bolt. Plates must", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 79.49900000000002, "r_x1": 309.022, "r_y1": 79.49900000000002, "r_x2": 309.022, "r_y2": 71.03399999999999, "r_x3": 71.992, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "be attached to the structure in a positive manner to eliminate", "orig": "be attached to the structure in a positive manner to eliminate", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 91.99900000000002, "r_x1": 311.412, "r_y1": 91.99900000000002, "r_x2": 311.412, "r_y2": 83.53399999999999, "r_x3": 71.992, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "rotation or misalignment when tightening the bolts or screws. ", "orig": "rotation or misalignment when tightening the bolts or screws. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws."}, {"label": "text", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "text", "bbox": {"l": 71.992, "t": 108.53399999999999, "r": 311.445, "b": 179.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9873439073562622, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 116.99900000000002, "r_x1": 309.033, "r_y1": 116.99900000000002, "r_x2": 309.033, "r_y2": 108.53399999999999, "r_x3": 71.992, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "The two general types of self-locking nuts currently in use", "orig": "The two general types of self-locking nuts currently in use", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 129.49900000000002, "r_x1": 309.003, "r_y1": 129.49900000000002, "r_x2": 309.003, "r_y2": 121.03399999999999, "r_x3": 71.993, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": "are the all-metal type and the fiber lock type. For the sake of", "orig": "are the all-metal type and the fiber lock type. For the sake of", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 141.99900000000002, "r_x1": 309.023, "r_y1": 141.99900000000002, "r_x2": 309.023, "r_y2": 133.534, "r_x3": 71.993, "r_y3": 133.534, "coord_origin": "TOPLEFT"}, "text": "simplicity, only three typical kinds of self-locking nuts are", "orig": "simplicity, only three typical kinds of self-locking nuts are", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 154.49900000000002, "r_x1": 308.993, "r_y1": 154.49900000000002, "r_x2": 308.993, "r_y2": 146.034, "r_x3": 71.993, "r_y3": 146.034, "coord_origin": "TOPLEFT"}, "text": "considered in this handbook: the Boots self-locking and the", "orig": "considered in this handbook: the Boots self-locking and the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 166.99900000000002, "r_x1": 309.053, "r_y1": 166.99900000000002, "r_x2": 309.053, "r_y2": 158.534, "r_x3": 71.993, "r_y3": 158.534, "coord_origin": "TOPLEFT"}, "text": "stainless steel self-locking nuts, representing the all-metal", "orig": "stainless steel self-locking nuts, representing the all-metal", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.993, "r_y0": 179.49900000000002, "r_x1": 311.445, "r_y1": 179.49900000000002, "r_x2": 311.445, "r_y2": 171.034, "r_x3": 71.993, "r_y3": 171.034, "coord_origin": "TOPLEFT"}, "text": "types; and the elastic stop nut, representing the fiber insert type. ", "orig": "types; and the elastic stop nut, representing the fiber insert type. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type."}, {"label": "section_header", "id": 14, "page_no": 0, "cluster": {"id": 14, "label": "section_header", "bbox": {"l": 71.992, "t": 196.12400000000002, "r": 167.272, "b": 204.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9309079647064209, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 204.5, "r_x1": 167.272, "r_y1": 204.5, "r_x2": 167.272, "r_y2": 196.12400000000002, "r_x3": 71.992, "r_y3": 196.12400000000002, "coord_origin": "TOPLEFT"}, "text": "Boots Self-Locking Nut ", "orig": "Boots Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Boots Self-Locking Nut"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 71.992, "t": 298.034, "r": 311.519, "b": 368.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9881331324577332, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 306.499, "r_x1": 309.03, "r_y1": 306.499, "r_x2": 309.03, "r_y2": 298.034, "r_x3": 71.992, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "The spring keeps the locking and load-carrying sections such", "orig": "The spring keeps the locking and load-carrying sections such", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 318.999, "r_x1": 311.487, "r_y1": 318.999, "r_x2": 311.487, "r_y2": 310.534, "r_x3": 71.992, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "a distance apart that the two sets of threads are out of phase ", "orig": "a distance apart that the two sets of threads are out of phase ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 331.499, "r_x1": 311.487, "r_y1": 331.499, "r_x2": 311.487, "r_y2": 323.034, "r_x3": 71.992, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "or spaced so that a bolt, which has been screwed through the ", "orig": "or spaced so that a bolt, which has been screwed through the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 343.999, "r_x1": 309.082, "r_y1": 343.999, "r_x2": 309.082, "r_y2": 335.534, "r_x3": 71.992, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "load-carrying section, must push the locking section outward", "orig": "load-carrying section, must push the locking section outward", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 356.499, "r_x1": 311.519, "r_y1": 356.499, "r_x2": 311.519, "r_y2": 348.034, "r_x3": 71.992, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "against the force of the spring to engage the threads of the ", "orig": "against the force of the spring to engage the threads of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 368.999, "r_x1": 174.202, "r_y1": 368.999, "r_x2": 174.202, "r_y2": 360.534, "r_x3": 71.992, "r_y3": 360.534, "coord_origin": "TOPLEFT"}, "text": "locking section properly. ", "orig": "locking section properly. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly."}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 71.992, "t": 385.534, "r": 311.492, "b": 481.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9882921576499939, "cells": [{"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 393.999, "r_x1": 309.042, "r_y1": 393.999, "r_x2": 309.042, "r_y2": 385.534, "r_x3": 71.992, "r_y3": 385.534, "coord_origin": "TOPLEFT"}, "text": "The spring, through the medium of the locking section, exerts", "orig": "The spring, through the medium of the locking section, exerts", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 406.499, "r_x1": 308.892, "r_y1": 406.499, "r_x2": 308.892, "r_y2": 398.034, "r_x3": 71.992, "r_y3": 398.034, "coord_origin": "TOPLEFT"}, "text": "a constant locking force on the bolt in the same direction as a", "orig": "a constant locking force on the bolt in the same direction as a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 418.999, "r_x1": 308.872, "r_y1": 418.999, "r_x2": 308.872, "r_y2": 410.534, "r_x3": 71.992, "r_y3": 410.534, "coord_origin": "TOPLEFT"}, "text": "force that would tighten the nut. In this nut, the load-carrying", "orig": "force that would tighten the nut. In this nut, the load-carrying", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 431.499, "r_x1": 308.852, "r_y1": 431.499, "r_x2": 308.852, "r_y2": 423.034, "r_x3": 71.992, "r_y3": 423.034, "coord_origin": "TOPLEFT"}, "text": "section has the thread strength of a standard nut of comparable", "orig": "section has the thread strength of a standard nut of comparable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 443.999, "r_x1": 311.489, "r_y1": 443.999, "r_x2": 311.489, "r_y2": 435.534, "r_x3": 71.992, "r_y3": 435.534, "coord_origin": "TOPLEFT"}, "text": "size, while the locking section presses against the threads of ", "orig": "size, while the locking section presses against the threads of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 456.499, "r_x1": 311.482, "r_y1": 456.499, "r_x2": 311.482, "r_y2": 448.034, "r_x3": 71.992, "r_y3": 448.034, "coord_origin": "TOPLEFT"}, "text": "the bolt and locks the nut firmly in position. Only a wrench ", "orig": "the bolt and locks the nut firmly in position. Only a wrench ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 468.999, "r_x1": 311.492, "r_y1": 468.999, "r_x2": 311.492, "r_y2": 460.534, "r_x3": 71.992, "r_y3": 460.534, "coord_origin": "TOPLEFT"}, "text": "applied to the nut loosens it. The nut can be removed and ", "orig": "applied to the nut loosens it. The nut can be removed and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 481.499, "r_x1": 231.972, "r_y1": 481.499, "r_x2": 231.972, "r_y2": 473.034, "r_x3": 71.992, "r_y3": 473.034, "coord_origin": "TOPLEFT"}, "text": "reused without impairing its efficiency. ", "orig": "reused without impairing its efficiency. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency."}, {"label": "text", "id": 9, "page_no": 0, "cluster": {"id": 9, "label": "text", "bbox": {"l": 71.992, "t": 498.034, "r": 311.502, "b": 518.999, "coord_origin": "TOPLEFT"}, "confidence": 0.9695363640785217, "cells": [{"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 506.499, "r_x1": 311.502, "r_y1": 506.499, "r_x2": 311.502, "r_y2": 498.034, "r_x3": 71.992, "r_y3": 498.034, "coord_origin": "TOPLEFT"}, "text": "Boots self-locking nuts are made with three different spring ", "orig": "Boots self-locking nuts are made with three different spring ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 71.992, "r_y0": 518.999, "r_x1": 311.492, "r_y1": 518.999, "r_x2": 311.492, "r_y2": 510.534, "r_x3": 71.992, "r_y3": 510.534, "coord_origin": "TOPLEFT"}, "text": "styles and in various shapes and sizes. The wing type that is ", "orig": "styles and in various shapes and sizes. The wing type that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is"}, {"label": "text", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "text", "bbox": {"l": 320.992, "t": 46.03399999999999, "r": 560.517, "b": 116.99900000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9870070219039917, "cells": [{"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.992, "r_y0": 54.499000000000024, "r_x1": 560.508, "r_y1": 54.499000000000024, "r_x2": 560.508, "r_y2": 46.03399999999999, "r_x3": 320.992, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "orig": "the most common ranges in size for No. 6 up to 1 4 inch, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 516.813, "r_y0": 54.499000000000024, "r_x1": 518.483, "r_y1": 54.499000000000024, "r_x2": 518.483, "r_y2": 46.03399999999999, "r_x3": 516.813, "r_y3": 46.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.998, "r_y0": 66.99900000000002, "r_x1": 443.867, "r_y1": 66.99900000000002, "r_x2": 443.867, "r_y2": 58.53399999999999, "r_x3": 320.998, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "Rol-top ranges from 1 4 inch to ", "orig": "Rol-top ranges from 1 4 inch to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.24, "r_y0": 66.99900000000002, "r_x1": 408.91, "r_y1": 66.99900000000002, "r_x2": 408.91, "r_y2": 58.53399999999999, "r_x3": 407.24, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.826, "r_y0": 62.96799999999996, "r_x1": 446.576, "r_y1": 62.96799999999996, "r_x2": 446.576, "r_y2": 58.31299999999999, "r_x3": 443.826, "r_y3": 58.31299999999999, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.576, "r_y0": 66.99900000000002, "r_x1": 448.246, "r_y1": 66.99900000000002, "r_x2": 448.246, "r_y2": 58.53399999999999, "r_x3": 446.576, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 66, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.226, "r_y0": 66.168, "r_x1": 450.976, "r_y1": 66.168, "r_x2": 450.976, "r_y2": 61.513000000000034, "r_x3": 448.226, "r_y3": 61.513000000000034, "coord_origin": "TOPLEFT"}, "text": "6", "orig": "6", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 67, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 450.976, "r_y0": 66.99900000000002, "r_x1": 560.508, "r_y1": 66.99900000000002, "r_x2": 560.508, "r_y2": 58.53399999999999, "r_x3": 450.976, "r_y3": 58.53399999999999, "coord_origin": "TOPLEFT"}, "text": " inch, and the bellows type ", "orig": " inch, and the bellows type ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 68, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.996, "r_y0": 79.49900000000002, "r_x1": 448.626, "r_y1": 79.49900000000002, "r_x2": 448.626, "r_y2": 71.03399999999999, "r_x3": 320.996, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "ranges in size from No. 8 up to ", "orig": "ranges in size from No. 8 up to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 69, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 448.797, "r_y0": 75.46799999999996, "r_x1": 451.547, "r_y1": 75.46799999999996, "r_x2": 451.547, "r_y2": 70.81299999999999, "r_x3": 448.797, "r_y3": 70.81299999999999, "coord_origin": "TOPLEFT"}, "text": "3", "orig": "3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 70, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 451.506, "r_y0": 79.49900000000002, "r_x1": 453.176, "r_y1": 79.49900000000002, "r_x2": 453.176, "r_y2": 71.03399999999999, "r_x3": 451.506, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": "\u2044", "orig": "\u2044", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 453.175, "r_y0": 78.668, "r_x1": 455.925, "r_y1": 78.668, "r_x2": 455.925, "r_y2": 74.01300000000003, "r_x3": 453.175, "r_y3": 74.01300000000003, "coord_origin": "TOPLEFT"}, "text": "8", "orig": "8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 72, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 455.925, "r_y0": 79.49900000000002, "r_x1": 560.508, "r_y1": 79.49900000000002, "r_x2": 560.508, "r_y2": 71.03399999999999, "r_x3": 455.925, "r_y3": 71.03399999999999, "coord_origin": "TOPLEFT"}, "text": " inch. Wing-type nuts are ", "orig": " inch. Wing-type nuts are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 73, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 91.99900000000002, "r_x1": 560.498, "r_y1": 91.99900000000002, "r_x2": 560.498, "r_y2": 83.53399999999999, "r_x3": 320.995, "r_y3": 83.53399999999999, "coord_origin": "TOPLEFT"}, "text": "made of anodized aluminum alloy, cadmium-plated carbon ", "orig": "made of anodized aluminum alloy, cadmium-plated carbon ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 74, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 104.49900000000002, "r_x1": 560.517, "r_y1": 104.49900000000002, "r_x2": 560.517, "r_y2": 96.03399999999999, "r_x3": 320.995, "r_y3": 96.03399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "orig": "steel, or stainless steel. The Rol-top nut is cadmium-plated ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 75, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 116.99900000000002, "r_x1": 560.508, "r_y1": 116.99900000000002, "r_x2": 560.508, "r_y2": 108.53399999999999, "r_x3": 320.995, "r_y3": 108.53399999999999, "coord_origin": "TOPLEFT"}, "text": "steel, and the bellows type is made of aluminum alloy only. ", "orig": "steel, and the bellows type is made of aluminum alloy only. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "the most common ranges in size for No. 6 up to 1 4 inch, the / Rol-top ranges from 1 4 inch to / 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only."}, {"label": "text", "id": 19, "page_no": 0, "cluster": {"id": 19, "label": "text", "bbox": {"l": 320.995, "t": 121.03399999999999, "r": 325.995, "b": 129.49900000000002, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 76, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 129.49900000000002, "r_x1": 325.995, "r_y1": 129.49900000000002, "r_x2": 325.995, "r_y2": 121.03399999999999, "r_x3": 320.995, "r_y3": 121.03399999999999, "coord_origin": "TOPLEFT"}, "text": ". ", "orig": ". ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "."}, {"label": "section_header", "id": 15, "page_no": 0, "cluster": {"id": 15, "label": "section_header", "bbox": {"l": 320.995, "t": 133.62400000000002, "r": 450.995, "b": 142.0, "coord_origin": "TOPLEFT"}, "confidence": 0.9263731241226196, "cells": [{"index": 77, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 142.0, "r_x1": 450.995, "r_y1": 142.0, "r_x2": 450.995, "r_y2": 133.62400000000002, "r_x3": 320.995, "r_y3": 133.62400000000002, "coord_origin": "TOPLEFT"}, "text": "Stainless Steel Self-Locking Nut ", "orig": "Stainless Steel Self-Locking Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Stainless Steel Self-Locking Nut"}, {"label": "text", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "text", "bbox": {"l": 320.995, "t": 148.034, "r": 560.513, "b": 356.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9879258871078491, "cells": [{"index": 78, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 156.49900000000002, "r_x1": 560.498, "r_y1": 156.49900000000002, "r_x2": 560.498, "r_y2": 148.034, "r_x3": 320.995, "r_y3": 148.034, "coord_origin": "TOPLEFT"}, "text": "The stainless steel self-locking nut may be spun on and off ", "orig": "The stainless steel self-locking nut may be spun on and off ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 79, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 168.99900000000002, "r_x1": 557.905, "r_y1": 168.99900000000002, "r_x2": 557.905, "r_y2": 160.534, "r_x3": 320.995, "r_y3": 160.534, "coord_origin": "TOPLEFT"}, "text": "by hand as its locking action takes places only when the", "orig": "by hand as its locking action takes places only when the", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 80, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 181.49900000000002, "r_x1": 560.508, "r_y1": 181.49900000000002, "r_x2": 560.508, "r_y2": 173.034, "r_x3": 320.995, "r_y3": 173.034, "coord_origin": "TOPLEFT"}, "text": "nut is seated against a solid surface and tightened. The nut ", "orig": "nut is seated against a solid surface and tightened. The nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 81, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 193.99900000000002, "r_x1": 560.513, "r_y1": 193.99900000000002, "r_x2": 560.513, "r_y2": 185.534, "r_x3": 320.995, "r_y3": 185.534, "coord_origin": "TOPLEFT"}, "text": "consists of two parts: a case with a beveled locking shoulder ", "orig": "consists of two parts: a case with a beveled locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 82, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 206.49900000000002, "r_x1": 557.817, "r_y1": 206.49900000000002, "r_x2": 557.817, "r_y2": 198.034, "r_x3": 320.995, "r_y3": 198.034, "coord_origin": "TOPLEFT"}, "text": "and key and a thread insert with a locking shoulder and", "orig": "and key and a thread insert with a locking shoulder and", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 83, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 218.99900000000002, "r_x1": 560.506, "r_y1": 218.99900000000002, "r_x2": 560.506, "r_y2": 210.534, "r_x3": 320.995, "r_y3": 210.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway. Until the nut is tightened, it spins on the ", "orig": "slotted keyway. Until the nut is tightened, it spins on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 84, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 231.49900000000002, "r_x1": 557.805, "r_y1": 231.49900000000002, "r_x2": 557.805, "r_y2": 223.034, "r_x3": 320.995, "r_y3": 223.034, "coord_origin": "TOPLEFT"}, "text": "bolt easily, because the threaded insert is the proper size", "orig": "bolt easily, because the threaded insert is the proper size", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 85, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 243.99900000000002, "r_x1": 560.512, "r_y1": 243.99900000000002, "r_x2": 560.512, "r_y2": 235.534, "r_x3": 320.995, "r_y3": 235.534, "coord_origin": "TOPLEFT"}, "text": "for the bolt. However, when the nut is seated against a solid ", "orig": "for the bolt. However, when the nut is seated against a solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 86, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 256.499, "r_x1": 560.512, "r_y1": 256.499, "r_x2": 560.512, "r_y2": 248.034, "r_x3": 320.995, "r_y3": 248.034, "coord_origin": "TOPLEFT"}, "text": "surface and tightened, the locking shoulder of the insert is ", "orig": "surface and tightened, the locking shoulder of the insert is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 87, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 268.999, "r_x1": 560.491, "r_y1": 268.999, "r_x2": 560.491, "r_y2": 260.534, "r_x3": 320.995, "r_y3": 260.534, "coord_origin": "TOPLEFT"}, "text": "pulled downward and wedged against the locking shoulder ", "orig": "pulled downward and wedged against the locking shoulder ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 88, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 281.499, "r_x1": 560.495, "r_y1": 281.499, "r_x2": 560.495, "r_y2": 273.034, "r_x3": 320.995, "r_y3": 273.034, "coord_origin": "TOPLEFT"}, "text": "of the case. This action compresses the threaded insert and ", "orig": "of the case. This action compresses the threaded insert and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 89, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 293.999, "r_x1": 560.511, "r_y1": 293.999, "r_x2": 560.511, "r_y2": 285.534, "r_x3": 320.995, "r_y3": 285.534, "coord_origin": "TOPLEFT"}, "text": "causes it to clench the bolt tightly. The cross-sectional view ", "orig": "causes it to clench the bolt tightly. The cross-sectional view ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 90, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 306.499, "r_x1": 331.275, "r_y1": 306.499, "r_x2": 331.275, "r_y2": 298.034, "r_x3": 320.995, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": "in ", "orig": "in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 91, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 331.815, "r_y0": 306.5, "r_x1": 380.405, "r_y1": 306.5, "r_x2": 380.405, "r_y2": 298.124, "r_x3": 331.815, "r_y3": 298.124, "coord_origin": "TOPLEFT"}, "text": "Figure 7-27", "orig": "Figure 7-27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 92, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 380.405, "r_y0": 306.499, "r_x1": 560.505, "r_y1": 306.499, "r_x2": 560.505, "r_y2": 298.034, "r_x3": 380.405, "r_y3": 298.034, "coord_origin": "TOPLEFT"}, "text": " shows how the key of the case fits into the ", "orig": " shows how the key of the case fits into the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 93, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 318.999, "r_x1": 560.503, "r_y1": 318.999, "r_x2": 560.503, "r_y2": 310.534, "r_x3": 320.995, "r_y3": 310.534, "coord_origin": "TOPLEFT"}, "text": "slotted keyway of the insert so that when the case is turned, ", "orig": "slotted keyway of the insert so that when the case is turned, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 94, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 331.499, "r_x1": 558.024, "r_y1": 331.499, "r_x2": 558.024, "r_y2": 323.034, "r_x3": 320.995, "r_y3": 323.034, "coord_origin": "TOPLEFT"}, "text": "the threaded insert is turned with it. Note that the slot is wider", "orig": "the threaded insert is turned with it. Note that the slot is wider", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 95, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 343.999, "r_x1": 560.505, "r_y1": 343.999, "r_x2": 560.505, "r_y2": 335.534, "r_x3": 320.995, "r_y3": 335.534, "coord_origin": "TOPLEFT"}, "text": "than the key. This permits the slot to be narrowed and the ", "orig": "than the key. This permits the slot to be narrowed and the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 96, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 356.499, "r_x1": 523.195, "r_y1": 356.499, "r_x2": 523.195, "r_y2": 348.034, "r_x3": 320.995, "r_y3": 348.034, "coord_origin": "TOPLEFT"}, "text": "insert to be compressed when the nut is tightened. ", "orig": "insert to be compressed when the nut is tightened. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened."}, {"label": "section_header", "id": 16, "page_no": 0, "cluster": {"id": 16, "label": "section_header", "bbox": {"l": 320.995, "t": 373.124, "r": 388.505, "b": 381.5, "coord_origin": "TOPLEFT"}, "confidence": 0.9253152012825012, "cells": [{"index": 97, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 381.5, "r_x1": 388.505, "r_y1": 381.5, "r_x2": 388.505, "r_y2": 373.124, "r_x3": 320.995, "r_y3": 373.124, "coord_origin": "TOPLEFT"}, "text": "Elastic Stop Nut ", "orig": "Elastic Stop Nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Elastic Stop Nut"}, {"label": "text", "id": 10, "page_no": 0, "cluster": {"id": 10, "label": "text", "bbox": {"l": 320.995, "t": 387.534, "r": 558.104, "b": 408.499, "coord_origin": "TOPLEFT"}, "confidence": 0.9676451683044434, "cells": [{"index": 98, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 395.999, "r_x1": 558.104, "r_y1": 395.999, "r_x2": 558.104, "r_y2": 387.534, "r_x3": 320.995, "r_y3": 387.534, "coord_origin": "TOPLEFT"}, "text": "The elastic stop nut is a standard nut with the height", "orig": "The elastic stop nut is a standard nut with the height", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 99, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.995, "r_y0": 408.499, "r_x1": 558.081, "r_y1": 408.499, "r_x2": 558.081, "r_y2": 400.034, "r_x3": 320.995, "r_y3": 400.034, "coord_origin": "TOPLEFT"}, "text": "increased to accommodate a fiber locking collar. This", "orig": "increased to accommodate a fiber locking collar. This", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 320.4467468261719, "t": 421.640625, "r": 558.8576049804688, "b": 692.310791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9881086945533752, "cells": [{"index": 100, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 479.136, "r_y0": 682.01, "r_x1": 531.167, "r_y1": 682.01, "r_x2": 531.167, "r_y2": 675.255, "r_x3": 479.136, "r_y3": 675.255, "coord_origin": "TOPLEFT"}, "text": "Tightened nut ", "orig": "Tightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 101, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 474.37, "r_y0": 541.167, "r_x1": 535.234, "r_y1": 541.167, "r_x2": 535.234, "r_y2": 534.412, "r_x3": 474.37, "r_y3": 534.412, "coord_origin": "TOPLEFT"}, "text": "Untightened nut ", "orig": "Untightened nut ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 102, "rgba": 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"text": "Figure 7-26. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, +<_unknown_>function add(a, b) { return a + b; } console.log(add(3, 5)); +Listing 1: Simple JavaScript Program +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, 1 Formula -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir- -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie - -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. + +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. 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Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. 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Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.768, "t": 315.52, "r": 477.484, "b": 247.038, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 415]}], "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.768, "t": 243.789, "r": 477.484, "b": 175.30700000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 415]}], "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 2, "bbox": {"l": 303.133, "t": 146.058, "r": 308.114, "b": 137.35199999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [], "key_value_items": [], "form_items": [], "pages": {"1": {"size": {"width": 612.0, "height": 792.0}, "image": null, "page_no": 1}, "2": {"size": {"width": 595.276, "height": 841.89}, "image": null, "page_no": 2}}} \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.md b/tests/data/groundtruth/docling_v2/code_and_formula.md index 6044d723..e1bd20e0 100644 --- a/tests/data/groundtruth/docling_v2/code_and_formula.md +++ b/tests/data/groundtruth/docling_v2/code_and_formula.md @@ -1,27 +1,27 @@ ## JavaScript Code Example -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir- +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, ``` -function +function add(a, b) { return a + b; } console.log(add(3, 5)); ``` -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, ## Formula -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir- +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie \ No newline at end of file +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/code_and_formula.pages.json b/tests/data/groundtruth/docling_v2/code_and_formula.pages.json index b0b1c2aa..d030dca4 100644 --- a/tests/data/groundtruth/docling_v2/code_and_formula.pages.json +++ b/tests/data/groundtruth/docling_v2/code_and_formula.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 137.51599999999996, "r_x1": 315.916, "r_y1": 137.51599999999996, "r_x2": 315.916, "r_y2": 124.92399999999998, "r_x3": 133.768, "r_y3": 124.92399999999998, "coord_origin": "TOPLEFT"}, "text": "JavaScript Code Example", "orig": "JavaScript Code Example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 158.48800000000006, "r_x1": 477.48, "r_y1": 158.48800000000006, "r_x2": 477.48, "r_y2": 149.78200000000004, "r_x3": 133.768, "r_y3": 149.78200000000004, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 170.44299999999998, "r_x1": 477.481, "r_y1": 170.44299999999998, "r_x2": 477.481, "r_y2": 161.73699999999997, "r_x3": 133.768, "r_y3": 161.73699999999997, "coord_origin": "TOPLEFT"}, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "orig": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 182.39800000000002, "r_x1": 150.927, "r_y1": 182.39800000000002, "r_x2": 150.927, "r_y2": 173.692, "r_x3": 133.768, "r_y3": 173.692, "coord_origin": "TOPLEFT"}, "text": "tua.", "orig": "tua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.436, "r_y0": 182.39800000000002, "r_x1": 408.928, "r_y1": 182.39800000000002, "r_x2": 408.928, "r_y2": 173.692, "r_x3": 156.436, "r_y3": 173.692, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.437, "r_y0": 182.39800000000002, "r_x1": 477.476, "r_y1": 182.39800000000002, "r_x2": 477.476, "r_y2": 173.692, "r_x3": 414.437, "r_y3": 173.692, "coord_origin": "TOPLEFT"}, "text": "Stet clita kasd", "orig": "Stet clita kasd", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 194.35299999999995, "r_x1": 477.475, "r_y1": 194.35299999999995, "r_x2": 477.475, "r_y2": 185.64700000000005, "r_x3": 133.768, "r_y3": 185.64700000000005, "coord_origin": "TOPLEFT"}, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "orig": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 206.308, "r_x1": 477.477, "r_y1": 206.308, "r_x2": 477.477, "r_y2": 197.60199999999998, "r_x3": 133.768, "r_y3": 197.60199999999998, "coord_origin": "TOPLEFT"}, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "orig": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 218.26300000000003, "r_x1": 440.023, "r_y1": 218.26300000000003, "r_x2": 440.023, "r_y2": 209.55700000000002, "r_x3": 133.768, "r_y3": 209.55700000000002, "coord_origin": "TOPLEFT"}, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "invidunt ut labore et dolore magna aliquyam 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.608, "r_x1": 477.482, "r_y1": 499.608, "r_x2": 477.482, "r_y2": 490.902, "r_x3": 133.768, "r_y3": 490.902, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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praesent luptatum zzril delenit augue duis dolore", "orig": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 337.815, "r_x1": 230.812, "r_y1": 337.815, "r_x2": 230.812, "r_y2": 329.10900000000004, "r_x3": 133.768, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "te feugait nulla facilisi.", "orig": "te feugait nulla facilisi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.976, "r_y0": 337.815, "r_x1": 477.481, "r_y1": 337.815, "r_x2": 477.481, "r_y2": 329.10900000000004, "r_x3": 234.976, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "orig": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 349.77099999999996, "r_x1": 326.216, "r_y1": 349.77099999999996, "r_x2": 326.216, "r_y2": 341.065, "r_x3": 133.768, "r_y3": 341.065, "coord_origin": "TOPLEFT"}, "text": "sed diam nonummy nibh euismod tincidunt.", "orig": "sed diam nonummy nibh euismod tincidunt.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.554, "r_y0": 373.681, "r_x1": 285.82, "r_y1": 373.681, "r_x2": 285.82, "r_y2": 364.97499999999997, "r_x3": 280.554, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 285.82, "r_y0": 368.996, "r_x1": 289.792, "r_y1": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 403.56899999999996, "r_x1": 432.467, "r_y1": 403.56899999999996, "r_x2": 432.467, "r_y2": 394.863, "r_x3": 133.768, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 403.56899999999996, "r_x1": 477.479, "r_y1": 403.56899999999996, "r_x2": 477.479, "r_y2": 394.863, "r_x3": 437.398, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 427.479, "r_x1": 477.476, "r_y1": 427.479, "r_x2": 477.476, "r_y2": 418.77299999999997, "r_x3": 133.768, "r_y3": 418.77299999999997, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 439.43399999999997, "r_x1": 186.966, "r_y1": 439.43399999999997, "r_x2": 186.966, "r_y2": 430.728, "r_x3": 133.768, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "ipsum dolor", "orig": "ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.938, "r_y0": 439.43399999999997, "r_x1": 202.509, "r_y1": 439.43399999999997, "r_x2": 202.509, "r_y2": 430.728, "r_x3": 191.938, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sit", "orig": "sit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 207.47, "r_y0": 439.43399999999997, "r_x1": 231.823, "r_y1": 439.43399999999997, "r_x2": 231.823, "r_y2": 430.728, "r_x3": 207.47, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "amet,", "orig": "amet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.203, "r_y0": 439.43399999999997, "r_x1": 282.117, "r_y1": 439.43399999999997, "r_x2": 282.117, "r_y2": 430.728, "r_x3": 237.203, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "consetetur", "orig": "consetetur", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 287.078, "r_y0": 439.43399999999997, "r_x1": 331.468, "r_y1": 439.43399999999997, "r_x2": 331.468, "r_y2": 430.728, "r_x3": 287.078, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sadipscing", "orig": "sadipscing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.439, "r_y0": 439.43399999999997, "r_x1": 356.946, "r_y1": 439.43399999999997, "r_x2": 356.946, "r_y2": 430.728, "r_x3": 336.439, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "elitr,", "orig": "elitr,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 362.316, "r_y0": 439.43399999999997, "r_x1": 376.208, "r_y1": 439.43399999999997, "r_x2": 376.208, "r_y2": 430.728, "r_x3": 362.316, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sed", "orig": "sed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.179, "r_y0": 439.43399999999997, "r_x1": 477.482, "r_y1": 439.43399999999997, "r_x2": 477.482, "r_y2": 430.728, "r_x3": 381.179, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "diam nonumy eirmod", "orig": "diam nonumy eirmod", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 451.39, "r_x1": 477.481, "r_y1": 451.39, "r_x2": 477.481, "r_y2": 442.68399999999997, "r_x3": 133.768, "r_y3": 442.68399999999997, "coord_origin": "TOPLEFT"}, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 475.3, "r_x1": 387.354, "r_y1": 475.3, "r_x2": 387.354, "r_y2": 466.594, "r_x3": 133.768, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 475.3, "r_x1": 477.478, "r_y1": 475.3, "r_x2": 477.478, "r_y2": 466.594, "r_x3": 393.571, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 487.255, 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"r_y2": 490.50399999999996, "r_x3": 414.054, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 511.16499999999996, "r_x1": 323.816, "r_y1": 511.16499999999996, "r_x2": 323.816, "r_y2": 502.459, "r_x3": 133.768, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 511.16499999999996, "r_x1": 371.404, "r_y1": 511.16499999999996, "r_x2": 371.404, "r_y2": 502.459, "r_x3": 330.72, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 511.16499999999996, "r_x1": 477.476, "r_y1": 511.16499999999996, "r_x2": 477.476, "r_y2": 502.459, "r_x3": 375.548, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 523.121, "r_x1": 351.485, "r_y1": 523.121, "r_x2": 351.485, "r_y2": 514.415, "r_x3": 133.768, "r_y3": 514.415, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 535.076, "r_x1": 477.475, "r_y1": 535.076, 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.608, "r_x1": 477.482, "r_y1": 499.608, "r_x2": 477.482, "r_y2": 490.902, "r_x3": 133.768, "r_y3": 490.902, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 511.564, "r_x1": 387.354, "r_y1": 511.564, "r_x2": 387.354, "r_y2": 502.858, "r_x3": 133.768, "r_y3": 502.858, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 511.564, "r_x1": 477.478, "r_y1": 511.564, "r_x2": 477.478, "r_y2": 502.858, "r_x3": 393.571, "r_y3": 502.858, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 523.519, "r_x1": 477.48, "r_y1": 523.519, "r_x2": 477.48, "r_y2": 514.813, "r_x3": 133.768, "r_y3": 514.813, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 535.4739999999999, "r_x1": 407.17, "r_y1": 535.4739999999999, "r_x2": 407.17, "r_y2": 526.768, "r_x3": 133.768, "r_y3": 526.768, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 535.4739999999999, "r_x1": 477.475, "r_y1": 535.4739999999999, "r_x2": 477.475, "r_y2": 526.768, "r_x3": 414.054, "r_y3": 526.768, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 547.429, "r_x1": 323.816, "r_y1": 547.429, "r_x2": 323.816, "r_y2": 538.723, "r_x3": 133.768, "r_y3": 538.723, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 547.429, "r_x1": 371.404, "r_y1": 547.429, "r_x2": 371.404, "r_y2": 538.723, "r_x3": 330.72, "r_y3": 538.723, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 547.429, "r_x1": 477.476, "r_y1": 547.429, "r_x2": 477.476, "r_y2": 538.723, "r_x3": 375.548, "r_y3": 538.723, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 65, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 559.384, "r_x1": 351.485, "r_y1": 559.384, "r_x2": 351.485, "r_y2": 550.678, "r_x3": 133.768, "r_y3": 550.678, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Lorem ip-", "orig": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.608, "r_x1": 477.482, "r_y1": 499.608, "r_x2": 477.482, "r_y2": 490.902, "r_x3": 133.768, "r_y3": 490.902, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 242.17399999999998, "r_x1": 477.478, "r_y1": 242.17399999999998, "r_x2": 477.478, "r_y2": 233.46799999999996, "r_x3": 393.571, "r_y3": 233.46799999999996, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 254.12900000000002, "r_x1": 477.48, "r_y1": 254.12900000000002, "r_x2": 477.48, "r_y2": 245.423, "r_x3": 133.768, "r_y3": 245.423, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 266.08399999999995, "r_x1": 407.17, "r_y1": 266.08399999999995, "r_x2": 407.17, "r_y2": 257.37800000000004, "r_x3": 133.768, "r_y3": 257.37800000000004, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 266.08399999999995, "r_x1": 477.475, "r_y1": 266.08399999999995, "r_x2": 477.475, "r_y2": 257.37800000000004, "r_x3": 414.054, "r_y3": 257.37800000000004, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 278.03999999999996, "r_x1": 323.816, "r_y1": 278.03999999999996, "r_x2": 323.816, "r_y2": 269.33399999999995, "r_x3": 133.768, "r_y3": 269.33399999999995, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 278.03999999999996, "r_x1": 371.404, "r_y1": 278.03999999999996, "r_x2": 371.404, "r_y2": 269.33399999999995, "r_x3": 330.72, "r_y3": 269.33399999999995, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 278.03999999999996, "r_x1": 477.476, "r_y1": 278.03999999999996, "r_x2": 477.476, "r_y2": 269.33399999999995, "r_x3": 375.548, "r_y3": 269.33399999999995, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 289.995, "r_x1": 351.485, "r_y1": 289.995, "r_x2": 351.485, "r_y2": 281.289, "r_x3": 133.768, "r_y3": 281.289, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.768, "t": 293.244, "r": 477.482, "b": 349.77099999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9853153228759766, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 301.94999999999993, "r_x1": 477.475, "r_y1": 301.94999999999993, "r_x2": 477.475, "r_y2": 293.244, "r_x3": 148.712, "r_y3": 293.244, "coord_origin": "TOPLEFT"}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 313.905, "r_x1": 477.482, "r_y1": 313.905, "r_x2": 477.482, "r_y2": 305.19899999999996, "r_x3": 133.768, "r_y3": 305.19899999999996, "coord_origin": "TOPLEFT"}, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "orig": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 325.86, "r_x1": 477.479, "r_y1": 325.86, "r_x2": 477.479, "r_y2": 317.154, "r_x3": 133.768, "r_y3": 317.154, "coord_origin": "TOPLEFT"}, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "orig": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 337.815, "r_x1": 230.812, "r_y1": 337.815, "r_x2": 230.812, "r_y2": 329.10900000000004, "r_x3": 133.768, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "te feugait nulla facilisi.", "orig": "te feugait nulla facilisi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.976, "r_y0": 337.815, "r_x1": 477.481, "r_y1": 337.815, "r_x2": 477.481, "r_y2": 329.10900000000004, "r_x3": 234.976, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "orig": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 349.77099999999996, "r_x1": 326.216, "r_y1": 349.77099999999996, "r_x2": 326.216, "r_y2": 341.065, "r_x3": 133.768, "r_y3": 341.065, "coord_origin": "TOPLEFT"}, "text": "sed diam nonummy nibh euismod tincidunt.", "orig": "sed diam nonummy nibh euismod tincidunt.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.554, "t": 362.902, "r": 330.697, "b": 373.681, "coord_origin": "TOPLEFT"}, "confidence": 0.8727474808692932, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.554, "r_y0": 373.681, "r_x1": 285.82, "r_y1": 373.681, "r_x2": 285.82, "r_y2": 364.97499999999997, "r_x3": 280.554, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 285.82, "r_y0": 368.996, "r_x1": 289.792, "r_y1": 368.996, "r_x2": 289.792, "r_y2": 362.902, "r_x3": 285.82, "r_y3": 362.902, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.504, "r_y0": 373.681, "r_x1": 330.697, "r_y1": 373.681, "r_x2": 330.697, "r_y2": 364.97499999999997, "r_x3": 292.504, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "+8 = 12", "orig": "+8 = 12", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a 2 +8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 382.90799999999996, "r": 477.482, "b": 523.121, "coord_origin": "TOPLEFT"}, "confidence": 0.9879696369171143, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 391.614, "r_x1": 477.478, "r_y1": 391.614, "r_x2": 477.478, "r_y2": 382.90799999999996, "r_x3": 148.712, "r_y3": 382.90799999999996, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 403.56899999999996, "r_x1": 432.467, "r_y1": 403.56899999999996, "r_x2": 432.467, "r_y2": 394.863, "r_x3": 133.768, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 403.56899999999996, "r_x1": 477.479, "r_y1": 403.56899999999996, "r_x2": 477.479, "r_y2": 394.863, "r_x3": 437.398, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 415.524, "r_x1": 174.725, "r_y1": 415.524, "r_x2": 174.725, "r_y2": 406.818, "r_x3": 133.768, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 415.524, "r_x1": 431.98, "r_y1": 415.524, "r_x2": 431.98, "r_y2": 406.818, "r_x3": 180.075, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 415.524, "r_x1": 477.476, "r_y1": 415.524, "r_x2": 477.476, "r_y2": 406.818, "r_x3": 437.319, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 427.479, "r_x1": 477.476, "r_y1": 427.479, "r_x2": 477.476, "r_y2": 418.77299999999997, "r_x3": 133.768, "r_y3": 418.77299999999997, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 475.3, "r_x1": 387.354, "r_y1": 475.3, "r_x2": 387.354, "r_y2": 466.594, "r_x3": 133.768, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 475.3, "r_x1": 477.478, "r_y1": 475.3, "r_x2": 477.478, "r_y2": 466.594, "r_x3": 393.571, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 487.255, "r_x1": 477.48, "r_y1": 487.255, "r_x2": 477.48, "r_y2": 478.549, "r_x3": 133.768, "r_y3": 478.549, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.21, "r_x1": 407.16, "r_y1": 499.21, "r_x2": 407.16, "r_y2": 490.50399999999996, "r_x3": 133.768, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 499.21, "r_x1": 477.475, "r_y1": 499.21, "r_x2": 477.475, "r_y2": 490.50399999999996, "r_x3": 414.054, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 511.16499999999996, "r_x1": 323.816, "r_y1": 511.16499999999996, "r_x2": 323.816, "r_y2": 502.459, "r_x3": 133.768, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 511.16499999999996, "r_x1": 371.404, "r_y1": 511.16499999999996, "r_x2": 371.404, "r_y2": 502.459, "r_x3": 330.72, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 511.16499999999996, "r_x1": 477.476, "r_y1": 511.16499999999996, "r_x2": 477.476, "r_y2": 502.459, "r_x3": 375.548, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 523.121, "r_x1": 351.485, "r_y1": 523.121, "r_x2": 351.485, "r_y2": 514.415, "r_x3": 133.768, "r_y3": 514.415, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "text", "bbox": {"l": 133.768, "t": 293.244, "r": 477.482, "b": 349.77099999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9853153228759766, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 301.94999999999993, "r_x1": 477.475, "r_y1": 301.94999999999993, "r_x2": 477.475, "r_y2": 293.244, "r_x3": 148.712, "r_y3": 293.244, "coord_origin": "TOPLEFT"}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 313.905, "r_x1": 477.482, "r_y1": 313.905, "r_x2": 477.482, "r_y2": 305.19899999999996, "r_x3": 133.768, "r_y3": 305.19899999999996, "coord_origin": "TOPLEFT"}, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "orig": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 325.86, "r_x1": 477.479, "r_y1": 325.86, "r_x2": 477.479, "r_y2": 317.154, "r_x3": 133.768, "r_y3": 317.154, "coord_origin": "TOPLEFT"}, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "orig": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 337.815, "r_x1": 230.812, "r_y1": 337.815, "r_x2": 230.812, "r_y2": 329.10900000000004, "r_x3": 133.768, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "te feugait nulla facilisi.", "orig": "te feugait nulla facilisi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.976, "r_y0": 337.815, "r_x1": 477.481, "r_y1": 337.815, "r_x2": 477.481, "r_y2": 329.10900000000004, "r_x3": 234.976, "r_y3": 329.10900000000004, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "orig": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 349.77099999999996, "r_x1": 326.216, "r_y1": 349.77099999999996, "r_x2": 326.216, "r_y2": 341.065, "r_x3": 133.768, "r_y3": 341.065, "coord_origin": "TOPLEFT"}, "text": "sed diam nonummy nibh euismod tincidunt.", "orig": "sed diam nonummy nibh euismod tincidunt.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt."}, {"label": "formula", "id": 6, "page_no": 1, "cluster": {"id": 6, "label": "formula", "bbox": {"l": 280.554, "t": 362.902, "r": 330.697, "b": 373.681, "coord_origin": "TOPLEFT"}, "confidence": 0.8727474808692932, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 280.554, "r_y0": 373.681, "r_x1": 285.82, "r_y1": 373.681, "r_x2": 285.82, "r_y2": 364.97499999999997, "r_x3": 280.554, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "a", "orig": "a", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 285.82, "r_y0": 368.996, "r_x1": 289.792, "r_y1": 368.996, "r_x2": 289.792, "r_y2": 362.902, "r_x3": 285.82, "r_y3": 362.902, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 292.504, "r_y0": 373.681, "r_x1": 330.697, "r_y1": 373.681, "r_x2": 330.697, "r_y2": 364.97499999999997, "r_x3": 292.504, "r_y3": 364.97499999999997, "coord_origin": "TOPLEFT"}, "text": "+8 = 12", "orig": "+8 = 12", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "a 2 +8 = 12"}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 382.90799999999996, "r": 477.482, "b": 523.121, "coord_origin": "TOPLEFT"}, "confidence": 0.9879696369171143, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 391.614, "r_x1": 477.478, "r_y1": 391.614, "r_x2": 477.478, "r_y2": 382.90799999999996, "r_x3": 148.712, "r_y3": 382.90799999999996, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 403.56899999999996, "r_x1": 432.467, "r_y1": 403.56899999999996, "r_x2": 432.467, "r_y2": 394.863, "r_x3": 133.768, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 403.56899999999996, "r_x1": 477.479, "r_y1": 403.56899999999996, "r_x2": 477.479, "r_y2": 394.863, "r_x3": 437.398, "r_y3": 394.863, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 415.524, "r_x1": 174.725, "r_y1": 415.524, "r_x2": 174.725, "r_y2": 406.818, "r_x3": 133.768, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 415.524, "r_x1": 431.98, "r_y1": 415.524, "r_x2": 431.98, "r_y2": 406.818, "r_x3": 180.075, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 415.524, "r_x1": 477.476, "r_y1": 415.524, "r_x2": 477.476, "r_y2": 406.818, "r_x3": 437.319, "r_y3": 406.818, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 427.479, "r_x1": 477.476, "r_y1": 427.479, "r_x2": 477.476, "r_y2": 418.77299999999997, "r_x3": 133.768, "r_y3": 418.77299999999997, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 439.43399999999997, "r_x1": 186.966, "r_y1": 439.43399999999997, "r_x2": 186.966, "r_y2": 430.728, "r_x3": 133.768, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "ipsum dolor", "orig": "ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.938, "r_y0": 439.43399999999997, "r_x1": 202.509, "r_y1": 439.43399999999997, "r_x2": 202.509, "r_y2": 430.728, "r_x3": 191.938, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sit", "orig": "sit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 207.47, "r_y0": 439.43399999999997, "r_x1": 231.823, "r_y1": 439.43399999999997, "r_x2": 231.823, "r_y2": 430.728, "r_x3": 207.47, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "amet,", "orig": "amet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.203, "r_y0": 439.43399999999997, "r_x1": 282.117, "r_y1": 439.43399999999997, "r_x2": 282.117, "r_y2": 430.728, "r_x3": 237.203, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "consetetur", "orig": "consetetur", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 287.078, "r_y0": 439.43399999999997, "r_x1": 331.468, "r_y1": 439.43399999999997, "r_x2": 331.468, "r_y2": 430.728, "r_x3": 287.078, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sadipscing", "orig": "sadipscing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.439, "r_y0": 439.43399999999997, "r_x1": 356.946, "r_y1": 439.43399999999997, "r_x2": 356.946, "r_y2": 430.728, "r_x3": 336.439, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "elitr,", "orig": "elitr,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 362.316, "r_y0": 439.43399999999997, "r_x1": 376.208, "r_y1": 439.43399999999997, "r_x2": 376.208, "r_y2": 430.728, "r_x3": 362.316, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "sed", "orig": "sed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.179, "r_y0": 439.43399999999997, "r_x1": 477.482, "r_y1": 439.43399999999997, "r_x2": 477.482, "r_y2": 430.728, "r_x3": 381.179, "r_y3": 430.728, "coord_origin": "TOPLEFT"}, "text": "diam nonumy eirmod", "orig": "diam nonumy eirmod", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 451.39, "r_x1": 477.481, "r_y1": 451.39, "r_x2": 477.481, "r_y2": 442.68399999999997, "r_x3": 133.768, "r_y3": 442.68399999999997, "coord_origin": "TOPLEFT"}, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 463.34499999999997, "r_x1": 477.482, "r_y1": 463.34499999999997, "r_x2": 477.482, "r_y2": 454.639, "r_x3": 133.768, "r_y3": 454.639, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 475.3, "r_x1": 387.354, "r_y1": 475.3, "r_x2": 387.354, "r_y2": 466.594, "r_x3": 133.768, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 475.3, "r_x1": 477.478, "r_y1": 475.3, "r_x2": 477.478, "r_y2": 466.594, "r_x3": 393.571, "r_y3": 466.594, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 487.255, "r_x1": 477.48, "r_y1": 487.255, "r_x2": 477.48, "r_y2": 478.549, "r_x3": 133.768, "r_y3": 478.549, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 499.21, "r_x1": 407.16, "r_y1": 499.21, "r_x2": 407.16, "r_y2": 490.50399999999996, "r_x3": 133.768, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 499.21, "r_x1": 477.475, "r_y1": 499.21, "r_x2": 477.475, "r_y2": 490.50399999999996, "r_x3": 414.054, "r_y3": 490.50399999999996, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 511.16499999999996, "r_x1": 323.816, "r_y1": 511.16499999999996, "r_x2": 323.816, "r_y2": 502.459, "r_x3": 133.768, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 511.16499999999996, "r_x1": 371.404, "r_y1": 511.16499999999996, "r_x2": 371.404, "r_y2": 502.459, "r_x3": 330.72, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 511.16499999999996, "r_x1": 477.476, "r_y1": 511.16499999999996, "r_x2": 477.476, "r_y2": 502.459, "r_x3": 375.548, "r_y3": 502.459, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 523.121, "r_x1": 351.485, "r_y1": 523.121, "r_x2": 351.485, "r_y2": 514.415, "r_x3": 133.768, "r_y3": 514.415, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "text", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "text", "bbox": {"l": 133.768, "t": 526.37, "r": 477.484, "b": 594.852, "coord_origin": "TOPLEFT"}, "confidence": 0.9845718145370483, "cells": [{"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 535.076, "r_x1": 477.475, "r_y1": 535.076, "r_x2": 477.475, "r_y2": 526.37, "r_x3": 148.712, "r_y3": 526.37, "coord_origin": "TOPLEFT"}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "orig": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 547.031, "r_x1": 477.482, "r_y1": 547.031, "r_x2": 477.482, "r_y2": 538.325, "r_x3": 133.768, "r_y3": 538.325, "coord_origin": "TOPLEFT"}, "text": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "orig": "consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 558.986, "r_x1": 477.479, "r_y1": 558.986, "r_x2": 477.479, "r_y2": 550.28, "r_x3": 133.768, "r_y3": 550.28, "coord_origin": "TOPLEFT"}, "text": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "orig": "iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 570.941, "r_x1": 230.812, "r_y1": 570.941, "r_x2": 230.812, "r_y2": 562.235, "r_x3": 133.768, "r_y3": 562.235, "coord_origin": "TOPLEFT"}, "text": "te feugait nulla facilisi.", "orig": "te feugait nulla facilisi.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 234.976, "r_y0": 570.941, "r_x1": 477.481, "r_y1": 570.941, "r_x2": 477.481, "r_y2": 562.235, "r_x3": 234.976, "r_y3": 562.235, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "orig": "Lorem ipsum dolor sit amet, consectetuer adipiscing elit,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 582.896, "r_x1": 477.484, "r_y1": 582.896, "r_x2": 477.484, "r_y2": 574.19, "r_x3": 133.768, "r_y3": 574.19, "coord_origin": "TOPLEFT"}, "text": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "orig": "sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 594.852, "r_x1": 173.064, "r_y1": 594.852, "r_x2": 173.064, "r_y2": 586.146, "r_x3": 133.768, "r_y3": 586.146, "coord_origin": "TOPLEFT"}, "text": "volutpat.", "orig": "volutpat.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. 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Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat."}], "headers": [{"label": "page_footer", "id": 7, "page_no": 1, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 303.133, "t": 695.832, "r": 308.114, "b": 704.538, "coord_origin": "TOPLEFT"}, "confidence": 0.8504500389099121, "cells": [{"index": 71, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.133, "r_y0": 704.538, "r_x1": 308.114, "r_y1": 704.538, "r_x2": 308.114, "r_y2": 695.832, "r_x3": 303.133, "r_y3": 695.832, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/example_01.html.json b/tests/data/groundtruth/docling_v2/example_01.html.json index d33e1932..3d741f68 100644 --- a/tests/data/groundtruth/docling_v2/example_01.html.json +++ b/tests/data/groundtruth/docling_v2/example_01.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "example_01", "origin": { "mimetype": "text/html", diff --git a/tests/data/groundtruth/docling_v2/example_01.html.md b/tests/data/groundtruth/docling_v2/example_01.html.md index 73031c09..d07a4b07 100644 --- a/tests/data/groundtruth/docling_v2/example_01.html.md +++ b/tests/data/groundtruth/docling_v2/example_01.html.md @@ -2,7 +2,7 @@ This is the first paragraph of the introduction. -## Background +### Background Some background information here. diff --git a/tests/data/groundtruth/docling_v2/example_02.html.json b/tests/data/groundtruth/docling_v2/example_02.html.json index 956b91e8..edf915fe 100644 --- a/tests/data/groundtruth/docling_v2/example_02.html.json +++ b/tests/data/groundtruth/docling_v2/example_02.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "example_02", "origin": { "mimetype": "text/html", diff --git a/tests/data/groundtruth/docling_v2/example_02.html.md b/tests/data/groundtruth/docling_v2/example_02.html.md index 2b0a2ceb..c4e4940e 100644 --- a/tests/data/groundtruth/docling_v2/example_02.html.md +++ b/tests/data/groundtruth/docling_v2/example_02.html.md @@ -2,7 +2,7 @@ This is the first paragraph of the introduction. -## Background +### Background Some background information here. diff --git a/tests/data/groundtruth/docling_v2/example_03.html.json b/tests/data/groundtruth/docling_v2/example_03.html.json index 0a7284c2..bdfaaebe 100644 --- a/tests/data/groundtruth/docling_v2/example_03.html.json +++ b/tests/data/groundtruth/docling_v2/example_03.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "example_03", "origin": { "mimetype": "text/html", diff --git a/tests/data/groundtruth/docling_v2/example_03.html.md b/tests/data/groundtruth/docling_v2/example_03.html.md index 90f2a009..7e4f5611 100644 --- a/tests/data/groundtruth/docling_v2/example_03.html.md +++ b/tests/data/groundtruth/docling_v2/example_03.html.md @@ -1,10 +1,10 @@ # Example Document -## Introduction +### Introduction This is the first paragraph of the introduction. -## Background +### Background Some background information here. @@ -18,7 +18,7 @@ Some background information here. 2. Nested ordered item 2 3. Second item in ordered list -## Data Table +### Data Table | Header 1 | Header 2 | Header 3 | |--------------|--------------|--------------| diff --git a/tests/data/groundtruth/docling_v2/example_04.html.json b/tests/data/groundtruth/docling_v2/example_04.html.json index 7da199b5..23c38904 100644 --- a/tests/data/groundtruth/docling_v2/example_04.html.json +++ b/tests/data/groundtruth/docling_v2/example_04.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "example_04", "origin": { "mimetype": "text/html", diff --git a/tests/data/groundtruth/docling_v2/example_04.html.md b/tests/data/groundtruth/docling_v2/example_04.html.md index f204a12a..e620a999 100644 --- a/tests/data/groundtruth/docling_v2/example_04.html.md +++ b/tests/data/groundtruth/docling_v2/example_04.html.md @@ -1,7 +1,7 @@ # Data Table with Rowspan and Colspan -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | |----------------------------|----------------------------|----------------------------| -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | -| Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | | Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/example_05.html.json b/tests/data/groundtruth/docling_v2/example_05.html.json index 6b3a4482..3c5c1f0e 100644 --- a/tests/data/groundtruth/docling_v2/example_05.html.json +++ b/tests/data/groundtruth/docling_v2/example_05.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "example_05", "origin": { "mimetype": "text/html", diff --git a/tests/data/groundtruth/docling_v2/example_05.html.md b/tests/data/groundtruth/docling_v2/example_05.html.md index fbb24b62..787f6d23 100644 --- a/tests/data/groundtruth/docling_v2/example_05.html.md +++ b/tests/data/groundtruth/docling_v2/example_05.html.md @@ -1,7 +1,7 @@ # Omitted html and body tags -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | |----------------------------|----------------------------|----------------------------| -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | -| Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | | Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/example_06.html.json b/tests/data/groundtruth/docling_v2/example_06.html.json index 4e99c62c..2f48245c 100644 --- a/tests/data/groundtruth/docling_v2/example_06.html.json +++ b/tests/data/groundtruth/docling_v2/example_06.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "example_06", "origin": { "mimetype": "text/html", diff --git a/tests/data/groundtruth/docling_v2/ipa20180000016.json b/tests/data/groundtruth/docling_v2/ipa20180000016.json index 8de2fb64..57381160 100644 --- a/tests/data/groundtruth/docling_v2/ipa20180000016.json +++ b/tests/data/groundtruth/docling_v2/ipa20180000016.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "ipa20180000016.xml", "origin": { "mimetype": "application/xml", diff --git a/tests/data/groundtruth/docling_v2/ipa20180000016.md b/tests/data/groundtruth/docling_v2/ipa20180000016.md index d1cd8c0b..b617f949 100644 --- a/tests/data/groundtruth/docling_v2/ipa20180000016.md +++ b/tests/data/groundtruth/docling_v2/ipa20180000016.md @@ -1,20 +1,20 @@ # LIGHT EMITTING DEVICE AND PLANT CULTIVATION METHOD -## ABSTRACT +### ABSTRACT Provided is a light emitting device that includes a light emitting element having a light emission peak wavelength ranging from 380 nm to 490 nm, and a fluorescent material excited by light from the light emitting element and emitting light having at a light emission peak wavelength ranging from 580 nm or more to less than 680 nm. The light emitting device emits light having a ratio R/B of a photon flux density R to a photon flux density B ranging from 2.0 to 4.0 and a ratio R/FR of the photon flux density R to a photon flux density FR ranging from 0.7 to 13.0, the photon flux density R being in a wavelength range of 620 nm or more and less than 700 nm, the photon flux density B being in a wavelength range of 380 nm or more and 490 nm or less, and the photon flux density FR being in a wavelength range of 700 nm or more and 780 nm or less. -## CROSS-REFERENCE TO RELATED APPLICATION +### CROSS-REFERENCE TO RELATED APPLICATION The application claims benefit of Japanese Patent Application No. 2016-128835 filed on Jun. 29, 2016, the entire disclosure of which is hereby incorporated by reference in its entirety. -## BACKGROUND +### BACKGROUND -## Technical Field +### Technical Field The present disclosure relates to a light emitting device and a plant cultivation method. -## Description of Related Art +### Description of Related Art With environmental changes due to climate change and other artificial disruptions, plant factories are expected to increase production efficiency of vegetables and be capable of adjusting production in order to make it possible to stably supply vegetables. Plant factories that are capable of artificial management can stably supply clean and safe vegetables to markets, and therefore are expected to be the next-generation industries. @@ -26,7 +26,7 @@ In plant factories, the light source used in place of sunlight affect a growth p For example, Japanese Unexamined Patent Publication No. 2009-125007 discloses a plant growth method. In this method, the plants is irradiated with light emitted from a first LED light emitting element and/or a second LED light emitting element at predetermined timings using a lighting apparatus including the first LED light emitting element emitting light having a wavelength region of 625 to 690 nm and the second LED light emitting element emitting light having a wavelength region of 420 to 490 nm in order to emit lights having sufficient intensities and different wavelengths from each other. -## SUMMARY +### SUMMARY However, even though plants are merely irradiated with lights having different wavelengths as in the plant growth method disclosed in Japanese Unexamined Patent Publication No. 2009-125007, the effect of promoting plant growth is not sufficient. Further improvement is required in promotion of plant growth. @@ -40,7 +40,7 @@ A second embodiment of the present disclosure is a plant cultivation method incl According to embodiments of the present disclosure, a light emitting device capable of promoting growth of plants and a plant cultivation method can be provided. -## BRIEF DESCRIPTION OF THE DRAWINGS +### BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a schematic cross sectional view of a light emitting device according to an embodiment of the present disclosure. @@ -50,11 +50,11 @@ FIG. 3 is a graph showing fresh weight (edible part) at the harvest time of each FIG. 4 is a graph showing nitrate nitrogen content in each plant grown by irradiating the plant with light from exemplary light emitting devices according to embodiments of the present disclosure and a comparative light emitting device. -## DETAILED DESCRIPTION +### DETAILED DESCRIPTION A light emitting device and a plant cultivation method according to the present invention will be described below based on an embodiment. However, the embodiment described below only exemplifies the technical concept of the present invention, and the present invention is not limited to the light emitting device and plant cultivation method described below. In the present specification, the relationship between the color name and the chromaticity coordinate, the relationship between the wavelength range of light and the color name of monochromatic light follows JIS Z8110. -### Light Emitting Device +#### Light Emitting Device An embodiment of the present disclosure is a light emitting device including a light emitting element having a light emission peak wavelength in a range of 380 nm or more and 490 nm or less (hereinafter sometimes referred to as a “region of from near ultraviolet to blue color”), and a first fluorescent material emitting light having at least one light emission peak wavelength in a range of 580 nm or more and less than 680 nm by being excited by light from the light emitting element. The light emitting device emits light having a ratio R/B of a photon flux density R to a photon flux density B within a range of 2.0 or more and 4.0 or less, and a ratio R/FR of the photon flux density R to a photon flux density FR within a range of 0.7 or more and 13.0 or less, where the photon flux density R is the number of light quanta (μmol·m⁻²·g⁻¹) incident per unit time and unit area in a wavelength range of 620 nm or more and less than 700 nm, the photon flux density B is the number of light quanta (μmol·m⁻²·g⁻¹) incident per unit time and unit area in a wavelength range of 380 nm or more and 490 nm or less, and the photon flux density FR is the number of light quanta (μmol·m⁻²·g⁻¹) incident per unit time and unit area in a wavelength range of 700 nm or more and 780 nm or less. @@ -84,7 +84,7 @@ For the above reasons, nitrogen is one of nutrients necessary for growth of plan It is preferred that the light emitting device 100 further include the second fluorescent material 72 having at least one light emission peak wavelength in a range of 680 nm or more and 800 nm or less by being excited by light from the light emitting element 10, wherein the R/FR ratio is within a range of 0.7 or more and 5.0 or less. The R/FR ratio is more preferably within a range of 0.7 or more and 2.0 or less. -### Light Emitting Element +#### Light Emitting Element The light emitting element 10 is used as an excitation light source, and is a light emitting element emitting light having a light emission peak wavelength in a range of 380 nm or more and 490 nm or less. As a result, a stable light emitting device having high efficiency, high linearity of output to input and strong mechanical impacts can be obtained. @@ -92,7 +92,7 @@ The range of the light emission peak wavelength of the light emitting element 10 The half value width of emission spectrum of the light emitting element 10 can be, for example, 30 nm or less. -### Fluorescent Member +#### Fluorescent Member The fluorescent member 50 used in the light emitting device 100 preferably includes the first fluorescent material 71 and a sealing material, and more preferably further includes the second fluorescent material 72. A thermoplastic resin and a thermosetting resin can be used as the sealing material. The fluorescent member 50 may contain other components such as a filler, a light stabilizer and a colorant, in addition to the fluorescent material and the sealing material. Examples of the filler include silica, barium titanate, titanium oxide and aluminum oxide. @@ -100,7 +100,7 @@ The content of other components other than the fluorescent material 70 and the s The total content of the fluorescent material 70 in the fluorescent member 50 can be, for example, 5 parts by mass or more and 300 parts by mass or less, per 100 parts by mass of the sealing material. The total content is preferably 10 parts by mass or more and 250 parts by mass or less, more preferably 15 parts by mass or more and 230 parts by mass or less, and still more preferably 15 parts by mass or more and 200 parts by mass or less. When the total content of the fluorescent material 70 in the fluorescent member 50 is within the above range, the light emitted from the light emitting element 10 can be efficiently subjected to wavelength conversion in the fluorescent material 70. -### First Fluorescent Material +#### First Fluorescent Material The first fluorescent material 71 is a fluorescent material that is excited by light from the light emitting element 10 and emits light having at least one light emission peak wavelength in a range of 580 nm or more and less than 680 nm. Examples of the first fluorescent material 71 include an Mn⁴⁺-activated fluorogermanate fluorescent material, an Eu²⁺-activated nitride fluorescent material, an Eu²⁺-activated alkaline earth sulfide fluorescent material and an Mn⁴⁺-activated halide fluorescent material. The first fluorescent material 71 may use one selected from those fluorescent materials and may use a combination of two or more thereof. The first fluorescent material preferably contains an Eu²⁺-activated nitride fluorescent material and an Mn⁴⁺-activated fluorogermanate fluorescent material. @@ -138,7 +138,7 @@ The first fluorescent material 71 preferably contains at least two fluorescent m In the case where the first fluorescent material 71 contains at least two fluorescent materials and two fluorescent materials are a MGF fluorescent material and a CASN fluorescent material, where a compounding ratio thereof (MGF fluorescent material:CASN fluorescent material) is preferably in a range of 50:50 or more and 99:1 or less, more preferably in a range of 60:40 or more and 97:3 or less, and still more preferably in a range of 70:30 or more and 96:4 or less, in mass ratio. In the case where the first fluorescent material contains two fluorescent materials, when those fluorescent materials are a MGF fluorescent material and a CASN fluorescent material and the mass ratio thereof is within the aforementioned range, the light emitted from the light emitting element 10 can be efficiently subjected to wavelength conversion in the first fluorescent material 71. In addition, the R/B ratio can be adjusted to within a range of 2.0 or more and 4.0 or less, and the R/FR ratio is easy to be adjusted to within a range of 0.7 or more and 13.0 or less. -### Second Fluorescent Material +#### Second Fluorescent Material The second fluorescent material 72 is a fluorescent material that is excited by the light from the light emitting element 10 and emits light having at least one light emission peak wavelength in a range of 680 nm or more and 800 nm or less. @@ -160,25 +160,25 @@ In the second fluorescent material 72, the value of the parameter y is preferabl The parameter x is an activation amount of Ce and the value of the parameter x is in a range of exceeding 0.0002 and less than 0.50 (0.0002<x<0.50), and the parameter y is an activation amount of Cr. When the value of the parameter y is in a range of exceeding 0.0001 and less than 0.05 (0.0001<y<0.05), the activation amount of Ce and the activation amount of Cr that are light emission centers contained in the crystal structure of the fluorescent material are within optimum ranges, the decrease of light emission intensity due to the decrease of light emission center can be suppressed, the decrease of light emission intensity due to concentration quenching caused by the increase of the activation amount can be suppressed, and light emission intensity can be enhanced. -### Production Method of Second Fluorescent Material +#### Production Method of Second Fluorescent Material A method for producing the second fluorescent material 72 includes the following method. A compound containing at least one rare earth element Ln selected from the group consisting of rare earth elements excluding Ce, a compound containing at least one element M selected from the group consisting of Al, Ga, and In, a compound containing Ce and a compound containing Cr are mixed such that, when the total molar composition ratio of the M is taken as 5 as the standard, in the case where the total molar composition ratio of Ln, Ce, and Nd is 3, the molar ratio of Ce is a product of 3 and a value of a parameter x, and the molar ratio of Cr is a product of 3 and a value of a parameter y, the value of the parameter x is in a range of exceeding 0.0002 and less than 0.50 and the value of the parameter y is in a range of exceeding 0.0001 and less than 0.05, thereby obtaining a raw material mixture, the raw material mixture is heat-treated, followed by classification and the like, thereby obtaining the second fluorescent material. -### Compound Containing Rare Earth Element Ln +#### Compound Containing Rare Earth Element Ln Examples of the compound containing rare earth element Ln include oxides, hydroxides, nitrides, oxynitrides, fluorides, and chlorides, that contain at least one rare earth element Ln selected from the group consisting of rare earth elements excluding Ce. Those compounds may be hydrates. At least a part of the compounds containing rare earth element may use a metal simple substance or an alloy containing rare earth element. The compound containing rare earth element is preferably a compound containing at least one rare earth element Ln selected from the group consisting of Y, Gd, Lu, La, Tb, and Pr. The compound containing rare earth element may be used alone or may be used as a combination of at least two compounds containing rare earth element. The compound containing rare earth element is preferably an oxide that does not contain elements other than the target composition, as compared with other materials. Examples of the oxide specifically include Y₂O₃, Gd₂O₃, Lu₂O₃, La₂O₃, Tb₄O₇ and Pr₆O₁₁. -### Compound Containing M +#### Compound Containing M Examples of the compound containing at least one element M selected from the group consisting of Al, Ga, and In include oxides, hydroxides, nitrides, oxynitrides, fluorides, and chlorides, that contain Al, Ga, or In. Those compounds may be hydrates. Furthermore, Al metal simple substance, Ga metal simple substance, In metal simple substance, Al alloy, Ga alloy or In alloy may be used, and metal simple substance or an alloy may be used in place of at least a part of the compound. The compound containing Al, Ga, or In may be used alone or may be used as a combination of two or more thereof. The compound containing at least one element selected from the group consisting of Al, Ga, and In is preferably an oxide. The reason for this is that an oxide that does not contain elements other than the target composition, as compared with other materials, and a fluorescent material having a target composition are easy to be obtained. When a compound containing elements other than the target composition has been used, residual impurity elements are sometimes present in the fluorescent material obtained. The residual impurity element becomes a killer factor in light emission, leading to the possibility of remarkable decrease of light emission intensity. Examples of the compound containing Al, Ga, or In specifically include Al₂O₃, Ga₂O₃, and In₂O₃. -### Compound Containing Ce and Compound Containing Cr +#### Compound Containing Ce and Compound Containing Cr Examples of the compound containing Ce or the compound containing Cr include oxides, hydroxides, nitrides, fluorides, and chlorides, that contain cerium (Ce) or chromium (Cr). Those compounds may be hydrates. Ce metal simple substance, Ce alloy, Cr metal simple substance, or Cr alloy may be used, and a metal simple substance or an alloy may be used in place of a part of the compound. The compound containing Ce or the compound containing Cr may be used alone or may be used as a combination of two or more thereof. The compound containing Ce or the compound containing Cr is preferably an oxide. The reason for this is that an oxide that does not contain elements other than the target composition, as compared with other materials, and a fluorescent material having a target composition are easy to be obtained. When a compound containing elements other than the target composition has been used, residual impurity elements are sometimes present in the fluorescent material obtained. The residual impurity element becomes a killer factor in light emission, leading to the possibility of remarkable decrease of light emission intensity. @@ -204,7 +204,7 @@ The atmosphere for heat-treating the raw material mixture is an inert atmosphere The fluorescent material obtained may be subjected to post-treatment steps such as a solid-liquid separation by a method such as cleaning or filtration, drying by a method such as vacuum drying, and classification by dry sieving. After those post-treatment steps, a fluorescent material having a desired average particle diameter is obtained. -### Other Fluorescent Materials +#### Other Fluorescent Materials The light emitting device 100 may contain other kinds of fluorescent materials, in addition to the first fluorescent material 71. @@ -250,21 +250,21 @@ GdAlO₃:Cr (x) The light emitting device 100 can be utilized as a light emitting device for plant cultivation that can activate photosynthesis of plants and promote growth of plants so as to have favorable form and weight. -### Plant Cultivation Method +#### Plant Cultivation Method The plant cultivation method of one embodiment of the present disclosure is a method for cultivating plants, including irradiating plants with light emitted from the light emitting device 100. In the plant cultivation method, plants can be irradiated with light from the light emitting device 100 in plant factories that are completely isolated from external environment and make it possible for artificial control. The kind of plants is not particularly limited. However, the light emitting device 100 of one embodiment of the present disclosure can activate photosynthesis of plants and promote growth of plants such that a stem, a leaf, a root, a fruit have favorable form and weight, and therefore is preferably applied to cultivation of vegetables, flowers that contain much chlorophyll performing photosynthesis. Examples of the vegetables include lettuces such as garden lettuce, curl lettuce, Lamb's lettuce, Romaine lettuce, endive, Lollo Rosso, Rucola lettuce, and frill lettuce; Asteraceae vegetables such as “shungiku” (chrysanthemum coronarium); morning glory vegetables such as spinach; Rosaceae vegetables such as strawberry; and flowers such as chrysanthemum, gerbera, rose, and tulip. -## EXAMPLES +### EXAMPLES The present invention is further specifically described below by Examples and Comparative Examples. -## Examples 1 to 5 +### Examples 1 to 5 -### First Fluorescent Material +#### First Fluorescent Material Two fluorescent materials of fluorogarmanate fluorescent material that is activated by Mn⁴⁺, having a light emission peak at 660 nm and fluorescent material containing silicon nitride that are activated by Eu²⁺, having a light emission peak at 660 nm were used as the first fluorescent material 71. In the first fluorescent material 71, a mass ratio of a MGF fluorescent material to a CASN fluorescent material (MGF:CASN) was 95:5. -### Second Fluorescent Material +#### Second Fluorescent Material Fluorescent material that is obtained by the following production method was used as the second fluorescent material 72. @@ -272,7 +272,7 @@ Fluorescent material that is obtained by the following production method was use The raw material mixture obtained was placed in an alumina crucible, and a lid was put on the alumina crucible. The raw material mixture was heat-treated at 1,500° C. for 10 hours in a reducing atmosphere of H₂: 3 vol % and N₂: 97 vol %. Thus, a calcined product was obtained. The calcined product was passed through a dry sieve to obtain a second fluorescent material. The second fluorescent material obtained was subjected to composition analysis by ICP-AES emission spectrometry using an inductively coupled plasma emission analyzer (manufactured by Perkin Elmer). The composition of the second fluorescent material obtained was (Y₀.₉₇₇Ce₀.₀₀₉Cr₀.₀₁₄)₃Al₅O₁₂ (hereinafter referred to as “YAG: Ce, Cr”). -### Light Emitting Device +#### Light Emitting Device Nitride semiconductor having a light emission peak wavelength of 450 nm was used as the light emitting element 10 in the light emitting device 100. @@ -280,17 +280,17 @@ Silicone resin was used as a sealing material constituting the fluorescent membe The resin composition was poured on the light emitting element 10 of a depressed portion of the molded article 40 to fill the depressed portion, and heated at 150° C. for 4 hours to cure the resin composition, thereby forming the fluorescent member 50. Thus, the light emitting device 100 as shown in FIG. 1 was produced in each of Examples 1 to 5. -## Comparative Example 1 +### Comparative Example 1 A light emitting device X including a semiconductor light emitting element having a light emission peak wavelength of 450 nm and a light emitting device Y including a semiconductor light emitting element having a light emission peak length of 660 nm were used, and the R/B ratio was adjusted to 2.5. -### Evaluation +#### Evaluation -### Photon Flux Density +#### Photon Flux Density Photon flux densities of lights emitted from the light emitting device 100 used in Examples 1 to 5 and the light emitting devices X and Y used in Comparative Example 1 were measured using a photon measuring device (LI-250A, manufactured by Li-COR). The photon flux density B, the photon flux density R, and the photon flux density FR of lights emitted from the light emitting devices used in each of the Examples and Comparative Example; the R/B ratio; and the R/FR ratio are shown in Table 1. FIG. 2 shows spectra showing the relationship between a wavelength and a relative photon flux density, in the light emitting devices used in each Example and Comparative Example. -### Plant Cultivation Test +#### Plant Cultivation Test The plant cultivation method includes a method of conducting by “growth period by RGB light source (hereinafter referred to as a first growth period)” and “growth period by light source for plant growth (hereinafter referred to as a second growth period)” using a light emitting device according to an embodiment of the present disclosure as a light source. @@ -306,21 +306,21 @@ The cultivation test was specifically conducted by the following method. Romaine lettuce (green romaine, produced by Nakahara Seed Co., Ltd.) was used as cultivation plant. -### First Growth Period +#### First Growth Period Urethane sponges (salad urethane, manufactured by M Hydroponic Research Co., Ltd.) having Romaine lettuce seeded therein were placed side by side on a plastic tray, and were irradiated with light from RGB-LED light source (manufactured by Shibasaki Inc.) to cultivate plants. The plants were cultivated for 16 days under the conditions of room temperature: 22 to 23° C., humidity: 50 to 60%, photon flux density from light emitting device: 100 μmol·m⁻²·s⁻¹ and daytime hour: 16 hours/day. Only water was given until germination, and after the germination (about 4 days later), a solution obtained by mixing Otsuka House #1 (manufactured by Otsuka Chemical Co., Ltd.) and Otsuka House #2 (manufactured by Otsuka Chemical Co., Ltd.) in a mass ratio of 3:2 and dissolving the mixture in water was used as a nutrient solution (Otsuka Formulation A). Conductivity of the nutrient was 1.5 ms·cm⁻¹. -### Second Growth Period +#### Second Growth Period After the first growth period, the plants were irradiated with light from the light emitting devices of Examples 1 to 5 and Comparative Example 1, and were subjected to hydroponics. The plants were cultivated for 19 days under the conditions of room temperature: 22 to 24° C., humidity: 60 to 70%, CO₂ concentration: 600 to 700 ppm, photon flux density from light emitting device: 125 μmol·m⁻²·s⁻¹ and daytime hour: 16 hours/day. Otsuka Formulation A was used as the nutrient solution. Conductivity of the nutrient was 1.5 ms·cm⁻¹. The values of the R/B and R/FR ratios of light for plant irradiation from each light emitting device in the second growth period are shown in Table 1. -### Measurement of Fresh Weight (Edible Part) +#### Measurement of Fresh Weight (Edible Part) The plants after cultivation were harvested, and wet weights of a terrestrial part and a root were measured. The wet weight of a terrestrial part of each of 6 cultivated plants having been subjected to hydroponics by irradiating with light from the light emitting devices of Examples 1 to 5 and Comparative Example 1 was measured as a fresh weight (edible part) (g). The results obtained are shown in Table 1 and FIG. 3. -### Measurement of Nitrate Nitrogen Content +#### Measurement of Nitrate Nitrogen Content The edible part (about 20 g) of each of the cultivated plants, from which a foot about 5 cm had been removed, was frozen with liquid nitrogen and crushed with a juice mixer (laboratory mixer LM-PLUS, manufactured by Osaka Chemical Co., Ltd.) for 1 minute. The resulting liquid was filtered with Miracloth (manufactured by Milipore), and the filtrate was centrifuged at 4° C. and 15,000 rpm for 5 minutes. The nitrate nitrogen content (mg/100 g) in the cultivated plant in the supernatant was measured using a portable reflection photometer system (product name: RQ flex system, manufactured by Merck) and a test paper (product name: Reflectoquant (registered trade mark), manufactured by Kanto Chemical Co., Inc.). The results are shown in Table 1 and FIG. 4. @@ -355,7 +355,7 @@ In addition, in the foregoing Detailed Description, various features may be grou The above disclosed subject matter shall be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments which fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure may be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description. -## CLAIMS +### CLAIMS 1. A light emitting device comprising: a light emitting element having a light emission peak wavelength in a range of 380 nm or more and 490 nm or less; and a fluorescent material that is excited by light from the light emitting element and emits light having at least one light emission peak wavelength in a range of 580 nm or more and less than 680 nm, wherein the light emitting device emits light having a ratio R/B of a photon flux density R to a photon flux density B within a range of 2.0 or more and 4.0 or less, and a ratio R/FR of the photon flux density R to a photon flux density FR within a range of 0.7 or more and 13.0 or less, wherein the photon flux density R is in a wavelength range of 620 nm or more and less than 700 nm, the photon flux density B is in a wavelength range of 380 nm or more and 490 nm or less, and the photon flux density FR is in a wavelength range of 700 nm or more and 780 nm or less. diff --git a/tests/data/groundtruth/docling_v2/ipa20200022300.json b/tests/data/groundtruth/docling_v2/ipa20200022300.json index e9acc5f2..93dee608 100644 --- a/tests/data/groundtruth/docling_v2/ipa20200022300.json +++ b/tests/data/groundtruth/docling_v2/ipa20200022300.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "ipa20200022300.xml", "origin": { "mimetype": "application/xml", diff --git a/tests/data/groundtruth/docling_v2/ipa20200022300.md b/tests/data/groundtruth/docling_v2/ipa20200022300.md index 24bf1c80..4dcf49dd 100644 --- a/tests/data/groundtruth/docling_v2/ipa20200022300.md +++ b/tests/data/groundtruth/docling_v2/ipa20200022300.md @@ -1,14 +1,14 @@ # SYSTEM FOR CONTROLLING THE OPERATION OF AN ACTUATOR MOUNTED ON A SEED PLANTING IMPLEMENT -## ABSTRACT +### ABSTRACT In one aspect, a system for controlling an operation of an actuator mounted on a seed planting implement may include an actuator configured to adjust a position of a row unit of the seed planting implement relative to a toolbar of the seed planting implement. The system may also include a flow restrictor fluidly coupled to a fluid chamber of the actuator, with the flow restrictor being configured to reduce a rate at which fluid is permitted to exit the fluid chamber in a manner that provides damping to the row unit. Furthermore, the system may include a valve fluidly coupled to the flow restrictor in a parallel relationship such that the valve is configured to permit the fluid exiting the fluid chamber to flow through the flow restrictor and the fluid entering the fluid chamber to bypass the flow restrictor. -## FIELD +### FIELD The present disclosure generally relates to seed planting implements and, more particularly, to systems for controlling the operation of an actuator mounted on a seed planting implement in a manner that provides damping to one or more components of the seed planting implement. -## BACKGROUND +### BACKGROUND Modern farming practices strive to increase yields of agricultural fields. In this respect, seed planting implements are towed behind a tractor or other work vehicle to deposit seeds in a field. For example, seed planting implements typically include one or more ground engaging tools or openers that form a furrow or trench in the soil. One or more dispensing devices of the seed planting implement may, in turn, deposit seeds into the furrow(s). After deposition of the seeds, a packer wheel may pack the soil on top of the deposited seeds. @@ -16,7 +16,7 @@ In certain instances, the packer wheel may also control the penetration depth of Accordingly, an improved system for controlling the operation of an actuator mounted on s seed planting implement to enhance the overall operation of the implement would be welcomed in the technology. -## BRIEF DESCRIPTION +### BRIEF DESCRIPTION Aspects and advantages of the technology will be set forth in part in the following description, or may be obvious from the description, or may be learned through practice of the technology. @@ -28,7 +28,7 @@ In a further aspect, the present subject matter is directed to a system for prov These and other features, aspects and advantages of the present technology will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the technology and, together with the description, serve to explain the principles of the technology. -## BRIEF DESCRIPTION OF THE DRAWINGS +### BRIEF DESCRIPTION OF THE DRAWINGS A full and enabling disclosure of the present technology, including the best mode thereof, directed to one of ordinary skill in the art, is set forth in the specification, which makes reference to the appended figures, in which: @@ -52,7 +52,7 @@ FIG. 9 illustrates a cross-sectional view of a further embodiment of a system fo Repeat use of reference characters in the present specification and drawings is intended to represent the same or analogous features or elements of the present technology. -## DETAILED DESCRIPTION +### DETAILED DESCRIPTION Reference now will be made in detail to embodiments of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents. @@ -114,7 +114,7 @@ Referring now to FIG. 9, a schematic view of a further embodiment of the system This written description uses examples to disclose the technology, including the best mode, and also to enable any person skilled in the art to practice the technology, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the technology is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims. -## CLAIMS +### CLAIMS 1. A system for controlling an operation of an actuator mounted on a seed planting implement, the system comprising: a toolbar; a row unit adjustably mounted on the toolbar; a fluid-driven actuator configured to adjust a position of the row unit relative to the toolbar, the fluid-driven actuator defining first and second fluid chambers; a flow restrictor fluidly coupled to the first fluid chamber, the flow restrictor being configured to reduce a rate at which fluid is permitted to exit the first fluid chamber in a manner that provides damping to the row unit; and a valve fluidly coupled to the first fluid chamber, the valve further being fluidly coupled to the flow restrictor in a parallel relationship such that the valve is configured to permit the fluid exiting the first fluid chamber to flow through the flow restrictor and the fluid entering the first fluid chamber to bypass the flow restrictor. diff --git a/tests/data/groundtruth/docling_v2/lorem_ipsum.docx.json b/tests/data/groundtruth/docling_v2/lorem_ipsum.docx.json index 8c1c47bd..4be50e63 100644 --- a/tests/data/groundtruth/docling_v2/lorem_ipsum.docx.json +++ b/tests/data/groundtruth/docling_v2/lorem_ipsum.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "lorem_ipsum", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", diff --git a/tests/data/groundtruth/docling_v2/mixed.md.md b/tests/data/groundtruth/docling_v2/mixed.md.md index 6cd5d52b..69fad98e 100644 --- a/tests/data/groundtruth/docling_v2/mixed.md.md +++ b/tests/data/groundtruth/docling_v2/mixed.md.md @@ -2,7 +2,7 @@ Some text -## Famous ducks +### Famous ducks Here is a table: diff --git a/tests/data/groundtruth/docling_v2/pa20010031492.json b/tests/data/groundtruth/docling_v2/pa20010031492.json index a91a4531..fc06619b 100644 --- a/tests/data/groundtruth/docling_v2/pa20010031492.json +++ b/tests/data/groundtruth/docling_v2/pa20010031492.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "pa20010031492.xml", "origin": { "mimetype": "application/xml", diff --git a/tests/data/groundtruth/docling_v2/pa20010031492.md b/tests/data/groundtruth/docling_v2/pa20010031492.md index 24700ce6..162946d1 100644 --- a/tests/data/groundtruth/docling_v2/pa20010031492.md +++ b/tests/data/groundtruth/docling_v2/pa20010031492.md @@ -1,6 +1,6 @@ # Assay reagent -## ABSTRACT +### ABSTRACT A cell-derived assay reagent prepared from cells which have been killed by treatment with an antibiotic selected from the bleomycin-phleomycin family of antibiotics but which retain a signal-generating metabolic activity such as bioluminescence. @@ -74,9 +74,9 @@ FIG. 7: Control cells, stationary phase. FIG. 8: Zeocin™ treated cells, stationary phase. -## EXAMPLE 1 +### EXAMPLE 1 -## (A) Inactivation of Bioluminescent E. coil Method +### (A) Inactivation of Bioluminescent E. coil Method 1. Bioluminescent genetically modified E. coil strain HB101 (E. coli HB101 made bioluminescent by transformation with a plasmid carrying the lux operon of Vibrio fischeri constructed by the method of Shaw and Kado, as described in Biotechnology 4: 560-564) were grown from a frozen stock in 5 ml of low salt medium (LB (5 g/ml NaCl)+glycerol+MgSO₄) for 24 hours. @@ -90,7 +90,7 @@ FIG. 8: Zeocin™ treated cells, stationary phase. FIG. 1 shows the effect of Zeocin™ treatment on the light output and viable count (per ml) of recombinant bioluminescent E. coil. Zeocin™ was added to a final concentration of 1.5 mg/ml at time zero. The number of viable cells in the culture was observed to decrease with increasing contact cells with Zeocin™, the culture being completely inactivated after 3 hours. The light output from the culture was observed to decrease gradually with increasing Zeocin™ contact time. -## (B) Production of Assay Reagent +### (B) Production of Assay Reagent Five hours after the addition of Zeocin™ or water the remaining bacterial cells in the Zeocin™ treated and control cultures were harvested by the centrifugation, washed (to remove traces of Zeocin™ from the Zeocin™ treated culture), re-centrifuged and resuspended in cryoprotectant to an OD₆₃₀ of 0.25. 200 μl aliquots of the cells in cryoprotectant were dispensed into single shot vials, and freeze dried. Freeze dried samples of the Zeocin™ treated cells and control cells were reconstituted in 0.2M sucrose to form assay reagents and the light output of the assay reagents measured at various times after reconstitution. @@ -98,9 +98,9 @@ The light output from assay reagent prepared from cells exposed to 1.5 mg/ml Zeo FIG. 2 shows the light output from five separate vials of reconstituted Zeocin™ treated assay reagent inactivated according to the method of Example 1(A) and processed into assay reagent as described in Example 1(B). Reconstitution solution was added at time zero and thereafter light output was observed to increase steadily before stabilising out at around 15 minutes after reconstitution. All five vials were observed to give similar light profiles after reconstitution. -## EXAMPLE 2 +### EXAMPLE 2 -## Sensitivity of Zeocin™ Treated Assay Reagent to Toxicant Method +### Sensitivity of Zeocin™ Treated Assay Reagent to Toxicant Method 1. Bioluminescent genetically modified E. coil strain HB101 (E. coli HB101 made bioluminescent by transformation with a plasmid carrying the lux operon of vibrio fischeri constructed by the method of Shaw and Kado, as described in Biotechnology 4: 560-564) was grown in fermenter as a batch culture in low salt medium (LB(5 g/ml NaCl)+glycerol+MgSO₄). @@ -160,9 +160,9 @@ Table 1: Sensitivity of the different assay reagents to ZnSo₄ expressed as EC The results of the toxicity assays indicate that Zeocin™ treatment does not significantly affect the sensitivity of a recombinant bioluminescent E. coli derived assay reagent to ZnSO₄. Similar results could be expected with other toxic substances which have an effect on signal-generating metabolic activities. -## EXAMPLE 3 +### EXAMPLE 3 -## Method to Determine Viable Count +### Method to Determine Viable Count 1. Samples of bacterial culture to be assayed for viable count were centrifuged at 10,000 rpm for 5 minutes to pellet the bacterial cells. @@ -174,7 +174,7 @@ The results of the toxicity assays indicate that Zeocin™ treatment does not si 5. The number of bacterial colonies present for each of the three aliquots at each of the serial dilutions were counted and the values averaged. Viable count was calculated per ml of bacterial culture. -## CLAIMS +### CLAIMS 1. A method of making a non-viable preparation of prokaryotic or eukaryotic cells, which preparation has a signal-generating metabolic activity, which method comprises contacting a viable culture of said cells having signal-generating metabolic activity with an antibiotic selected from the bleomycin/phleomycin family of antibiotics. diff --git a/tests/data/groundtruth/docling_v2/pftaps057006474.json b/tests/data/groundtruth/docling_v2/pftaps057006474.json index 6dffcd2d..4ebb899a 100644 --- a/tests/data/groundtruth/docling_v2/pftaps057006474.json +++ b/tests/data/groundtruth/docling_v2/pftaps057006474.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "pftaps057006474.txt", "origin": { "mimetype": "text/plain", diff --git a/tests/data/groundtruth/docling_v2/pftaps057006474.md b/tests/data/groundtruth/docling_v2/pftaps057006474.md index f7e302f2..4e8ae371 100644 --- a/tests/data/groundtruth/docling_v2/pftaps057006474.md +++ b/tests/data/groundtruth/docling_v2/pftaps057006474.md @@ -1,10 +1,10 @@ # Carbocation containing cyanine-type dye -## ABSTRACT +### ABSTRACT To provide a reagent with excellent stability under storage, which can detect a subject compound to be measured with higher specificity and sensitibity. Complexes of a compound represented by the general formula (IV): -## BACKGROUND OF THE INVENTION +### BACKGROUND OF THE INVENTION 1. Field of the Invention @@ -26,7 +26,7 @@ However, these known cyanine dyes emitting fluorescence via absorption or excita If the dyes are used as labeling agents and bonded to substances from living organisms such as antibodies for preparing complexes, the complexes are likely to be oxidized easily by environmental factors such as light, heat, moisture, atmospheric oxygen and the like or to be subjected to modification such as generating cross-links. Particularly in water, a modification such as hydrolysis is further accelerated, disadvantageously. Therefore, the practical use of these complexes as detecting reagents in carrying out the microassay of the components of living organisms has encountered difficulties because of their poor stability under storage. -## SUMMARY OF THE INVENTION +### SUMMARY OF THE INVENTION The present inventors have made various investigations so as to solve the above problems, and have found that a dye of a particular structure, more specifically a particular polymethine dye, and among others, a dye having an azulene skelton, are extremely stable even after the immobilization thereof as a labeling agent onto substances from living organisms. Thus, the inventors have achieved the present invention. It is an object of the present invention to provide a labeled complex with excellent storage stability which can overcome the above problems. @@ -38,11 +38,11 @@ According to another aspect of the present invention, there is provided a method According to still another aspect of the present invention, there is provided a method of detecting a subject compound to be analyzed by means of optical means which method comprises using a labeled complex comprised of a substance from a living organism and a labeling agent fixed onto the substance and bonding the complex to the subject compound to be analyzed, wherein the labeling agent comprises a compound represented by the general formula (iv). -## BRIEF DESCRIPTION OF THE DRAWINGS +### BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 depicts one example of fluorescence emitting wave form of a labeling agent. -## DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS +### DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be explained in detail hereinbelow. @@ -106,7 +106,7 @@ Specific examples of these labeling agents are illustrated in Tables 1, 2 and 3, The synthetic method of these azulene dyes is described in U.S. Pat. No. 4,738,908. -## CLAIMS +### CLAIMS 1. A labeled complex for detecting a subject compound to be analyzed by means of optical means using near-infrared radiation which complex comprises a substance from a living organism and a labeling agent fixed onto the substance, the substance capable of specifically binding to the subject compound, wherein the labeling agent comprises a compound represented by the general formula (IV): wherein A, B, D and E are independently selected from the group consisting of hydrogen atom, a substituted or an unsubstituted alkyl group having two or more carbon atoms, alkenyl group, aralkyl group, aryl group, styryl group and heterocyclic group, and at least one of A and B is a substituted or unsubstituted aryl group, and at least one of D and E is a substituted or unsubstituted aryl group; r.sub.1 ' and r.sub.2 ' are individually selected from the group consisting of hydrogen atom, a substituted or an unsubstituted alkyl group, cyclic alkyl group, alkenyl group, aralkyl group and aryl group; k is 0 or 1; is 0, 1 or 2; and X.sub.2.sup..crclbar. represents an anion. diff --git a/tests/data/groundtruth/docling_v2/pg06442728.json b/tests/data/groundtruth/docling_v2/pg06442728.json index d5314198..3c312ae0 100644 --- a/tests/data/groundtruth/docling_v2/pg06442728.json +++ b/tests/data/groundtruth/docling_v2/pg06442728.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "pg06442728.xml", "origin": { "mimetype": "application/xml", diff --git a/tests/data/groundtruth/docling_v2/pg06442728.md b/tests/data/groundtruth/docling_v2/pg06442728.md index e4fe6679..3f675f37 100644 --- a/tests/data/groundtruth/docling_v2/pg06442728.md +++ b/tests/data/groundtruth/docling_v2/pg06442728.md @@ -1,18 +1,18 @@ # Methods and apparatus for turbo code -## ABSTRACT +### ABSTRACT An interleaver receives incoming data frames of size N. The interleaver indexes the elements of the frame with an N₁×N₂ index array. The interleaver then effectively rearranges (permutes) the data by permuting the rows of the index array. The interleaver employs the equation I(j,k)=I(j,αjk+βj)modP) to permute the columns (indexed by k) of each row (indexed by j). P is at least equal to N₂, βj is a constant which may be different for each row, and each αj is a relative prime number relative to P. After permuting, the interleaver outputs the data in a different order than received (e.g., receives sequentially row by row, outputs sequentially each column by column). -## CROSS-REFERENCE TO RELATED APPLICATIONS +### CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 60/115,394 filed Jan. 11, 1999. -## FIELD OF THE INVENTION +### FIELD OF THE INVENTION This invention relates generally to communication systems and, more particularly, to interleavers for performing code modulation. -## BACKGROUND OF THE INVENTION +### BACKGROUND OF THE INVENTION Techniques for encoding communication channels, known as coded modulation, have been found to improve the bit error rate (BER) of electronic communication systems such as modem and wireless communication systems. Turbo coded modulation has proven to be a practical, power-efficient, and bandwidth-efficient modulation method for “random-error” channels characterized by additive white Gaussian noise (AWGN) or fading. These random-error channels can be found, for example, in the code division multiple access (CDMA) environment. Since the capacity of a CDMA environment is dependent upon the operating signal to noise ratio, improved performance translates into higher capacity. @@ -34,7 +34,7 @@ It is also an object of the present invention to provide systems and methods of These and other objects of the invention will become apparent to those skilled in the art from the following description thereof. -## SUMMARY OF THE INVENTION +### SUMMARY OF THE INVENTION The foregoing objects, and others, may be accomplished by the present invention, which interleaves a data frame, where the data frame has a predetermined size and is made up of portions. An embodiment of the invention includes an interleaver for interleaving these data frames. The interleaver includes an input memory configured to store a received data frame as an array organized into rows and columns, a processor connected to the input memory and configured to permute the received data frame in accordance with the equation D(j,k)=D (j, (αjk+βj)modP), and a working memory in electrical communication with the processor and configured to store a permuted version of the data frame. The elements of the equation are as follows: D is the data frame, j and k are indexes to the rows and columns, respectively, in the data frame, α and β are sets of constants selected according to the current row, and P and each αj are relative prime numbers. (“Relative prime numbers” connotes a set of numbers that have no common divisor other than 1. Members of a set of relative prime numbers, considered by themselves, need not be prime numbers.) @@ -44,7 +44,7 @@ Still another embodiment of the invention includes an interleaver which includes The invention will next be described in connection with certain illustrated embodiments and practices. However, it will be clear to those skilled in the art that various modifications, additions and subtractions can be made without departing from the spirit or scope of the claims. -## BRIEF DESCRIPTION OF THE DRAWINGS +### BRIEF DESCRIPTION OF THE DRAWINGS The invention will be more clearly understood by reference to the following detailed description of an exemplary embodiment in conjunction with the accompanying drawings, in which: @@ -58,7 +58,7 @@ FIG. 4 depicts a data frame stored in consecutive storage locations; FIG. 5 depicts an index array for indexing the data frame shown in FIG. 4, and permutation of the index array. -## DETAILED DESCRIPTION OF THE INVENTION +### DETAILED DESCRIPTION OF THE INVENTION FIG. 1 illustrates a conventional turbo encoder. As illustrated, conventional turbo encoders include two encoders 20 and an interleaver 100. An interleaver 100 in accordance with the present invention receives incoming data frames 110 of size N, where N is the number of bits, number of bytes, or the number of some other portion the frame may be separated into, which are regarded as frame elements. The interleaver 100 separates the N frame elements into sets of data, such as rows. The interleaver then rearranges (permutes) the data in each set (row) in a pseudo-random fashion. The interleaver 100 may employ different methods for rearranging the data of the different sets. However, those skilled in the art will recognize that one or more of the methods could be reused on one or more of the sets without departing from the scope of the invention. After permuting the data in each of the sets, the interleaver outputs the data in a different order than received. @@ -142,7 +142,7 @@ It will be understood that changes may be made in the above construction and in It is also to be understood that the following claims are intended to cover all of the generic and specific features of the invention as described herein, and all statements of the scope of the invention which, as a matter of language, might be said to fall therebetween. -## CLAIMS +### CLAIMS 1. A method of interleaving elements of frames of signal data communication channel, the method comprising; storing a frame of signal data comprising a plurality of elements as an array D having N₁ rows enumerated as 0, 1, . . . N₁−1; and N₂ columns enumerated as 0, 1, . . . N₂−1, wherein N₁ and N₂ are positive integers greater than 1; and permuting array D into array D₁ according to D₁(𝑗,𝑘)=D(𝑗,(αj𝑘+βj)𝑚𝑜𝑑𝑃) wherein j is an index through the rows of arrays D and D₁; k is an index through the columns of arrays D and D₁; αj and βj are integers predetermined for each row j; P is an integer at least equal to N₂; and each αj is a relative prime number relative to P. diff --git a/tests/data/groundtruth/docling_v2/picture_classification.doctags.txt b/tests/data/groundtruth/docling_v2/picture_classification.doctags.txt index f990405a..96c7c68e 100644 --- a/tests/data/groundtruth/docling_v2/picture_classification.doctags.txt +++ b/tests/data/groundtruth/docling_v2/picture_classification.doctags.txt @@ -1,11 +1,11 @@ Figures Example -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir- -Figure 1: -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Figure 1: This is an example image. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 1 -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy -Figure 2: -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Figure 2: This is an example image. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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"orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/6", "parent": {"cref": "#/pictures/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 226.891, "t": 268.679, "r": 384.355, "b": 259.97299999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "Figure 2: This is an example image.", "text": "Figure 2: This is an example image.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 133.768, "t": 245.60799999999995, "r": 477.482, "b": 117.351, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 804]}], "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.", "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Figure 1: +Figure 1: This is an example image. -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. -Figure 2: +Figure 2: This is an example image. -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy \ No newline at end of file +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 208.39800000000002, "r_x1": 477.482, "r_y1": 208.39800000000002, "r_x2": 477.482, "r_y2": 199.692, "r_x3": 133.768, "r_y3": 199.692, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 266.08399999999995, "r_x1": 407.17, "r_y1": 266.08399999999995, "r_x2": 407.17, "r_y2": 257.37800000000004, "r_x3": 133.768, "r_y3": 257.37800000000004, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 266.08399999999995, "r_x1": 477.475, "r_y1": 266.08399999999995, "r_x2": 477.475, "r_y2": 257.37800000000004, "r_x3": 414.054, "r_y3": 257.37800000000004, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 278.039, "r_x1": 323.816, "r_y1": 278.039, "r_x2": 323.816, "r_y2": 269.33299999999997, "r_x3": 133.768, "r_y3": 269.33299999999997, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 278.039, "r_x1": 371.404, "r_y1": 278.039, "r_x2": 371.404, "r_y2": 269.33299999999997, "r_x3": 330.72, "r_y3": 269.33299999999997, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 278.039, "r_x1": 477.476, "r_y1": 278.039, "r_x2": 477.476, "r_y2": 269.33299999999997, "r_x3": 375.548, "r_y3": 269.33299999999997, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 289.995, "r_x1": 351.485, "r_y1": 289.995, "r_x2": 351.485, "r_y2": 281.289, "r_x3": 133.768, "r_y3": 281.289, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "caption", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 226.891, "t": 529.245, "r": 384.355, "b": 537.951, "coord_origin": "TOPLEFT"}, "confidence": 0.9477447271347046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.891, "r_y0": 537.951, "r_x1": 266.075, "r_y1": 537.951, "r_x2": 266.075, "r_y2": 529.245, "r_x3": 226.891, "r_y3": 529.245, "coord_origin": "TOPLEFT"}, "text": "Figure 1:", "orig": "Figure 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.508, "r_y0": 537.951, "r_x1": 384.355, "r_y1": 537.951, "r_x2": 384.355, "r_y2": 529.245, "r_x3": 270.508, "r_y3": 529.245, "coord_origin": "TOPLEFT"}, "text": "This is an example image.", "orig": "This is an example image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 1: This is an example image."}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.768, "t": 553.155, "r": 477.482, "b": 669.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9862836599349976, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 561.861, "r_x1": 477.478, "r_y1": 561.861, "r_x2": 477.478, "r_y2": 553.155, "r_x3": 148.712, "r_y3": 553.155, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 573.816, "r_x1": 432.467, "r_y1": 573.816, "r_x2": 432.467, "r_y2": 565.11, "r_x3": 133.768, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 573.816, "r_x1": 477.479, "r_y1": 573.816, "r_x2": 477.479, "r_y2": 565.11, "r_x3": 437.398, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 585.771, "r_x1": 174.725, "r_y1": 585.771, "r_x2": 174.725, "r_y2": 577.065, "r_x3": 133.768, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 585.771, "r_x1": 431.98, "r_y1": 585.771, "r_x2": 431.98, "r_y2": 577.065, "r_x3": 180.075, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 585.771, "r_x1": 477.476, "r_y1": 585.771, "r_x2": 477.476, "r_y2": 577.065, "r_x3": 437.319, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 597.726, "r_x1": 477.476, "r_y1": 597.726, "r_x2": 477.476, "r_y2": 589.02, "r_x3": 133.768, "r_y3": 589.02, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 609.682, "r_x1": 186.966, "r_y1": 609.682, "r_x2": 186.966, "r_y2": 600.976, "r_x3": 133.768, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "ipsum dolor", "orig": "ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.938, "r_y0": 609.682, "r_x1": 202.509, "r_y1": 609.682, "r_x2": 202.509, "r_y2": 600.976, "r_x3": 191.938, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sit", "orig": "sit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 207.47, "r_y0": 609.682, "r_x1": 231.823, "r_y1": 609.682, "r_x2": 231.823, "r_y2": 600.976, "r_x3": 207.47, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "amet,", "orig": "amet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.203, "r_y0": 609.682, "r_x1": 282.117, "r_y1": 609.682, "r_x2": 282.117, "r_y2": 600.976, "r_x3": 237.203, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "consetetur", "orig": "consetetur", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 287.078, "r_y0": 609.682, "r_x1": 331.468, "r_y1": 609.682, "r_x2": 331.468, "r_y2": 600.976, "r_x3": 287.078, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sadipscing", "orig": "sadipscing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.439, "r_y0": 609.682, "r_x1": 356.946, "r_y1": 609.682, "r_x2": 356.946, "r_y2": 600.976, "r_x3": 336.439, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "elitr,", "orig": "elitr,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 362.316, "r_y0": 609.682, "r_x1": 376.208, "r_y1": 609.682, "r_x2": 376.208, "r_y2": 600.976, "r_x3": 362.316, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sed", "orig": "sed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.179, "r_y0": 609.682, "r_x1": 477.482, "r_y1": 609.682, "r_x2": 477.482, "r_y2": 600.976, "r_x3": 381.179, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "diam nonumy eirmod", "orig": "diam nonumy eirmod", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 621.637, "r_x1": 477.481, "r_y1": 621.637, "r_x2": 477.481, "r_y2": 612.931, "r_x3": 133.768, "r_y3": 612.931, "coord_origin": "TOPLEFT"}, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 633.592, "r_x1": 477.482, "r_y1": 633.592, "r_x2": 477.482, "r_y2": 624.886, "r_x3": 133.768, "r_y3": 624.886, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 645.547, "r_x1": 387.354, "r_y1": 645.547, "r_x2": 387.354, "r_y2": 636.841, "r_x3": 133.768, "r_y3": 636.841, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 645.547, "r_x1": 477.478, "r_y1": 645.547, "r_x2": 477.478, "r_y2": 636.841, "r_x3": 393.571, "r_y3": 636.841, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 657.502, "r_x1": 477.48, "r_y1": 657.502, "r_x2": 477.48, "r_y2": 648.796, "r_x3": 133.768, "r_y3": 648.796, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 669.457, "r_x1": 399.578, "r_y1": 669.457, "r_x2": 399.578, "r_y2": 660.751, "r_x3": 133.768, "r_y3": 660.751, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua."}, {"label": "page_footer", "id": 5, "page_no": 0, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 303.133, "t": 695.831, "r": 308.114, "b": 704.537, "coord_origin": "TOPLEFT"}, "confidence": 0.8646790981292725, "cells": [{"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.133, "r_y0": 704.537, "r_x1": 308.114, "r_y1": 704.537, "r_x2": 308.114, "r_y2": 695.831, "r_x3": 303.133, "r_y3": 695.831, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "picture", "id": 2, "page_no": 0, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 134.9200439453125, "t": 304.890625, "r": 475.6635437011719, "b": 510.21826171875, "coord_origin": "TOPLEFT"}, "confidence": 0.9803217053413391, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "section_header", "id": 3, "page_no": 0, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 133.768, "t": 124.92399999999998, "r": 252.355, "b": 137.51599999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9627318382263184, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 137.51599999999996, "r_x1": 252.355, "r_y1": 137.51599999999996, "r_x2": 252.355, "r_y2": 124.92399999999998, "r_x3": 133.768, "r_y3": 124.92399999999998, "coord_origin": "TOPLEFT"}, "text": "Figures Example", "orig": "Figures Example", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figures Example"}, {"label": "text", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 149.78200000000004, "r": 477.483, "b": 289.995, "coord_origin": "TOPLEFT"}, "confidence": 0.9869934320449829, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 158.48800000000006, "r_x1": 477.48, "r_y1": 158.48800000000006, "r_x2": 477.48, "r_y2": 149.78200000000004, "r_x3": 133.768, "r_y3": 149.78200000000004, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 170.44299999999998, "r_x1": 477.481, "r_y1": 170.44299999999998, "r_x2": 477.481, "r_y2": 161.73699999999997, "r_x3": 133.768, "r_y3": 161.73699999999997, "coord_origin": "TOPLEFT"}, "text": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "orig": "mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 182.39800000000002, "r_x1": 150.927, "r_y1": 182.39800000000002, "r_x2": 150.927, "r_y2": 173.692, "r_x3": 133.768, "r_y3": 173.692, "coord_origin": "TOPLEFT"}, "text": "tua.", "orig": "tua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 156.436, "r_y0": 182.39800000000002, "r_x1": 408.928, "r_y1": 182.39800000000002, "r_x2": 408.928, "r_y2": 173.692, "r_x3": 156.436, "r_y3": 173.692, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.437, "r_y0": 182.39800000000002, "r_x1": 477.476, "r_y1": 182.39800000000002, "r_x2": 477.476, "r_y2": 173.692, "r_x3": 414.437, "r_y3": 173.692, "coord_origin": "TOPLEFT"}, "text": "Stet clita kasd", "orig": "Stet clita kasd", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 194.35299999999995, "r_x1": 477.475, "r_y1": 194.35299999999995, "r_x2": 477.475, "r_y2": 185.64700000000005, "r_x3": 133.768, "r_y3": 185.64700000000005, "coord_origin": "TOPLEFT"}, "text": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "orig": "gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 206.308, "r_x1": 477.477, "r_y1": 206.308, "r_x2": 477.477, "r_y2": 197.60199999999998, "r_x3": 133.768, "r_y3": 197.60199999999998, "coord_origin": "TOPLEFT"}, "text": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "orig": "sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 218.26300000000003, "r_x1": 440.023, "r_y1": 218.26300000000003, "r_x2": 440.023, "r_y2": 209.55700000000002, "r_x3": 133.768, "r_y3": 209.55700000000002, "coord_origin": "TOPLEFT"}, "text": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 444.716, "r_y0": 218.26300000000003, "r_x1": 477.481, "r_y1": 218.26300000000003, "r_x2": 477.481, "r_y2": 209.55700000000002, "r_x3": 444.716, "r_y3": 209.55700000000002, "coord_origin": "TOPLEFT"}, "text": "At vero", "orig": "At vero", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 230.21900000000005, "r_x1": 355.209, "r_y1": 230.21900000000005, "r_x2": 355.209, "r_y2": 221.51300000000003, "r_x3": 133.768, "r_y3": 221.51300000000003, "coord_origin": "TOPLEFT"}, "text": "eos et accusam et justo duo dolores et ea rebum.", "orig": "eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 362.601, "r_y0": 230.21900000000005, "r_x1": 403.435, "r_y1": 230.21900000000005, "r_x2": 403.435, "r_y2": 221.51300000000003, "r_x3": 362.601, "r_y3": 221.51300000000003, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 407.748, "r_y0": 230.21900000000005, "r_x1": 477.483, "r_y1": 230.21900000000005, "r_x2": 477.483, "r_y2": 221.51300000000003, "r_x3": 407.748, "r_y3": 221.51300000000003, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren,", "orig": "kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 242.17399999999998, "r_x1": 387.354, "r_y1": 242.17399999999998, "r_x2": 387.354, "r_y2": 233.46799999999996, "r_x3": 133.768, "r_y3": 233.46799999999996, 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amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 266.08399999999995, "r_x1": 407.17, "r_y1": 266.08399999999995, "r_x2": 407.17, "r_y2": 257.37800000000004, "r_x3": 133.768, "r_y3": 257.37800000000004, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 266.08399999999995, "r_x1": 477.475, "r_y1": 266.08399999999995, "r_x2": 477.475, "r_y2": 257.37800000000004, "r_x3": 414.054, "r_y3": 257.37800000000004, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 278.039, "r_x1": 323.816, "r_y1": 278.039, "r_x2": 323.816, "r_y2": 269.33299999999997, "r_x3": 133.768, "r_y3": 269.33299999999997, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 278.039, "r_x1": 371.404, "r_y1": 278.039, "r_x2": 371.404, "r_y2": 269.33299999999997, "r_x3": 330.72, "r_y3": 269.33299999999997, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 278.039, "r_x1": 477.476, "r_y1": 278.039, "r_x2": 477.476, "r_y2": 269.33299999999997, "r_x3": 375.548, "r_y3": 269.33299999999997, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 289.995, "r_x1": 351.485, "r_y1": 289.995, "r_x2": 351.485, "r_y2": 281.289, "r_x3": 133.768, "r_y3": 281.289, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "caption", "id": 4, "page_no": 0, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 226.891, "t": 529.245, "r": 384.355, "b": 537.951, "coord_origin": "TOPLEFT"}, "confidence": 0.9477447271347046, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.891, "r_y0": 537.951, "r_x1": 266.075, "r_y1": 537.951, "r_x2": 266.075, "r_y2": 529.245, "r_x3": 226.891, "r_y3": 529.245, "coord_origin": "TOPLEFT"}, "text": "Figure 1:", "orig": "Figure 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.508, "r_y0": 537.951, "r_x1": 384.355, "r_y1": 537.951, "r_x2": 384.355, "r_y2": 529.245, "r_x3": 270.508, "r_y3": 529.245, "coord_origin": "TOPLEFT"}, "text": "This is an example image.", "orig": "This is an example image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 1: This is an example image."}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.768, "t": 553.155, "r": 477.482, "b": 669.457, "coord_origin": "TOPLEFT"}, "confidence": 0.9862836599349976, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 561.861, "r_x1": 477.478, "r_y1": 561.861, "r_x2": 477.478, "r_y2": 553.155, "r_x3": 148.712, "r_y3": 553.155, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 573.816, "r_x1": 432.467, "r_y1": 573.816, "r_x2": 432.467, "r_y2": 565.11, "r_x3": 133.768, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 573.816, "r_x1": 477.479, "r_y1": 573.816, "r_x2": 477.479, "r_y2": 565.11, "r_x3": 437.398, "r_y3": 565.11, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 585.771, "r_x1": 174.725, "r_y1": 585.771, "r_x2": 174.725, "r_y2": 577.065, "r_x3": 133.768, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 585.771, "r_x1": 431.98, "r_y1": 585.771, "r_x2": 431.98, "r_y2": 577.065, "r_x3": 180.075, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 585.771, "r_x1": 477.476, "r_y1": 585.771, "r_x2": 477.476, "r_y2": 577.065, "r_x3": 437.319, "r_y3": 577.065, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 597.726, "r_x1": 477.476, "r_y1": 597.726, "r_x2": 477.476, "r_y2": 589.02, "r_x3": 133.768, "r_y3": 589.02, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 609.682, "r_x1": 186.966, "r_y1": 609.682, "r_x2": 186.966, "r_y2": 600.976, "r_x3": 133.768, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "ipsum dolor", "orig": "ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.938, "r_y0": 609.682, "r_x1": 202.509, "r_y1": 609.682, "r_x2": 202.509, "r_y2": 600.976, "r_x3": 191.938, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sit", "orig": "sit", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 207.47, "r_y0": 609.682, "r_x1": 231.823, "r_y1": 609.682, "r_x2": 231.823, "r_y2": 600.976, "r_x3": 207.47, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "amet,", "orig": "amet,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 237.203, "r_y0": 609.682, "r_x1": 282.117, "r_y1": 609.682, "r_x2": 282.117, "r_y2": 600.976, "r_x3": 237.203, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "consetetur", "orig": "consetetur", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 287.078, "r_y0": 609.682, "r_x1": 331.468, "r_y1": 609.682, "r_x2": 331.468, "r_y2": 600.976, "r_x3": 287.078, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sadipscing", "orig": "sadipscing", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 336.439, "r_y0": 609.682, "r_x1": 356.946, "r_y1": 609.682, "r_x2": 356.946, "r_y2": 600.976, "r_x3": 336.439, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "elitr,", "orig": "elitr,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 362.316, "r_y0": 609.682, "r_x1": 376.208, "r_y1": 609.682, "r_x2": 376.208, "r_y2": 600.976, "r_x3": 362.316, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "sed", "orig": "sed", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.179, "r_y0": 609.682, "r_x1": 477.482, "r_y1": 609.682, "r_x2": 477.482, "r_y2": 600.976, "r_x3": 381.179, "r_y3": 600.976, "coord_origin": "TOPLEFT"}, "text": "diam nonumy eirmod", "orig": "diam nonumy eirmod", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 621.637, "r_x1": 477.481, "r_y1": 621.637, "r_x2": 477.481, "r_y2": 612.931, "r_x3": 133.768, "r_y3": 612.931, "coord_origin": "TOPLEFT"}, "text": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 633.592, "r_x1": 477.482, "r_y1": 633.592, "r_x2": 477.482, "r_y2": 624.886, "r_x3": 133.768, "r_y3": 624.886, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 645.547, "r_x1": 387.354, "r_y1": 645.547, "r_x2": 387.354, "r_y2": 636.841, "r_x3": 133.768, "r_y3": 636.841, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 645.547, "r_x1": 477.478, "r_y1": 645.547, "r_x2": 477.478, "r_y2": 636.841, "r_x3": 393.571, "r_y3": 636.841, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 657.502, "r_x1": 477.48, "r_y1": 657.502, "r_x2": 477.48, "r_y2": 648.796, "r_x3": 133.768, "r_y3": 648.796, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 669.457, "r_x1": 399.578, "r_y1": 669.457, "r_x2": 399.578, "r_y2": 660.751, "r_x3": 133.768, "r_y3": 660.751, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 208.39800000000002, "r_x1": 477.482, "r_y1": 208.39800000000002, "r_x2": 477.482, "r_y2": 199.692, "r_x3": 133.768, "r_y3": 199.692, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 626.829, "r_x1": 477.482, "r_y1": 626.829, "r_x2": 477.482, "r_y2": 618.123, "r_x3": 133.768, "r_y3": 618.123, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 626.829, "r_x1": 477.482, "r_y1": 626.829, "r_x2": 477.482, "r_y2": 618.123, "r_x3": 133.768, "r_y3": 618.123, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 208.39800000000002, "r_x1": 477.482, "r_y1": 208.39800000000002, "r_x2": 477.482, "r_y2": 199.692, "r_x3": 133.768, "r_y3": 199.692, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 220.35299999999995, "r_x1": 387.354, "r_y1": 220.35299999999995, "r_x2": 387.354, "r_y2": 211.64700000000005, "r_x3": 133.768, "r_y3": 211.64700000000005, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 220.35299999999995, "r_x1": 477.478, "r_y1": 220.35299999999995, "r_x2": 477.478, "r_y2": 211.64700000000005, "r_x3": 393.571, "r_y3": 211.64700000000005, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 232.308, "r_x1": 477.48, "r_y1": 232.308, "r_x2": 477.48, "r_y2": 223.60199999999998, "r_x3": 133.768, "r_y3": 223.60199999999998, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 244.26300000000003, "r_x1": 407.17, "r_y1": 244.26300000000003, "r_x2": 407.17, "r_y2": 235.55700000000002, "r_x3": 133.768, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 244.26300000000003, "r_x1": 477.475, "r_y1": 244.26300000000003, "r_x2": 477.475, "r_y2": 235.55700000000002, "r_x3": 414.054, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 256.21799999999996, "r_x1": 323.816, "r_y1": 256.21799999999996, "r_x2": 323.816, "r_y2": 247.51199999999994, "r_x3": 133.768, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 256.21799999999996, "r_x1": 371.404, "r_y1": 256.21799999999996, "r_x2": 371.404, "r_y2": 247.51199999999994, "r_x3": 330.72, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 256.21799999999996, "r_x1": 477.476, "r_y1": 256.21799999999996, "r_x2": 477.476, "r_y2": 247.51199999999994, "r_x3": 375.548, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 268.174, "r_x1": 351.485, "r_y1": 268.174, "r_x2": 351.485, "r_y2": 259.46799999999996, "r_x3": 133.768, "r_y3": 259.46799999999996, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "caption", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "caption", "bbox": {"l": 226.891, "t": 523.321, "r": 384.355, "b": 532.027, "coord_origin": "TOPLEFT"}, "confidence": 0.9494236707687378, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.891, "r_y0": 532.027, "r_x1": 266.075, "r_y1": 532.027, "r_x2": 266.075, "r_y2": 523.321, "r_x3": 226.891, "r_y3": 523.321, "coord_origin": "TOPLEFT"}, "text": "Figure 2:", "orig": "Figure 2:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.508, "r_y0": 532.027, "r_x1": 384.355, "r_y1": 532.027, "r_x2": 384.355, "r_y2": 523.321, "r_x3": 270.508, "r_y3": 523.321, "coord_origin": "TOPLEFT"}, "text": "This is an example image.", "orig": "This is an example image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: This is an example image."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 546.392, "r": 477.482, "b": 674.649, "coord_origin": "TOPLEFT"}, "confidence": 0.9874356985092163, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 555.098, "r_x1": 477.478, "r_y1": 555.098, "r_x2": 477.478, "r_y2": 546.392, "r_x3": 148.712, "r_y3": 546.392, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 567.053, "r_x1": 432.467, "r_y1": 567.053, "r_x2": 432.467, "r_y2": 558.347, "r_x3": 133.768, "r_y3": 558.347, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 567.053, "r_x1": 477.479, "r_y1": 567.053, "r_x2": 477.479, "r_y2": 558.347, "r_x3": 437.398, "r_y3": 558.347, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 579.008, "r_x1": 174.725, "r_y1": 579.008, "r_x2": 174.725, "r_y2": 570.302, "r_x3": 133.768, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 579.008, "r_x1": 431.98, "r_y1": 579.008, "r_x2": 431.98, "r_y2": 570.302, "r_x3": 180.075, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 579.008, "r_x1": 477.476, "r_y1": 579.008, "r_x2": 477.476, "r_y2": 570.302, "r_x3": 437.319, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 590.963, "r_x1": 477.476, "r_y1": 590.963, "r_x2": 477.476, "r_y2": 582.2570000000001, "r_x3": 133.768, "r_y3": 582.2570000000001, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 626.829, "r_x1": 477.482, "r_y1": 626.829, "r_x2": 477.482, "r_y2": 618.123, "r_x3": 133.768, "r_y3": 618.123, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 638.784, "r_x1": 387.354, "r_y1": 638.784, "r_x2": 387.354, "r_y2": 630.078, "r_x3": 133.768, "r_y3": 630.078, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 638.784, "r_x1": 477.478, "r_y1": 638.784, "r_x2": 477.478, "r_y2": 630.078, "r_x3": 393.571, "r_y3": 630.078, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 650.739, "r_x1": 477.48, "r_y1": 650.739, "r_x2": 477.48, "r_y2": 642.033, "r_x3": 133.768, "r_y3": 642.033, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 662.694, "r_x1": 407.17, "r_y1": 662.694, "r_x2": 407.17, "r_y2": 653.988, "r_x3": 133.768, "r_y3": 653.988, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 662.694, "r_x1": 477.475, "r_y1": 662.694, "r_x2": 477.475, "r_y2": 653.988, "r_x3": 414.054, "r_y3": 653.988, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 674.649, "r_x1": 318.017, "r_y1": 674.649, "r_x2": 318.017, "r_y2": 665.943, "r_x3": 133.768, "r_y3": 665.943, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum."}, {"label": "page_footer", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 303.133, "t": 695.831, "r": 308.114, "b": 704.537, "coord_origin": "TOPLEFT"}, "confidence": 0.8888521790504456, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.133, "r_y0": 704.537, "r_x1": 308.114, "r_y1": 704.537, "r_x2": 308.114, "r_y2": 695.831, "r_x3": 303.133, "r_y3": 695.831, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "picture", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 218.8155517578125, "t": 278.0153503417969, "r": 391.96246337890625, "b": 508.89410400390625, "coord_origin": "TOPLEFT"}, "confidence": 0.9837717413902283, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 1, "page_no": 1, "cluster": {"id": 1, "label": "text", "bbox": {"l": 133.768, "t": 127.96100000000001, "r": 477.482, "b": 268.174, "coord_origin": "TOPLEFT"}, "confidence": 0.9870928525924683, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 136.66700000000003, "r_x1": 477.478, "r_y1": 136.66700000000003, "r_x2": 477.478, "r_y2": 127.96100000000001, "r_x3": 148.712, "r_y3": 127.96100000000001, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 148.62199999999996, "r_x1": 432.467, "r_y1": 148.62199999999996, "r_x2": 432.467, "r_y2": 139.91600000000005, "r_x3": 133.768, "r_y3": 139.91600000000005, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 148.62199999999996, "r_x1": 477.479, "r_y1": 148.62199999999996, "r_x2": 477.479, "r_y2": 139.91600000000005, "r_x3": 437.398, "r_y3": 139.91600000000005, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 160.577, "r_x1": 174.725, "r_y1": 160.577, "r_x2": 174.725, "r_y2": 151.87099999999998, "r_x3": 133.768, "r_y3": 151.87099999999998, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 160.577, "r_x1": 431.98, "r_y1": 160.577, "r_x2": 431.98, "r_y2": 151.87099999999998, "r_x3": 180.075, "r_y3": 151.87099999999998, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 160.577, "r_x1": 477.476, "r_y1": 160.577, "r_x2": 477.476, "r_y2": 151.87099999999998, "r_x3": 437.319, "r_y3": 151.87099999999998, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 172.53200000000004, "r_x1": 477.476, "r_y1": 172.53200000000004, "r_x2": 477.476, "r_y2": 163.82600000000002, "r_x3": 133.768, "r_y3": 163.82600000000002, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 208.39800000000002, "r_x1": 477.482, "r_y1": 208.39800000000002, "r_x2": 477.482, "r_y2": 199.692, "r_x3": 133.768, "r_y3": 199.692, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 220.35299999999995, "r_x1": 387.354, "r_y1": 220.35299999999995, "r_x2": 387.354, "r_y2": 211.64700000000005, "r_x3": 133.768, "r_y3": 211.64700000000005, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 220.35299999999995, "r_x1": 477.478, "r_y1": 220.35299999999995, "r_x2": 477.478, "r_y2": 211.64700000000005, "r_x3": 393.571, "r_y3": 211.64700000000005, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 232.308, "r_x1": 477.48, "r_y1": 232.308, "r_x2": 477.48, "r_y2": 223.60199999999998, "r_x3": 133.768, "r_y3": 223.60199999999998, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 244.26300000000003, "r_x1": 407.17, "r_y1": 244.26300000000003, "r_x2": 407.17, "r_y2": 235.55700000000002, "r_x3": 133.768, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 244.26300000000003, "r_x1": 477.475, "r_y1": 244.26300000000003, "r_x2": 477.475, "r_y2": 235.55700000000002, "r_x3": 414.054, "r_y3": 235.55700000000002, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 256.21799999999996, "r_x1": 323.816, "r_y1": 256.21799999999996, "r_x2": 323.816, "r_y2": 247.51199999999994, "r_x3": 133.768, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 330.72, "r_y0": 256.21799999999996, "r_x1": 371.404, "r_y1": 256.21799999999996, "r_x2": 371.404, "r_y2": 247.51199999999994, "r_x3": 330.72, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.548, "r_y0": 256.21799999999996, "r_x1": 477.476, "r_y1": 256.21799999999996, "r_x2": 477.476, "r_y2": 247.51199999999994, "r_x3": 375.548, "r_y3": 247.51199999999994, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea", "orig": "kasd gubergren, no sea", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 268.174, "r_x1": 351.485, "r_y1": 268.174, "r_x2": 351.485, "r_y2": 259.46799999999996, "r_x3": 133.768, "r_y3": 259.46799999999996, "coord_origin": "TOPLEFT"}, "text": "takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."}, {"label": "caption", "id": 3, "page_no": 1, "cluster": {"id": 3, "label": "caption", "bbox": {"l": 226.891, "t": 523.321, "r": 384.355, "b": 532.027, "coord_origin": "TOPLEFT"}, "confidence": 0.9494236707687378, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.891, "r_y0": 532.027, "r_x1": 266.075, "r_y1": 532.027, "r_x2": 266.075, "r_y2": 523.321, "r_x3": 226.891, "r_y3": 523.321, "coord_origin": "TOPLEFT"}, "text": "Figure 2:", "orig": "Figure 2:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 270.508, "r_y0": 532.027, "r_x1": 384.355, "r_y1": 532.027, "r_x2": 384.355, "r_y2": 523.321, "r_x3": 270.508, "r_y3": 523.321, "coord_origin": "TOPLEFT"}, "text": "This is an example image.", "orig": "This is an example image.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 2: This is an example image."}, {"label": "text", "id": 0, "page_no": 1, "cluster": {"id": 0, "label": "text", "bbox": {"l": 133.768, "t": 546.392, "r": 477.482, "b": 674.649, "coord_origin": "TOPLEFT"}, "confidence": 0.9874356985092163, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.712, "r_y0": 555.098, "r_x1": 477.478, "r_y1": 555.098, "r_x2": 477.478, "r_y2": 546.392, "r_x3": 148.712, "r_y3": 546.392, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "orig": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 567.053, "r_x1": 432.467, "r_y1": 567.053, "r_x2": 432.467, "r_y2": 558.347, "r_x3": 133.768, "r_y3": 558.347, "coord_origin": "TOPLEFT"}, "text": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "orig": "eirmod tempor invidunt ut labore et dolore magna aliquyam erat,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.398, "r_y0": 567.053, "r_x1": 477.479, "r_y1": 567.053, "r_x2": 477.479, "r_y2": 558.347, "r_x3": 437.398, "r_y3": 558.347, "coord_origin": "TOPLEFT"}, "text": "sed diam", "orig": "sed diam", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 579.008, "r_x1": 174.725, "r_y1": 579.008, "r_x2": 174.725, "r_y2": 570.302, "r_x3": 133.768, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "voluptua.", "orig": "voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.075, "r_y0": 579.008, "r_x1": 431.98, "r_y1": 579.008, "r_x2": 431.98, "r_y2": 570.302, "r_x3": 180.075, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "At vero eos et accusam et justo duo dolores et ea rebum.", "orig": "At vero eos et accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 437.319, "r_y0": 579.008, "r_x1": 477.476, "r_y1": 579.008, "r_x2": 477.476, "r_y2": 570.302, "r_x3": 437.319, "r_y3": 570.302, "coord_origin": "TOPLEFT"}, "text": "Stet clita", "orig": "Stet clita", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 590.963, "r_x1": 477.476, "r_y1": 590.963, "r_x2": 477.476, "r_y2": 582.2570000000001, "r_x3": 133.768, "r_y3": 582.2570000000001, "coord_origin": "TOPLEFT"}, "text": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem", "orig": "kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. 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At", "orig": "tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 626.829, "r_x1": 477.482, "r_y1": 626.829, "r_x2": 477.482, "r_y2": 618.123, "r_x3": 133.768, "r_y3": 618.123, "coord_origin": "TOPLEFT"}, "text": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "orig": "vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 638.784, "r_x1": 387.354, "r_y1": 638.784, "r_x2": 387.354, "r_y2": 630.078, "r_x3": 133.768, "r_y3": 630.078, "coord_origin": "TOPLEFT"}, "text": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "orig": "no sea takimata sanctus est Lorem ipsum dolor sit amet.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 393.571, "r_y0": 638.784, "r_x1": 477.478, "r_y1": 638.784, "r_x2": 477.478, "r_y2": 630.078, "r_x3": 393.571, "r_y3": 630.078, "coord_origin": "TOPLEFT"}, "text": "Lorem ipsum dolor", "orig": "Lorem ipsum dolor", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 650.739, "r_x1": 477.48, "r_y1": 650.739, "r_x2": 477.48, "r_y2": 642.033, "r_x3": 133.768, "r_y3": 642.033, "coord_origin": "TOPLEFT"}, "text": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "orig": "sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 662.694, "r_x1": 407.17, "r_y1": 662.694, "r_x2": 407.17, "r_y2": 653.988, "r_x3": 133.768, "r_y3": 653.988, "coord_origin": "TOPLEFT"}, "text": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "orig": "ut labore et dolore magna aliquyam erat, sed diam voluptua.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.054, "r_y0": 662.694, "r_x1": 477.475, "r_y1": 662.694, "r_x2": 477.475, "r_y2": 653.988, "r_x3": 414.054, "r_y3": 653.988, "coord_origin": "TOPLEFT"}, "text": "At vero eos et", "orig": "At vero eos et", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 133.768, "r_y0": 674.649, "r_x1": 318.017, "r_y1": 674.649, "r_x2": 318.017, "r_y2": 665.943, "r_x3": 133.768, "r_y3": 665.943, "coord_origin": "TOPLEFT"}, "text": "accusam et justo duo dolores et ea rebum.", "orig": "accusam et justo duo dolores et ea rebum.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. 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At vero eos et accusam et justo duo dolores et ea rebum."}, {"label": "picture", "id": 2, "page_no": 1, "cluster": {"id": 2, "label": "picture", "bbox": {"l": 218.8155517578125, "t": 278.0153503417969, "r": 391.96246337890625, "b": 508.89410400390625, "coord_origin": "TOPLEFT"}, "confidence": 0.9837717413902283, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 4, "page_no": 1, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 303.133, "t": 695.831, "r": 308.114, "b": 704.537, "coord_origin": "TOPLEFT"}, "confidence": 0.8888521790504456, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.133, "r_y0": 704.537, "r_x1": 308.114, "r_y1": 704.537, "r_x2": 308.114, "r_y2": 695.831, "r_x3": 303.133, "r_y3": 695.831, "coord_origin": "TOPLEFT"}, "text": "2", "orig": "2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}]}}] \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/powerpoint_sample.pptx.json b/tests/data/groundtruth/docling_v2/powerpoint_sample.pptx.json index edb629a9..6783cf3f 100644 --- a/tests/data/groundtruth/docling_v2/powerpoint_sample.pptx.json +++ b/tests/data/groundtruth/docling_v2/powerpoint_sample.pptx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "powerpoint_sample", "origin": { "mimetype": "application/vnd.ms-powerpoint", diff --git a/tests/data/groundtruth/docling_v2/powerpoint_with_image.pptx.json b/tests/data/groundtruth/docling_v2/powerpoint_with_image.pptx.json index 7698096b..192ef753 100644 --- a/tests/data/groundtruth/docling_v2/powerpoint_with_image.pptx.json +++ b/tests/data/groundtruth/docling_v2/powerpoint_with_image.pptx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "powerpoint_with_image", "origin": { "mimetype": "application/vnd.ms-powerpoint", diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt b/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt index 5b93cda9..bea60803 100644 --- a/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt +++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.doctags.txt @@ -1,223 +1,229 @@ - -Row and Column Access Control +Front cover + +Row and Column Access Control Support in IBM DB2 for i -ibm.com +ibm.com /redbooks Contents - +Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiTrademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiDB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAuthors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiNow you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiComments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiStay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xivChapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 82.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 82.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 92.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 92.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 92.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 102.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 143.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.4 Establishing and controlling accessibility by using the RCAC rule text. . . . . . . . . . . . . 213.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . . 233.6.2 Creating group profiles for the users and their roles. . . . . . . . . . . . . . . . . . . . . . . 233.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.6.5 Defining and creating column masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32 ' Copyright IBM Corp. 2014. All rights reserved. iii -Solution Brief +DB2 for i Center of Excellence +Solution Brief IBM Systems Lab Services and Training Highlights -/g115/g3 -/g115/g3 -/g115/g3 -/g115/g3 +/g115/g3 /g40/g81/g75/g68/g81/g70/g72/g3 /g87/g75/g72/g3 /g83/g72/g85/g73/g82/g85/g80/g68/g81/g70/g72/g3 /g82/g73/g3 /g92/g82/g88/g85/g3 /g71/g68/g87/g68/g69/g68/g86/g72/g3 /g82/g83/g72/g85/g68/g87/g76/g82/g81/g86 +/g115/g3 /g40/g68/g85/g81/g3 /g74/g85/g72/g68/g87/g72/g85/g3 /g85 /g72/g87/g88/g85/g81/g3 /g82/g81/g3 /g44/g55/g3 /g83/g85 /g82/g77/g72/g70/g87/g86/g3 /g87/g75/g85 /g82/g88/g74/g75/g3 /g80/g82/g71/g72/g85/g81/g76/g93/g68/g87/g76/g82/g81/g3 /g82/g73/g3 /g71/g68/g87/g68/g69/g68/g86/g72/g3 /g68/g81/g71/g3 /g68/g83/g83/g79/g76/g70/g68/g87/g76/g82/g81/g86 +/g115/g3 /g53/g72/g79/g92/g3 /g82/g81/g3 /g44/g37/g48/g3 /g72/g91/g83/g72/g85/g87/g3 /g70/g82/g81/g86/g88/g79/g87/g76/g81/g74/g15/g3 /g86/g78/g76/g79/g79/g86/g3 /g86/g75/g68/g85/g76/g81/g74/g3 /g68/g81/g71/g3 /g85/g72/g81/g82/g90/g81/g3 /g86/g72/g85/g89/g76/g70/g72/g86 +/g115/g3 /g55 /g68/g78/g72/g3 /g68/g71/g89/g68/g81/g87/g68/g74/g72/g3 /g82/g73/g3 /g68/g70/g70/g72/g86/g86/g3 /g87/g82/g3 /g68/g3 /g90/g82/g85/g79/g71/g90/g76/g71/g72/g3 /g86/g82/g88/g85/g70/g72/g3 /g82/g73/g3 /g72/g91/g83/g72/g85/g87/g76/g86/g72 Power Services -DB2 for i +DB2 for i Center of Excellence Expert help to achieve your business requirements We build confident, satisfied clients -No one else has the vast consulting experiences, skills sharing and +No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you. Because no one else is IBM. -With combined experiences and direct access to development groups, +With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. Who we are, some of what we do Global CoE engagements cover topics including: -rglyph -rglyph -rglyph -rglyph -rglyph -rglyph -rglyph -rglyph -rglyph +rglyph Database performance and scalability +rglyph Advanced SQL knowledge and skills transfer +rglyph Business intelligence and analytics +rglyph DB2 Web Query +rglyph Query/400 modernization for better reporting and analysis capabilities +rglyph Database modernization and re-engineering +rglyph Data-centric architecture and design +rglyph Extremely large database and overcoming limits to growth +rglyph ISV education and enablement Preface -This IBMfi Redpaper™ publication provides information about the IBM i 7.2 feature of IBM -This paper is intended for database engineers, data-centric application developers, and -This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with +This IBMfi Redpaper™ publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. +This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US. -Jim Bainbridge +Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office. -Hernando Bedoya +Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com . Authors ' Copyright IBM Corp. 2014. All rights reserved. xi 1 -Securing and protecting IBM DB2 -Recent news headlines are filled with reports of data breaches and cyber-attacks impacting -Businesses must make a serious effort to secure their data and recognize that securing -This chapter describes how you can secure and protect data in DB2 for i. The following topics -/SM590000 -/SM590000 -/SM590000 +Chapter 1. +Securing and protecting IBM DB2 data +Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. +Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement. +This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter: +/SM590000 Security fundamentals +/SM590000 Current state of IBM i security +/SM590000 DB2 for i security controls -1 -2 +1 http://www.idtheftcenter.org +2 http://www.ponemon.org / ' Copyright IBM Corp. 2014. All rights reserved. 1 1.1 Security fundamentals -Before reviewing database security techniques, there are two fundamental steps in securing -/SM590000 -The monitoring and assessment of adherence to the security policy determines whether +Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described: +/SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. +The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. A security policy is what defines whether the system and its settings are secure (or not). -/SM590000 +/SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. -With your eyes now open to the importance of securing information assets, the rest of this +With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i. 1.2 Current state of IBM i security -Because of the inherently secure nature of IBM i, many clients rely on the default system -Even more disturbing is that many IBM i clients remain in this state, despite the news -Traditionally, IBM i applications have employed menu-based security to counteract this default +Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE. +Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data. +Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data. 2 Row and Column Access Control Support in IBM DB2 for i -Many businesses are trying to limit data access to a need-to-know basis. This security goal +Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage. 1.3.1 Existing row and column control -Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL -Using SQL views to limit access to a subset of the data in a table also has its own set of -Even if you are willing to live with these performance and management issues, a user with +Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator. +Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases. +Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view. Figure 1-2 Existing row and column controls 4 Row and Column Access Control Support in IBM DB2 for i 2.1.6 Change Function Usage CL command The following CL commands can be used to work with, display, or change function usage IDs: -/SM590000 -/SM590000 -/SM590000 +/SM590000 Work Function Usage ( WRKFCNUSG ) +/SM590000 Change Function Usage ( CHGFCNUSG ) +/SM590000 Display Function Usage ( DSPFCNUSG ) -For example, the following +For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules: CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED) 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view -The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 -Table 2-1 FUNCTION_USAGE view -To discover who has authorization to define and manage RCAC, you can use the query that is +The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view. +Column nameData typeDescriptionFUNCTION_IDVARCHAR(30)ID of the function.USER_NAMEVARCHAR(10)Name of the user profile that has a usage setting for this function.USAGEVARCHAR(7)Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function.USER_TYPEVARCHAR(5)Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group.Table 2-1 FUNCTION_USAGE view +To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. Example 2-1 Query to determine who has authority to define and manage RCAC SELECT function_id, +user_name, +usage, user_type FROM function_usage WHERE function_id='QIBM_DB_SECADM' ORDER BY user_name; 2.2 Separation of duties -Separation of duties helps businesses comply with industry regulations or organizational +Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority. 10 Row and Column Access Control Support in IBM DB2 for i -For example, assume that a business has assigned the duty to manage security on IBM i to -In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, -QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special -QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows -A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function -Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to -Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority +For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security. +In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table. +QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group. +QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table. +A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself. +Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools. +User action*JOBCTLQIBM_DB_SECADMQIBM_DB_SQLADMQIBM_DB_SYSMONNo AuthoritySET CURRENT DEGREE (SQL statement)XXCHGQRYA command targeting a different user's jobXXSTRDBMON or ENDDBMON commands targeting a different user's jobXXSTRDBMON or ENDDBMON commands targeting a job that matches the current userXXXXQUSRJOBI() API format 900 or System i Navigator's SQL Details for JobXXXVisual Explain within Run SQL scriptsXXXXVisual Explain outside of Run SQL scriptsXXANALYZE PLAN CACHE procedureXXDUMP PLAN CACHE procedureXXMODIFY PLAN CACHE procedureXXMODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority)XXCHANGE PLAN CACHE SIZE procedure (currently does not check authority)XXTable 2-2 Comparison of the different function usage IDs and *JOBCTL authority Chapter 2. Roles and separation of duties 11 -The SQL +The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules. Figure 3-1 CREATE PERMISSION SQL statement Column mask -A column mask is a database object that manifests a column value access control rule for a +A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number. Chapter 3. Row and Column Access Control 15 Table 3-1 summarizes these special registers and their values. -Table 3-1 Special registers and their corresponding values -Figure 3-5 shows the difference in the special register values when an adopted authority is -/SM590000 -/SM590000 -/SM590000 -/SM590000 -/SM590000 +Special registerCorresponding valueUSER or SESSION_USERThe effective user of the thread excluding adopted authority.CURRENT_USERThe effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER.SYSTEM_USERThe authorization ID that initiated the connection.Table 3-1 Special registers and their corresponding values +Figure 3-5 shows the difference in the special register values when an adopted authority is used: +/SM590000 A user connects to the server using the user profile ALICE. +/SM590000 USER and CURRENT USER initially have the same value of ALICE. +/SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called. +/SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority. +/SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE. Figure 3-5 Special registers and adopted authority 3.2.2 Built-in global variables -Built-in global variables are provided with the database manager and are used in SQL -IBM DB2 for i supports nine different built-in global variables that are read only and +Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables. +IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic. Chapter 3. Row and Column Access Control 19 Table 3-2 lists the nine built-in global variables. -Table 3-2 Built-in global variables +Global variableTypeDescriptionCLIENT_HOSTVARCHAR(255)Host name of the current client as returned by the systemCLIENT_IPADDRVARCHAR(128)IP address of the current client as returned by the systemCLIENT_PORTINTEGERPort used by the current client to communicate with the serverPACKAGE_NAMEVARCHAR(128)Name of the currently running packagePACKAGE_SCHEMAVARCHAR(128)Schema name of the currently running packagePACKAGE_VERSIONVARCHAR(64)Version identifier of the currently running packageROUTINE_SCHEMAVARCHAR(128)Schema name of the currently running routineROUTINE_SPECIFIC_NAMEVARCHAR(128)Name of the currently running routineROUTINE_TYPECHAR(1)Type of the currently running routineTable 3-2 Built-in global variables 3.3 VERIFY_GROUP_FOR_USER function -The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily -If a special register value is in the list of user profiles or it is a member of a group profile +The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error. +If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value. Here is an example of using the VERIFY_GROUP_FOR_USER function: -1. -2. -3. +1. There are user profiles for MGR, JANE, JUDY, and TONY. +2. The user profile JANE specifies a group profile of MGR. +3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1: -<_unknown_>VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') -<_unknown_>VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') +<_unknown_>VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY') 20 Row and Column Access Control Support in IBM DB2 for i RETURN CASE -<_unknown_>WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 -2. -- -- -- -- +<_unknown_>WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ; +2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones: +-Human Resources can see the unmasked TAX_ID of the employees. +-Employees can see only their own unmasked TAX_ID. +-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234). +-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX. To implement this column mask, run the SQL statement that is shown in Example 3-9. -<_unknown_>CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES +<_unknown_>CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; Example 3-9 Creating a mask on the TAX_ID column Chapter 3. Row and Column Access Control 27 -3. +3. Figure 3-10 shows the masks that are created in the HR_SCHEMA. Figure 3-10 Column masks shown in System i Navigator 3.6.6 Activating RCAC -Now that you have created the row permission and the two column masks, RCAC must be -1. +Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps: +1. Run the SQL statements that are shown in Example 3-10. Example 3-10 Activating RCAC on the EMPLOYEES table /* Active Row Access Control (permissions) */ /* Active Column Access Control (masks) */ +ALTER TABLE HR_SCHEMA.EMPLOYEES +ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL; -2. +2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables , right-click the EMPLOYEES table, and click Definition . Figure 3-11 Selecting the EMPLOYEES table from System i Navigator 28 Row and Column Access Control Support in IBM DB2 for i -2. -3. +2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. +3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause. Figure 4-68 Visual Explain with RCAC enabled Figure 4-69 Index advice with no RCAC Chapter 4. Implementing Row and Column Access Control: Banking example 77 -<_unknown_>THEN C . CUSTOMER_TAX_ID -<_unknown_>CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C +<_unknown_>THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ; 124 Row and Column Access Control Support in IBM DB2 for i -Row and Column Access Control -Implement roles and -This IBM Redpaper publication provides information about the IBM i 7.2 -Leverage row -Protect columns by -This paper is intended for database engineers, data-centric application +Back cover +Row and Column Access Control Support in IBM DB2 for i +Implement roles and separation of duties +Leverage row permissions on the database +This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. +Protect columns by defining column masks +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. -INTERNATIONAL -BUILDING TECHNICAL -IBM Redbooks are developed -For more information: +INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION +BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE +IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. 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developers, and"}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8, "t": 471.371, "r": 547.246, "b": 462.158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with", "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with"}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.4, "t": 416.351, "r": 335.697, "b": 407.138, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "Jim Bainbridge", "text": "Jim Bainbridge"}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.4, "t": 264.374, "r": 348.368, "b": 255.16100000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 15]}], "orig": "Hernando Bedoya", "text": "Hernando Bedoya"}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.8, "t": 503.699, "r": 125.338, "b": 488.936, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Authors", "text": "Authors", "level": 1}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 64.8, "t": 36.46199999999999, "r": 257.252, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 538.86, "t": 37.150999999999954, "r": 547.208, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "xi", "text": "xi"}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 500.4, "t": 698.831, "r": 522.618, "b": 661.868, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 537.114, "r": 549.157, "b": 513.082, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Securing and protecting IBM DB2", "text": "Securing and protecting IBM DB2", "level": 1}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 443.291, "r": 542.266, "b": 434.078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting", "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting"}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 349.272, "r": 525.074, "b": 340.059, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "Businesses must make a serious effort to secure their data and recognize that securing", "text": "Businesses must make a serious effort to secure their data and recognize that securing"}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 291.313, "r": 547.404, "b": 282.1, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics", "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics"}, {"self_ref": "#/texts/48", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 261.82500000000005, "r": 141.78, "b": 253.42499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/49", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 249.82600000000002, "r": 141.78, "b": 241.42499999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/50", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 237.82600000000002, "r": 141.78, "b": 229.42599999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/51", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 76.15999999999997, "r": 146.34, "b": 70.79600000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 66.42600000000004, "r": 143.908, "b": 60.543000000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 64.8, "t": 36.46199999999999, "r": 257.252, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 541.68, "t": 37.150999999999954, "r": 547.218, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1"}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.8, "t": 717.659, "r": 267.39, "b": 702.896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1.1 Security fundamentals", "text": "1.1 Security fundamentals", "level": 1}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 685.391, "r": 545.014, "b": 676.178, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Before reviewing database security techniques, there are two fundamental steps in securing", "text": "Before reviewing database security techniques, there are two fundamental steps in securing"}, {"self_ref": "#/texts/57", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 655.904, "r": 141.78, "b": 647.503, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/58", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 151.199, "t": 603.372, "r": 541.654, "b": 594.159, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "The monitoring and assessment of adherence to the security policy determines whether", "text": "The monitoring and assessment of adherence to the security policy determines whether", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 151.199, "t": 514.393, "r": 542.001, "b": 505.18, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A security policy is what defines whether the system and its settings are secure (or not).", "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.799, "t": 496.906, "r": 141.779, "b": 488.505, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 403.393, "r": 535.371, "b": 394.18, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "With your eyes now open to the importance of securing information assets, the rest of this", "text": "With your eyes now open to the importance of securing information assets, the rest of this"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.8, "t": 353.699, "r": 323.37, "b": 338.936, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "1.2 Current state of IBM i security", "text": "1.2 Current state of IBM i security", "level": 1}, {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 321.371, "r": 530.314, "b": 312.158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system", "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 263.352, "r": 513.913, "b": 254.139, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news", "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news"}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 193.33299999999997, "r": 547.652, "b": 184.12, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default", "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default"}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 72.822, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2"}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 87.84, "t": 36.46199999999999, "r": 328.556, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 720.491, "r": 541.139, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal", "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal"}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 64.8, "t": 618.665, "r": 301.46, "b": 606.677, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "1.3.1 Existing row and column control", "text": "1.3.1 Existing row and column control", "level": 1}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 592.511, "r": 534.91, "b": 583.298, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL", "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL"}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 522.492, "r": 526.893, "b": 513.279, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of", "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of"}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 464.473, "r": 536.476, "b": 455.26, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "Even if you are willing to live with these performance and management issues, a user with", "text": "Even if you are willing to live with these performance and management issues, a user with"}, {"self_ref": "#/texts/73", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 100.18200000000002, "r": 316.462, "b": 91.85699999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "Figure 1-2 Existing row and column controls", "text": "Figure 1-2 Existing row and column controls"}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 72.822, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4"}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 87.84, "t": 36.46199999999999, "r": 328.556, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.8, "t": 720.665, "r": 335.493, "b": 708.677, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "2.1.6 Change Function Usage CL command", "text": "2.1.6 Change Function Usage CL command", "level": 1}, {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 694.511, "r": 547.251, "b": 685.298, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"self_ref": "#/texts/78", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 677.024, "r": 141.78, "b": 668.623, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/79", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.801, "t": 665.024, "r": 141.781, "b": 656.623, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/80", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.801, "t": 653.024, "r": 141.781, "b": 644.624, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 631.512, "r": 255.049, "b": 622.299, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "For example, the following", "text": "For example, the following"}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.801, "t": 602.025, "r": 441.597, "b": 593.624, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"self_ref": "#/texts/83", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.8, "t": 572.645, "r": 544.461, "b": 560.657, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "level": 1}, {"self_ref": "#/texts/84", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 546.491, "r": 519.527, "b": 537.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 80]}], "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1", "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/tables/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 512.442, "r": 283.949, "b": 504.117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Table 2-1 FUNCTION_USAGE view", "text": "Table 2-1 FUNCTION_USAGE view"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 339.491, "r": 547.589, "b": 330.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is", "text": "To discover who has authorization to define and manage RCAC, you can use the query that is"}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 305.442, "r": 462.374, "b": 297.117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"self_ref": "#/texts/88", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 288.043, "r": 251.699, "b": 279.64300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "SELECT function_id,", "text": "SELECT function_id,"}, {"self_ref": "#/texts/89", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 252.04399999999998, "r": 236.699, "b": 243.64300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "user_type", "text": "user_type"}, {"self_ref": "#/texts/90", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 240.04399999999998, "r": 261.718, "b": 231.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "FROM function_usage", "text": "FROM function_usage"}, {"self_ref": "#/texts/91", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 228.04399999999998, "r": 331.677, "b": 219.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "WHERE function_id='QIBM_DB_SECADM'", "text": "WHERE function_id='QIBM_DB_SECADM'"}, {"self_ref": "#/texts/92", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 216.04399999999998, "r": 241.739, "b": 207.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "ORDER BY user_name;", "text": "ORDER BY user_name;"}, {"self_ref": "#/texts/93", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.8, "t": 171.779, "r": 249.582, "b": 157.01599999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "2.2 Separation of duties", "text": "2.2 Separation of duties", "level": 1}, {"self_ref": "#/texts/94", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 139.45100000000002, "r": 529.043, "b": 130.23800000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 88]}], "orig": "Separation of duties helps businesses comply with industry regulations or organizational", "text": "Separation of duties helps businesses comply with industry regulations or organizational"}, {"self_ref": "#/texts/95", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 78.402, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10"}, {"self_ref": "#/texts/96", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 93.42, "t": 36.46199999999999, "r": 334.258, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/97", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 720.491, "r": 542.703, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 88]}], "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to", "text": "For example, assume that a business has assigned the duty to manage security on IBM i to"}, {"self_ref": "#/texts/98", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 638.472, "r": 544.17, "b": 629.259, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 88]}], "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities,", "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities,"}, {"self_ref": "#/texts/99", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 580.513, "r": 538.66, "b": 571.3, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 80]}], "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special", "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special"}, {"self_ref": "#/texts/100", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 546.494, "r": 545.805, "b": 537.281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 85]}], "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows", "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows"}, {"self_ref": "#/texts/101", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 500.475, "r": 533.79, "b": 491.262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function", "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function"}, {"self_ref": "#/texts/102", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 442.515, "r": 543.076, "b": 433.302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to", "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to"}, {"self_ref": "#/texts/103", "parent": {"cref": "#/tables/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 64.8, "t": 408.462, "r": 391.767, "b": 400.137, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 355.32, "t": 36.46199999999999, "r": 523.523, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Chapter 2. Roles and separation of duties", "text": "Chapter 2. Roles and separation of duties"}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11"}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8, "t": 720.491, "r": 179.591, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "The SQL", "text": "The SQL"}, {"self_ref": "#/texts/107", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8, "t": 377.862, "r": 341.985, "b": 369.537, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text": "Figure 3-1 CREATE PERMISSION SQL statement"}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 136.8, "t": 352.056, "r": 215.383, "b": 340.956, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Column mask", "text": "Column mask", "level": 1}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 136.8, "t": 336.911, "r": 542.776, "b": 327.698, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A column mask is a database object that manifests a column value access control rule for a", "text": "A column mask is a database object that manifests a column value access control rule for a"}, {"self_ref": "#/texts/110", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 344.94, "t": 36.46199999999999, "r": 523.584, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15"}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 720.491, "r": 412.174, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Table 3-1 summarizes these special registers and their values.", "text": "Table 3-1 summarizes these special registers and their values."}, {"self_ref": "#/texts/113", "parent": {"cref": "#/tables/3"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 698.502, "r": 372.618, "b": 690.177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Table 3-1 Special registers and their corresponding values", "text": "Table 3-1 Special registers and their corresponding values"}, {"self_ref": "#/texts/114", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 577.511, "r": 538.503, "b": 568.298, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 91]}], "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is", "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is"}, {"self_ref": "#/texts/115", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 548.024, "r": 141.78, "b": 539.623, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/116", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 531.044, "r": 141.78, "b": 522.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/117", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 514.064, "r": 141.78, "b": 505.664, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/118", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 485.025, "r": 141.78, "b": 476.624, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/119", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.801, "t": 444.045, "r": 141.781, "b": 435.645, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "/SM590000", "text": "/SM590000", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 195.28200000000004, "r": 341.274, "b": 186.957, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 3-5 Special registers and adopted authority", "text": "Figure 3-5 Special registers and adopted authority"}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 140.732, "t": 404.558, "r": 216.376, "b": 396.624, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Signed on as ALICE", "text": "Signed on as ALICE"}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 380.668, "r": 191.711, "b": 372.734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 368.723, "r": 232.566, "b": 360.789, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE"}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 344.833, "r": 183.266, "b": 336.9, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "CALL proc1", "text": "CALL proc1"}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.43, "t": 317.957, "r": 174.059, "b": 310.024, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Proc1:", "text": "Proc1:"}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.43, "t": 270.177, "r": 201.665, "b": 262.244, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.43, "t": 258.23199999999997, "r": 234.579, "b": 250.29899999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "CURRENT USER = JOE", "text": "CURRENT USER = JOE"}, {"self_ref": "#/texts/128", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 225.38400000000001, "r": 191.711, "b": 217.45000000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE"}, {"self_ref": "#/texts/129", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 64.8, "t": 166.44499999999994, "r": 247.016, "b": 154.457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "3.2.2 Built-in global variables", "text": "3.2.2 Built-in global variables", "level": 1}, {"self_ref": "#/texts/130", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 140.29099999999994, "r": 518.01, "b": 131.07799999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 84]}], "orig": "Built-in global variables are provided with the database manager and are used in SQL", "text": "Built-in global variables are provided with the database manager and are used in SQL"}, {"self_ref": "#/texts/131", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 106.27200000000005, "r": 504.455, "b": 97.05899999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and", "text": "IBM DB2 for i supports nine different built-in global variables that are read only and"}, {"self_ref": "#/texts/132", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 344.94, "t": 36.46199999999999, "r": 523.584, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/133", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19"}, {"self_ref": "#/texts/134", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 720.491, "r": 342.519, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3-2 lists the nine built-in global variables.", "text": "Table 3-2 lists the nine built-in global variables."}, {"self_ref": "#/texts/135", "parent": {"cref": "#/tables/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 64.8, "t": 698.502, "r": 201.196, "b": 690.177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Table 3-2 Built-in global variables", "text": "Table 3-2 Built-in global variables"}, {"self_ref": "#/texts/136", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 64.8, "t": 469.799, "r": 384.345, "b": 455.036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text": "3.3 VERIFY_GROUP_FOR_USER function", "level": 1}, {"self_ref": "#/texts/137", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 437.471, "r": 542.844, "b": 428.258, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily", "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily"}, {"self_ref": "#/texts/138", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 343.513, "r": 525.156, "b": 334.3, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 96]}], "orig": "If a special register value is in the list of user profiles or it is a member of a group profile", "text": "If a special register value is in the list of user profiles or it is a member of a group profile"}, {"self_ref": "#/texts/139", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 297.493, "r": 458.427, "b": 288.28, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"self_ref": "#/texts/140", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 280.454, "r": 145.089, "b": 271.241, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/141", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 263.47399999999993, "r": 145.089, "b": 254.26099999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/142", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 246.49400000000003, "r": 145.099, "b": 237.28099999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/143", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.2, "t": 217.00700000000006, "r": 366.057, "b": 208.606, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "captions": [], "references": [], "footnotes": [], "image": null, "code_language": "unknown"}, {"self_ref": "#/texts/144", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.2, "t": 193.00700000000006, "r": 451.016, "b": 184.60699999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "captions": [], "references": [], "footnotes": [], "image": null, "code_language": "unknown"}, {"self_ref": "#/texts/145", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 78.402, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20"}, {"self_ref": "#/texts/146", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 93.42, "t": 36.46199999999999, "r": 334.258, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"self_ref": "#/texts/147", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 720.043, "r": 166.739, "b": 711.642, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "RETURN", "text": "RETURN"}, {"self_ref": "#/texts/148", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 708.043, "r": 156.779, "b": 699.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "CASE", "text": "CASE"}, {"self_ref": "#/texts/149", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 696.043, "r": 466.615, "b": 687.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "captions": [], "references": [], "footnotes": [], "image": null, "code_language": "unknown"}, {"self_ref": "#/texts/150", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 516.494, "r": 145.098, "b": 507.281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/151", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.039, "t": 487.515, "r": 157.577, "b": 478.302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/152", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.039, "t": 470.475, "r": 157.577, "b": 461.262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/153", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.039, "t": 453.495, "r": 157.577, "b": 444.282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/154", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.039, "t": 424.516, "r": 157.577, "b": 415.303, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "-", "text": "-", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/155", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 151.2, "t": 407.476, "r": 530.024, "b": 398.263, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/156", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/157"}], "content_layer": "body", "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 368.023, "r": 381.657, "b": 359.623, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES", "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES", "captions": [{"cref": "#/texts/157"}], "references": [], "footnotes": [], "image": null, "code_language": "unknown"}, {"self_ref": "#/texts/157", "parent": {"cref": "#/texts/156"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 385.482, "r": 351.999, "b": 377.157, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"self_ref": "#/texts/158", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 344.94, "t": 36.46199999999999, "r": 523.584, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 720.491, "r": 145.089, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.8, "t": 618.462, "r": 293.146, "b": 610.137, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Figure 3-10 Column masks shown in System i Navigator", "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 64.8, "t": 589.625, "r": 203.983, "b": 577.637, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "3.6.6 Activating RCAC", "text": "3.6.6 Activating RCAC", "level": 1}, {"self_ref": "#/texts/163", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 563.471, "r": 537.1, "b": 554.258, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "Now that you have created the row permission and the two column masks, RCAC must be", "text": "Now that you have created the row permission and the two column masks, RCAC must be"}, {"self_ref": "#/texts/164", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 522.492, "r": 145.089, "b": 513.279, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "1.", "text": "1.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/165", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 500.442, "r": 375.273, "b": 492.117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table", "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "level": 1}, {"self_ref": "#/texts/166", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 483.043, "r": 376.677, "b": 474.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "/* Active Row Access Control (permissions) */", "text": "/* Active Row Access Control (permissions) */", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/167", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 471.043, "r": 376.677, "b": 462.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "/* Active Column Access Control (masks) */", "text": "/* Active Column Access Control (masks) */", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/168", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 435.044, "r": 291.718, "b": 426.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"self_ref": "#/texts/169", "parent": {"cref": "#/groups/13"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 411.492, "r": 145.12, "b": 402.279, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "2.", "text": "2.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/170", "parent": {"cref": "#/pictures/12"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.8, "t": 142.962, "r": 347.441, "b": 134.63699999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"self_ref": "#/texts/171", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 78.402, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28"}, {"self_ref": "#/texts/172", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", 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Visual Explain with RCAC enabled"}, {"self_ref": "#/texts/175", "parent": {"cref": "#/groups/14"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8, "t": 285.431, "r": 145.089, "b": 276.2180000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "3.", "text": "3.", "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/176", "parent": {"cref": "#/pictures/14"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 64.8, "t": 124.48199999999997, "r": 227.119, "b": 116.15700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Figure 4-69 Index advice with no RCAC", "text": "Figure 4-69 Index advice with no RCAC"}, {"self_ref": "#/texts/177", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 214.8, "t": 36.46199999999999, "r": 523.576, "b": 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Implementing Row and Column Access Control: Banking example", "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"self_ref": "#/texts/178", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "77", "text": "77"}, {"self_ref": "#/texts/179", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 16, "bbox": {"l": 64.8, "t": 720.057, "r": 177.119, "b": 712.466, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "THEN C . CUSTOMER_TAX_ID", "text": "THEN C . 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[{"page_no": 3, "bbox": {"l": 280.24, "t": 261.78600000000006, "r": 399.726, "b": 254.38200000000006, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 83]}], "orig": "rglyph Data-centric architecture and design", "text": "rglyph Data-centric architecture and design", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/34", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.24, "t": 251.60300000000007, "r": 466.853, "b": 244.19900000000007, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 103]}], "orig": "rglyph Extremely large database and overcoming limits to growth", "text": "rglyph Extremely large database and overcoming limits to growth", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/35", "parent": {"cref": "#/groups/1"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 3, "bbox": {"l": 280.24, "t": 241.42100000000005, "r": 382.284, "b": 234.01700000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 75]}], "orig": "rglyph ISV education and enablement", "text": "rglyph ISV education and enablement", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.8, "t": 718.152, "r": 151.469, "b": 695.952, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Preface", "text": "Preface", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8, "t": 659.351, "r": 547.557, "b": 590.139, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 469]}], "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8, "t": 577.393, "r": 546.415, "b": 532.18, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 309]}], "orig": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 136.8, "t": 471.371, "r": 547.246, "b": 450.158, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 172]}], "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.4, "t": 416.351, "r": 541.74, "b": 275.14, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 684]}], "orig": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 263.4, "t": 264.374, "r": 541.582, "b": 111.16300000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 726]}], "orig": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com .", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 64.8, "t": 503.699, "r": 125.338, "b": 488.936, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "Authors", "text": "Authors", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 64.8, "t": 36.46199999999999, "r": 257.252, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 4, "bbox": {"l": 538.86, "t": 37.150999999999954, "r": 547.208, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "xi", "text": "xi", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 500.4, "t": 698.831, "r": 522.618, "b": 661.868, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 81.0, "t": 523.457, "r": 115.155, "b": 517.019, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Chapter 1.", "text": "Chapter 1.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 537.114, "r": 549.157, "b": 482.122, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "Securing and protecting IBM DB2 data", "text": "Securing and protecting IBM DB2 data", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 443.291, "r": 547.503, "b": 362.079, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 641]}], "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 349.272, "r": 527.156, "b": 304.06, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 304]}], "orig": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 291.313, "r": 547.404, "b": 270.1, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 122]}], "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/51", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 262.274, "r": 250.2, "b": 253.06100000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "/SM590000 Security fundamentals", "text": "/SM590000 Security fundamentals", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/52", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 250.274, "r": 282.955, "b": 241.06100000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "/SM590000 Current state of IBM i security", "text": "/SM590000 Current state of IBM i security", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/53", "parent": {"cref": "#/groups/2"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 238.274, "r": 264.868, "b": 229.06100000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "/SM590000 DB2 for i security controls", "text": "/SM590000 DB2 for i security controls", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 76.15999999999997, "r": 258.332, "b": 67.27999999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "1 http://www.idtheftcenter.org", "text": "1 http://www.idtheftcenter.org", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 136.8, "t": 66.42600000000004, "r": 234.059, "b": 57.02800000000002, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "2 http://www.ponemon.org /", "text": "2 http://www.ponemon.org /", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 64.8, "t": 36.46199999999999, "r": 257.252, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text": "' Copyright IBM Corp. 2014. All rights reserved.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 5, "bbox": {"l": 541.68, "t": 37.150999999999954, "r": 547.218, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "1", "text": "1", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.8, "t": 717.659, "r": 267.39, "b": 702.896, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "1.1 Security fundamentals", "text": "1.1 Security fundamentals", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 685.391, "r": 545.014, "b": 664.178, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 133]}], "orig": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/60", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 656.513, "r": 547.354, "b": 611.139, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 355]}], "orig": "/SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "text": "/SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/61", "parent": {"cref": "#/groups/3"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 151.199, "t": 603.372, "r": 547.57, "b": 522.16, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 587]}], "orig": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 151.199, "t": 514.393, "r": 542.001, "b": 505.18, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "A security policy is what defines whether the system and its settings are secure (or not).", "text": "A security policy is what defines whether the system and its settings are secure (or not).", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/63", "parent": {"cref": "#/groups/4"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 6, "bbox": {"l": 136.799, "t": 497.513, "r": 547.407, "b": 416.139, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 567]}], "orig": "/SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "text": "/SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 403.393, "r": 535.371, "b": 382.18, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 179]}], "orig": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 64.8, "t": 353.699, "r": 323.37, "b": 338.936, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 36]}], "orig": "1.2 Current state of IBM i security", "text": "1.2 Current state of IBM i security", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 321.371, "r": 547.567, "b": 276.159, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 306]}], "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 263.352, "r": 547.593, "b": 206.14, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 405]}], "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 136.8, "t": 193.33299999999997, "r": 547.652, "b": 112.12199999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 640]}], "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 72.822, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "2", "text": "2", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 6, "bbox": {"l": 87.84, "t": 36.46199999999999, "r": 328.556, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 720.491, "r": 544.312, "b": 639.279, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 589]}], "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 64.8, "t": 618.665, "r": 301.46, "b": 606.677, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 38]}], "orig": "1.3.1 Existing row and column control", "text": "1.3.1 Existing row and column control", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 592.511, "r": 541.576, "b": 535.299, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 377]}], "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 522.492, "r": 547.69, "b": 477.28, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 464.473, "r": 547.541, "b": 431.261, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/76", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 136.8, "t": 100.18200000000002, "r": 316.462, "b": 91.85699999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "Figure 1-2 Existing row and column controls", "text": "Figure 1-2 Existing row and column controls", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/77", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 180.959, "t": 408.75, "r": 212.54, "b": 402.612, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "User with", "text": "User with", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/78", "parent": {"cref": "#/pictures/8"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 170.006, "t": 401.254, "r": 226.926, "b": 395.116, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "*ALLOBJ access", "text": "*ALLOBJ access", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 72.822, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "4", "text": "4", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/80", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 7, "bbox": {"l": 87.84, "t": 36.46199999999999, "r": 328.556, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/81", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.8, "t": 720.665, "r": 335.493, "b": 708.677, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "2.1.6 Change Function Usage CL command", "text": "2.1.6 Change Function Usage CL command", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/82", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 694.511, "r": 547.251, "b": 685.298, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 90]}], "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/83", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 677.472, "r": 301.517, "b": 668.259, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 43]}], "orig": "/SM590000 Work Function Usage ( WRKFCNUSG )", "text": "/SM590000 Work Function Usage ( WRKFCNUSG )", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/84", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.801, "t": 665.472, "r": 313.398, "b": 656.259, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 45]}], "orig": "/SM590000 Change Function Usage ( CHGFCNUSG )", "text": "/SM590000 Change Function Usage ( CHGFCNUSG )", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/85", "parent": {"cref": "#/groups/5"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 8, "bbox": {"l": 136.801, "t": 653.472, "r": 310.817, "b": 644.259, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "/SM590000 Display Function Usage ( DSPFCNUSG )", "text": "/SM590000 Display Function Usage ( DSPFCNUSG )", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/86", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 631.512, "r": 512.547, "b": 610.299, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 126]}], "orig": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/87", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.801, "t": 602.025, "r": 441.597, "b": 593.624, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 61]}], "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/88", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.8, "t": 572.645, "r": 544.461, "b": 560.657, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 73]}], "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/89", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 546.491, "r": 519.527, "b": 525.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 130]}], "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/90", "parent": {"cref": "#/tables/1"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 512.442, "r": 283.949, "b": 504.117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "Table 2-1 FUNCTION_USAGE view", "text": "Table 2-1 FUNCTION_USAGE view", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/91", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 339.491, "r": 547.589, "b": 318.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 112]}], "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/92", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 305.442, "r": 462.374, "b": 297.117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/93", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 288.043, "r": 251.699, "b": 279.64300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 23]}], "orig": "SELECT function_id,", "text": "SELECT function_id,", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/94", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 276.043, "r": 241.739, "b": 267.64300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "user_name,", "text": "user_name,", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/95", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 264.044, "r": 221.699, "b": 255.64300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "usage,", "text": "usage,", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/96", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 252.04399999999998, "r": 236.699, "b": 243.64300000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "user_type", "text": "user_type", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/97", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 240.04399999999998, "r": 261.718, "b": 231.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "FROM function_usage", "text": "FROM function_usage", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/98", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 228.04399999999998, "r": 331.677, "b": 219.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "WHERE function_id='QIBM_DB_SECADM'", "text": "WHERE function_id='QIBM_DB_SECADM'", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/99", "parent": {"cref": "#/groups/6"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 216.04399999999998, "r": 241.739, "b": 207.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 21]}], "orig": "ORDER BY user_name;", "text": "ORDER BY user_name;", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/100", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 8, "bbox": {"l": 64.8, "t": 171.779, "r": 249.582, "b": 157.01599999999996, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 25]}], "orig": "2.2 Separation of duties", "text": "2.2 Separation of duties", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/101", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 8, "bbox": {"l": 136.8, "t": 139.45100000000002, "r": 547.592, "b": 82.23900000000003, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 463]}], "orig": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/102", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 78.402, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "10", "text": "10", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/103", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 8, "bbox": {"l": 93.42, "t": 36.46199999999999, "r": 334.258, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/104", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 720.491, "r": 542.703, "b": 651.279, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 516]}], "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/105", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 638.472, "r": 547.673, "b": 593.26, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/106", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 580.513, "r": 538.66, "b": 559.3, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/107", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 546.494, "r": 545.805, "b": 513.281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 204]}], "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/108", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 500.475, "r": 539.816, "b": 455.262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 285]}], "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/109", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 9, "bbox": {"l": 136.8, "t": 442.515, "r": 543.076, "b": 421.303, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/110", "parent": {"cref": "#/tables/2"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 9, "bbox": {"l": 64.8, "t": 408.462, "r": 391.767, "b": 400.137, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 81]}], "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/111", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 355.32, "t": 36.46199999999999, "r": 523.523, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 41]}], "orig": "Chapter 2. Roles and separation of duties", "text": "Chapter 2. Roles and separation of duties", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/112", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 9, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "11", "text": "11", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/113", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8, "t": 720.491, "r": 528.74, "b": 699.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 135]}], "orig": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/114", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 10, "bbox": {"l": 136.8, "t": 377.862, "r": 341.985, "b": 369.537, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 44]}], "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text": "Figure 3-1 CREATE PERMISSION SQL statement", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/115", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.134, "t": 651.792, "r": 251.916, "b": 642.629, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 17]}], "orig": "CREATE PERMISSION", "text": "CREATE PERMISSION", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/116", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 251.867, "t": 651.792, "r": 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"t": 670.114, "r": 530.748, "b": 662.776, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 47]}], "orig": "Names the row permission for row access control", "text": "Names the row permission for row access control", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/120", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.134, "t": 610.409, "r": 163.458, "b": 601.246, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "ON", "text": "ON", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/121", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 168.584, "t": 610.409, "r": 174.303, "b": 601.257, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/122", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 174.309, "t": 610.409, "r": 226.875, "b": 601.246, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "table name", "text": "table name", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/123", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 226.865, "t": 610.409, "r": 232.584, "b": 601.257, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ">", "text": ">", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/124", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 311.32, "t": 625.282, "r": 529.937, "b": 617.944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 59]}], "orig": "Identifies the table on which the row permission is created", "text": "Identifies the table on which the row permission is created", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/125", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.134, "t": 569.026, "r": 163.113, "b": 559.863, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "AS", "text": "AS", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/126", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 165.687, "t": 569.026, "r": 171.406, "b": 559.874, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "<", "text": "<", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/127", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 171.411, "t": 569.026, "r": 251.207, "b": 559.863, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 16]}], "orig": "correlation 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"content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 271.558, "t": 476.994, "r": 531.755, "b": 469.655, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 72]}], "orig": "Specifies that the row permission applies to all references of the table", "text": "Specifies that the row permission applies to all references of the table", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/139", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.134, "t": 417.289, "r": 185.175, "b": 408.126, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "ENABLE", "text": "ENABLE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/140", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 312.286, "t": 435.611, "r": 527.057, "b": 428.272, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Specifies that the row permission is to be initially enabled", "text": "Specifies that the row permission is to be initially enabled", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/141", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 311.734, "t": 414.919, "r": 363.82, "b": 407.581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Specifies tha", "text": "Specifies tha", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/142", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 358.655, "t": 414.919, "r": 527.606, "b": 407.581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "t the row perm ss on s o be nitially disabled", "text": "t the row perm ss on s o be nitially disabled", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/143", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 415.001, "t": 414.919, "r": 417.11, "b": 407.581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "i", "text": "i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/144", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 424.274, "t": 414.919, "r": 426.382, "b": 407.581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "i", "text": "i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/145", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 438.132, "t": 414.919, "r": 440.241, "b": 407.581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "i", "text": "i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/146", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 445.887, "t": 414.919, "r": 448.971, "b": 407.581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "t", "text": "t", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/147", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 467.367, "t": 414.919, "r": 469.476, "b": 407.581, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": "i", "text": "i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/148", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 148.134, "t": 403.495, "r": 187.618, "b": 394.331, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "DISABLE", "text": "DISABLE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/149", "parent": {"cref": "#/pictures/9"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 187.585, "t": 403.495, "r": 190.663, "b": 394.342, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1]}], "orig": ";", "text": ";", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/150", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 10, "bbox": {"l": 136.8, "t": 352.056, "r": 215.383, "b": 340.956, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Column mask", "text": "Column mask", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/151", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 10, "bbox": {"l": 136.8, "t": 336.911, "r": 542.776, "b": 291.699, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 297]}], "orig": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/152", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 344.94, "t": 36.46199999999999, "r": 523.584, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/153", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 10, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "15", "text": "15", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/154", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 720.491, "r": 412.174, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "Table 3-1 summarizes these special registers and their values.", "text": "Table 3-1 summarizes these special registers and their values.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/155", "parent": {"cref": "#/tables/3"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 698.502, "r": 372.618, "b": 690.177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 60]}], "orig": "Table 3-1 Special registers and their corresponding values", "text": "Table 3-1 Special registers and their corresponding values", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/156", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 577.511, "r": 538.503, "b": 556.298, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 97]}], "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/157", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 548.472, "r": 411.326, "b": 539.259, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "/SM590000 A user connects to the server using the user profile ALICE.", "text": "/SM590000 A user connects to the server using the user profile ALICE.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/158", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 531.492, "r": 453.245, "b": 522.279, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "/SM590000 USER and CURRENT USER initially have the same value of ALICE.", "text": "/SM590000 USER and CURRENT USER initially have the same value of ALICE.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/159", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 514.512, "r": 541.459, "b": 493.299, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 154]}], "orig": "/SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "text": "/SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/160", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 485.473, "r": 547.584, "b": 452.26, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 247]}], "orig": "/SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "text": "/SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/161", "parent": {"cref": "#/groups/7"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 11, "bbox": {"l": 136.801, "t": 444.493, "r": 547.483, "b": 423.281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 127]}], "orig": "/SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "text": "/SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/162", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 195.28200000000004, "r": 341.274, "b": 186.957, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 52]}], "orig": "Figure 3-5 Special registers and adopted authority", "text": "Figure 3-5 Special registers and adopted authority", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/163", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 140.732, "t": 404.558, "r": 216.376, "b": 396.624, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "Signed on as ALICE", "text": "Signed on as ALICE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/164", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 380.668, "r": 191.711, "b": 372.734, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/165", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 368.723, "r": 232.566, "b": 360.789, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "CURRENT USER = ALICE", "text": "CURRENT USER = ALICE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/166", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 344.833, "r": 183.266, "b": 336.9, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "CALL proc1", "text": "CALL proc1", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/167", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.43, "t": 317.957, "r": 174.059, "b": 310.024, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "Proc1:", "text": "Proc1:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/168", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.522, "t": 306.012, "r": 209.1, "b": 298.079, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 11]}], "orig": "Owner = JOE", "text": "Owner = JOE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/169", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 157.522, "t": 294.067, "r": 281.693, "b": 286.134, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 24]}], "orig": "SET OPTION USRPRF=*OWNER", "text": "SET OPTION USRPRF=*OWNER", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/170", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.43, "t": 270.177, "r": 201.665, "b": 262.244, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 12]}], "orig": "USER = ALICE", "text": "USER = ALICE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/171", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 148.43, "t": 258.23199999999997, "r": 234.579, "b": 250.29899999999998, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 18]}], "orig": "CURRENT USER = JOE", "text": "CURRENT USER = JOE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/172", "parent": {"cref": "#/pictures/10"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 138.476, "t": 225.38400000000001, "r": 232.566, "b": 205.505, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "USER = ALICE CURRENT USER = ALICE", "text": "USER = ALICE CURRENT USER = ALICE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/173", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 11, "bbox": {"l": 64.8, "t": 166.44499999999994, "r": 247.016, "b": 154.457, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 32]}], "orig": "3.2.2 Built-in global variables", "text": "3.2.2 Built-in global variables", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/174", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 140.29099999999994, "r": 518.01, "b": 119.07799999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 161]}], "orig": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/175", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 11, "bbox": {"l": 136.8, "t": 106.27200000000005, "r": 532.347, "b": 73.05899999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/176", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 344.94, "t": 36.46199999999999, "r": 523.584, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/177", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 11, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "19", "text": "19", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/178", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 720.491, "r": 342.519, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 51]}], "orig": "Table 3-2 lists the nine built-in global variables.", "text": "Table 3-2 lists the nine built-in global variables.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/179", "parent": {"cref": "#/tables/4"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 12, "bbox": {"l": 64.8, "t": 698.502, "r": 201.196, "b": 690.177, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "Table 3-2 Built-in global variables", "text": "Table 3-2 Built-in global variables", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/180", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 12, "bbox": {"l": 64.8, "t": 469.799, "r": 384.345, "b": 455.036, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 35]}], "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text": "3.3 VERIFY_GROUP_FOR_USER function", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/181", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 437.471, "r": 547.543, "b": 356.259, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 576]}], "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/182", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 343.513, "r": 547.386, "b": 310.3, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 235]}], "orig": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value.", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/183", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 297.493, "r": 458.427, "b": 288.28, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 63]}], "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/184", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 280.454, "r": 406.048, "b": 271.241, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/185", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 263.47399999999993, "r": 396.957, "b": 254.26099999999997, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 58]}], "orig": "2. The user profile JANE specifies a group profile of MGR.", "text": "2. The user profile JANE specifies a group profile of MGR.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/186", "parent": {"cref": "#/groups/8"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 12, "bbox": {"l": 136.8, "t": 246.49400000000003, "r": 536.577, "b": 225.28099999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 127]}], "orig": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/187", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 12, "bbox": {"l": 151.2, "t": 217.00700000000006, "r": 451.016, "b": 150.64699999999993, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "formatting": null, "hyperlink": null, "captions": [], "references": [], "footnotes": [], "image": null, "code_language": "unknown"}, {"self_ref": "#/texts/188", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 78.402, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "20", "text": "20", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/189", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 12, "bbox": {"l": 93.42, "t": 36.46199999999999, "r": 334.258, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/190", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 720.043, "r": 166.739, "b": 711.642, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 6]}], "orig": "RETURN", "text": "RETURN", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/191", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 708.043, "r": 156.779, "b": 699.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 4]}], "orig": "CASE", "text": "CASE", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/192", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 696.043, "r": 521.574, "b": 531.645, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 443]}], "orig": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;", "formatting": null, "hyperlink": null, "captions": [], "references": [], "footnotes": [], "image": null, "code_language": "unknown"}, {"self_ref": "#/texts/193", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 516.494, "r": 547.281, "b": 495.281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/194", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.039, "t": 487.515, "r": 469.118, "b": 478.302, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 62]}], "orig": "-Human Resources can see the unmasked TAX_ID of the employees.", "text": "-Human Resources can see the unmasked TAX_ID of the employees.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/195", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.039, "t": 470.475, "r": 403.932, "b": 461.262, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "-Employees can see only their own unmasked TAX_ID.", "text": "-Employees can see only their own unmasked TAX_ID.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/196", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.039, "t": 453.495, "r": 545.118, "b": 432.282, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 129]}], "orig": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234).", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/197", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 152.039, "t": 424.516, "r": 529.442, "b": 415.303, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 77]}], "orig": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/198", "parent": {"cref": "#/groups/9"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 13, "bbox": {"l": 151.2, "t": 407.476, "r": 530.024, "b": 398.263, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 82]}], "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/199", "parent": {"cref": "#/body"}, "children": [{"cref": "#/texts/200"}], "content_layer": "body", "label": "code", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 368.023, "r": 526.555, "b": 107.62699999999995, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 607]}], "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;", "formatting": null, "hyperlink": null, "captions": [{"cref": "#/texts/200"}], "references": [], "footnotes": [], "image": null, "code_language": "unknown"}, {"self_ref": "#/texts/200", "parent": {"cref": "#/texts/199"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 13, "bbox": {"l": 136.8, "t": 385.482, "r": 351.999, "b": 377.157, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 50]}], "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text": "Example 3-9 Creating a mask on the TAX_ID column", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/201", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 344.94, "t": 36.46199999999999, "r": 523.584, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Chapter 3. Row and Column Access Control", "text": "Chapter 3. Row and Column Access Control", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/202", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 13, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "27", "text": "27", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/203", "parent": {"cref": "#/groups/10"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 720.491, "r": 449.917, "b": 711.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 65]}], "orig": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/204", "parent": {"cref": "#/pictures/11"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.8, "t": 618.462, "r": 293.146, "b": 610.137, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Figure 3-10 Column masks shown in System i Navigator", "text": "Figure 3-10 Column masks shown in System i Navigator", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/205", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 64.8, "t": 589.625, "r": 203.983, "b": 577.637, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 22]}], "orig": "3.6.6 Activating RCAC", "text": "3.6.6 Activating RCAC", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/206", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 563.471, "r": 547.209, "b": 530.259, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/207", "parent": {"cref": "#/groups/11"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 522.492, "r": 409.443, "b": 513.279, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "1. Run the SQL statements that are shown in Example 3-10.", "text": "1. Run the SQL statements that are shown in Example 3-10.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/208", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 500.442, "r": 375.273, "b": 492.117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 53]}], "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table", "text": "Example 3-10 Activating RCAC on the EMPLOYEES table", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/209", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 483.043, "r": 376.677, "b": 474.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "/* Active Row Access Control (permissions) */", "text": "/* Active Row Access Control (permissions) */", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/210", "parent": {"cref": "#/groups/12"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 471.043, "r": 376.677, "b": 462.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "/* Active Column Access Control (masks) */", "text": "/* Active Column Access Control (masks) */", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/211", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 459.043, "r": 291.718, "b": 450.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/212", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 447.044, "r": 271.678, "b": 438.643, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "ACTIVATE ROW ACCESS CONTROL", "text": "ACTIVATE ROW ACCESS CONTROL", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/213", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 435.044, "r": 291.718, "b": 426.644, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 31]}], "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text": "ACTIVATE COLUMN ACCESS CONTROL;", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/214", "parent": {"cref": "#/groups/13"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 14, "bbox": {"l": 136.8, "t": 411.492, "r": 540.81, "b": 378.28, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition .", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/215", "parent": {"cref": "#/pictures/12"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 14, "bbox": {"l": 64.8, "t": 142.962, "r": 347.441, "b": 134.63699999999994, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 67]}], "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/216", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 78.402, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "28", "text": "28", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/217", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 14, "bbox": {"l": 93.42, "t": 36.46199999999999, "r": 334.258, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/218", "parent": {"cref": "#/groups/14"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8, "t": 720.491, "r": 514.058, "b": 687.278, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 228]}], "orig": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/219", "parent": {"cref": "#/pictures/13"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 136.8, "t": 311.442, "r": 327.111, "b": 303.117, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text": "Figure 4-68 Visual Explain with RCAC enabled", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/220", "parent": {"cref": "#/groups/14"}, "children": [], "content_layer": "body", "label": "list_item", "prov": [{"page_no": 15, "bbox": {"l": 136.8, "t": 285.431, "r": 547.308, "b": 252.21900000000005, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 232]}], "orig": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause.", "formatting": null, "hyperlink": null, "enumerated": false, "marker": "-"}, {"self_ref": "#/texts/221", "parent": {"cref": "#/pictures/14"}, "children": [], "content_layer": "body", "label": "caption", "prov": [{"page_no": 15, "bbox": {"l": 64.8, "t": 124.48199999999997, "r": 227.119, "b": 116.15700000000004, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 39]}], "orig": "Figure 4-69 Index advice with no RCAC", "text": "Figure 4-69 Index advice with no RCAC", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/222", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 214.8, "t": 36.46199999999999, "r": 523.576, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 70]}], "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example", "text": "Chapter 4. Implementing Row and Column Access Control: Banking example", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/223", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 15, "bbox": {"l": 536.1, "t": 37.150999999999954, "r": 547.217, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2]}], "orig": "77", "text": "77", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/224", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "code", "prov": [{"page_no": 16, "bbox": {"l": 64.8, "t": 720.057, "r": 500.697, "b": 85.46100000000001, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 2007]}], "orig": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;", "formatting": null, "hyperlink": null, "captions": [], "references": [], "footnotes": [], "image": null, "code_language": "unknown"}, {"self_ref": "#/texts/225", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 64.8, "t": 37.150999999999954, "r": 83.982, "b": 27.937999999999988, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 3]}], "orig": "124", "text": "124", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/226", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_footer", "prov": [{"page_no": 16, "bbox": {"l": 98.94, "t": 36.46199999999999, "r": 339.654, "b": 28.136999999999944, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/227", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 287.22, "t": 763.452, "r": 414.264, "b": 741.252, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 10]}], "orig": "Back cover", "text": "Back cover", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/228", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "section_header", "prov": [{"page_no": 18, "bbox": {"l": 27.0, "t": 718.362, "r": 447.36, "b": 651.54, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 54]}], "orig": "Row and Column Access Control Support in IBM DB2 for i", "text": "Row and Column Access Control Support in IBM DB2 for i", "formatting": null, "hyperlink": null, "level": 1}, {"self_ref": "#/texts/229", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.7, "t": 549.828, "r": 127.444, "b": 525.168, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Implement roles and separation of duties", "text": "Implement roles and separation of duties", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/230", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 26.7, "t": 507.828, "r": 120.283, "b": 469.128, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 40]}], "orig": "Leverage row permissions on the database", "text": "Leverage row permissions on the database", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/231", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 18, "bbox": {"l": 152.94, "t": 549.271, "r": 414.461, "b": 468.408, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 464]}], "orig": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment.", "text": "This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. 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Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment.", "text": "IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 145.12800000000004, "r": 547.184, "b": 154.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 167.62800000000004, "r": 547.199, "b": 176.841, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 190.12800000000004, "r": 547.188, "b": 199.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 347.145, "r": 547.204, "b": 356.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 359.145, "r": 547.214, "b": 368.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 371.625, "r": 547.164, "b": 380.838, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 384.105, "r": 547.156, "b": 393.318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 396.644, "r": 547.193, "b": 405.857, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 409.124, "r": 547.186, "b": 418.337, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 434.144, "r": 547.176, "b": 443.357, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 446.624, "r": 547.158, "b": 455.837, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 459.104, "r": 547.215, "b": 468.317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 506.143, "r": 547.163, "b": 515.356, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 518.623, "r": 547.127, "b": 527.836, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 27, "end_row_offset_idx": 28, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 531.162, "r": 547.154, "b": 540.375, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 28, "end_row_offset_idx": 29, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 556.1220000000001, "r": 547.185, "b": 565.335, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 31, "end_row_offset_idx": 32, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 32, "end_row_offset_idx": 33, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.4 Establishing and controlling accessibility by using the RCAC rule text. . . . . . . . . . . . . 21", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 606.1610000000001, "r": 547.215, "b": 615.374, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 33, "end_row_offset_idx": 34, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 618.641, "r": 547.162, "b": 627.854, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 34, "end_row_offset_idx": 35, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . . 23", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 631.121, "r": 547.168, "b": 640.3340000000001, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 35, "end_row_offset_idx": 36, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.2 Creating group profiles for the users and their roles. . . . . . . . . . . . . . . . . . . . . . . 23", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 643.66, "r": 547.158, "b": 652.873, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 36, "end_row_offset_idx": 37, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 656.14, "r": 547.16, "b": 665.353, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 37, "end_row_offset_idx": 38, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 668.62, "r": 547.17, "b": 677.833, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 38, "end_row_offset_idx": 39, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.5 Defining and creating column masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 681.16, "r": 547.189, "b": 690.373, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 39, "end_row_offset_idx": 40, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 706.12, "r": 547.13, "b": 715.333, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 40, "end_row_offset_idx": 41, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "column_header": false, "row_header": true, "row_section": false}], "num_rows": 41, "num_cols": 1, "grid": [[{"bbox": {"l": 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"end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.798, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.797, "t": 347.145, "r": 547.204, "b": 356.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.797, "t": 359.145, "r": 547.214, "b": 368.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 371.625, "r": 547.164, "b": 380.838, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 384.105, "r": 547.156, "b": 393.318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 396.644, "r": 547.193, "b": 405.857, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 409.124, "r": 547.186, "b": 418.337, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 434.144, "r": 547.176, "b": 443.357, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 446.624, "r": 547.158, "b": 455.837, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.797, "t": 459.104, "r": 547.215, "b": 468.317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.797, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}], [{"bbox": {"l": 151.197, "t": 506.143, "r": 547.163, "b": 515.356, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": 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100644 --- a/tests/data/groundtruth/docling_v2/redp5110_sampled.md +++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.md @@ -1,6 +1,8 @@ +Front cover + -## Row and Column Access Control +## Row and Column Access Control Support in IBM DB2 for i @@ -8,62 +10,107 @@ ## Contents -Solution Brief +| Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii | +|--------------------------------------------------------------------------------------------------------------------------------------------------| +| Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii | +| DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix | +| Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi | +| Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi | +| Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii | +| Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii | +| Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv | +| Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 | +| 1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 | +| 1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 | +| 1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 | +| 1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 | +| 1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5 | +| Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 | +| 2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 | +| 2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8 | +| 2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8 | +| 2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9 | +| 2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9 | +| 2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9 | +| 2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 | +| 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10 | +| 2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 | +| Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 | +| 3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14 | +| 3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 | +| 3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 | +| 3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 | +| 3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 | +| 3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 | +| 3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 | +| 3.4 Establishing and controlling accessibility by using the RCAC rule text. . . . . . . . . . . . . 21 | +| 3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 | +| 3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . . . . . . 23 | +| 3.6.2 Creating group profiles for the users and their roles. . . . . . . . . . . . . . . . . . . . . . . 23 | +| 3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 | +| 3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 | +| 3.6.5 Defining and creating column masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 | +| 3.6.6 Activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 | +| 3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32 | + +DB2 for i Center of Excellence + +Solution Brief IBM Systems Lab Services and Training ## Highlights -- /g115/g3 -- /g115/g3 -- /g115/g3 -- /g115/g3 +- /g115/g3 /g40/g81/g75/g68/g81/g70/g72/g3 /g87/g75/g72/g3 /g83/g72/g85/g73/g82/g85/g80/g68/g81/g70/g72/g3 /g82/g73/g3 /g92/g82/g88/g85/g3 /g71/g68/g87/g68/g69/g68/g86/g72/g3 /g82/g83/g72/g85/g68/g87/g76/g82/g81/g86 +- /g115/g3 /g40/g68/g85/g81/g3 /g74/g85/g72/g68/g87/g72/g85/g3 /g85 /g72/g87/g88/g85/g81/g3 /g82/g81/g3 /g44/g55/g3 /g83/g85 /g82/g77/g72/g70/g87/g86/g3 /g87/g75/g85 /g82/g88/g74/g75/g3 /g80/g82/g71/g72/g85/g81/g76/g93/g68/g87/g76/g82/g81/g3 /g82/g73/g3 /g71/g68/g87/g68/g69/g68/g86/g72/g3 /g68/g81/g71/g3 /g68/g83/g83/g79/g76/g70/g68/g87/g76/g82/g81/g86 +- /g115/g3 /g53/g72/g79/g92/g3 /g82/g81/g3 /g44/g37/g48/g3 /g72/g91/g83/g72/g85/g87/g3 /g70/g82/g81/g86/g88/g79/g87/g76/g81/g74/g15/g3 /g86/g78/g76/g79/g79/g86/g3 /g86/g75/g68/g85/g76/g81/g74/g3 /g68/g81/g71/g3 /g85/g72/g81/g82/g90/g81/g3 /g86/g72/g85/g89/g76/g70/g72/g86 +- /g115/g3 /g55 /g68/g78/g72/g3 /g68/g71/g89/g68/g81/g87/g68/g74/g72/g3 /g82/g73/g3 /g68/g70/g70/g72/g86/g86/g3 /g87/g82/g3 /g68/g3 /g90/g82/g85/g79/g71/g90/g76/g71/g72/g3 /g86/g82/g88/g85/g70/g72/g3 /g82/g73/g3 /g72/g91/g83/g72/g85/g87/g76/g86/g72 Power Services -## DB2 for i +## DB2 for i Center of Excellence Expert help to achieve your business requirements ## We build confident, satisfied clients -No one else has the vast consulting experiences, skills sharing and +No one else has the vast consulting experiences, skills sharing and renown service offerings to do what we can do for you. Because no one else is IBM. -With combined experiences and direct access to development groups, +With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. ## Who we are, some of what we do Global CoE engagements cover topics including: -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> -- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> Database performance and scalability +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> Advanced SQL knowledge and skills transfer +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> Business intelligence and analytics +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> DB2 Web Query +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> Query/400 modernization for better reporting and analysis capabilities +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> Database modernization and re-engineering +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> Data-centric architecture and design +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> Extremely large database and overcoming limits to growth +- rglyph<c=1,font=/NKDKKL+JansonTextLTStd-Roman> ISV education and enablement ## Preface -This IBMfi Redpaper™ publication provides information about the IBM i 7.2 feature of IBM +This IBMfi Redpaper™ publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. -This paper is intended for database engineers, data-centric application developers, and +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. -This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with +This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US. -Jim Bainbridge +Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office. -Hernando Bedoya +Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com . ## Authors @@ -71,48 +118,50 @@ Hernando Bedoya 1 -## Securing and protecting IBM DB2 +Chapter 1. -Recent news headlines are filled with reports of data breaches and cyber-attacks impacting +## Securing and protecting IBM DB2 data -Businesses must make a serious effort to secure their data and recognize that securing +Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. -This chapter describes how you can secure and protect data in DB2 for i. The following topics +Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement. -- /SM590000 -- /SM590000 -- /SM590000 +This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter: + +- /SM590000 Security fundamentals +- /SM590000 Current state of IBM i security +- /SM590000 DB2 for i security controls ## 1.1 Security fundamentals -Before reviewing database security techniques, there are two fundamental steps in securing +Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described: -- /SM590000 -- The monitoring and assessment of adherence to the security policy determines whether +- /SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. +- The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. A security policy is what defines whether the system and its settings are secure (or not). -- /SM590000 +- /SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. -With your eyes now open to the importance of securing information assets, the rest of this +With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i. ## 1.2 Current state of IBM i security -Because of the inherently secure nature of IBM i, many clients rely on the default system +Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE. -Even more disturbing is that many IBM i clients remain in this state, despite the news +Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data. -Traditionally, IBM i applications have employed menu-based security to counteract this default +Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data. -Many businesses are trying to limit data access to a need-to-know basis. This security goal +Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage. ## 1.3.1 Existing row and column control -Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL +Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator. -Using SQL views to limit access to a subset of the data in a table also has its own set of +Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases. -Even if you are willing to live with these performance and management issues, a user with +Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view. Figure 1-2 Existing row and column controls @@ -122,24 +171,35 @@ Figure 1-2 Existing row and column controls The following CL commands can be used to work with, display, or change function usage IDs: -- /SM590000 -- /SM590000 -- /SM590000 +- /SM590000 Work Function Usage ( WRKFCNUSG ) +- /SM590000 Change Function Usage ( CHGFCNUSG ) +- /SM590000 Display Function Usage ( DSPFCNUSG ) -For example, the following +For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules: CHGFCNUSG FCNID(QIBM\_DB\_SECADM) USER(HBEDOYA) USAGE(*ALLOWED) ## 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION\_USAGE view -The FUNCTION\_USAGE view contains function usage configuration details. Table 2-1 +The FUNCTION\_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION\_USAGE view. Table 2-1 FUNCTION\_USAGE view -To discover who has authorization to define and manage RCAC, you can use the query that is +| Column name | Data type | Description | +|---------------|-------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| +| FUNCTION_ID | VARCHAR(30) | ID of the function. | +| USER_NAME | VARCHAR(10) | Name of the user profile that has a usage setting for this function. | +| USAGE | VARCHAR(7) | Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function. | +| USER_TYPE | VARCHAR(5) | Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group. | + +To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. SELECT function\_id, +user\_name, + +usage, + user\_type FROM function\_usage @@ -150,39 +210,60 @@ ORDER BY user\_name; ## 2.2 Separation of duties -Separation of duties helps businesses comply with industry regulations or organizational +Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority. -For example, assume that a business has assigned the duty to manage security on IBM i to +For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security. -In IBM i 7.2, the QIBM\_DB\_SECADM function usage grants authorities, revokes authorities, +In IBM i 7.2, the QIBM\_DB\_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table. -QIBM\_DB\_SECADM function usage can be granted only by a user with *SECADM special +QIBM\_DB\_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group. -QIBM\_DB\_SECADM also is responsible for administering RCAC, which restricts which rows +QIBM\_DB\_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table. -A preferred practice is that the RCAC administrator has the QIBM\_DB\_SECADM function +A preferred practice is that the RCAC administrator has the QIBM\_DB\_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself. -Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to +Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools. Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority +| User action | *JOBCTL | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | No Authority | +|--------------------------------------------------------------------------------|-----------|------------------|------------------|------------------|----------------| +| SET CURRENT DEGREE (SQL statement) | X | | X | | | +| CHGQRYA command targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a job that matches the current user | X | | X | X | X | +| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job | X | | X | X | | +| Visual Explain within Run SQL scripts | X | | X | X | X | +| Visual Explain outside of Run SQL scripts | X | | X | | | +| ANALYZE PLAN CACHE procedure | X | | X | | | +| DUMP PLAN CACHE procedure | X | | X | | | +| MODIFY PLAN CACHE procedure | X | | X | | | +| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) | X | | X | | | +| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) | X | | X | | | + Figure 3-1 CREATE PERMISSION SQL statement ## Column mask -A column mask is a database object that manifests a column value access control rule for a +A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number. Table 3-1 Special registers and their corresponding values -Figure 3-5 shows the difference in the special register values when an adopted authority is +| Special register | Corresponding value | +|----------------------|---------------------------------------------------------------------------------------------------------------------------------------| +| USER or SESSION_USER | The effective user of the thread excluding adopted authority. | +| CURRENT_USER | The effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER. | +| SYSTEM_USER | The authorization ID that initiated the connection. | -- /SM590000 -- /SM590000 -- /SM590000 -- /SM590000 -- /SM590000 +Figure 3-5 shows the difference in the special register values when an adopted authority is used: + +- /SM590000 A user connects to the server using the user profile ALICE. +- /SM590000 USER and CURRENT USER initially have the same value of ALICE. +- /SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called. +- /SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority. +- /SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE. Figure 3-5 Special registers and adopted authority @@ -190,32 +271,40 @@ Figure 3-5 Special registers and adopted authority ## 3.2.2 Built-in global variables -Built-in global variables are provided with the database manager and are used in SQL +Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables. -IBM DB2 for i supports nine different built-in global variables that are read only and +IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic. Table 3-2 lists the nine built-in global variables. Table 3-2 Built-in global variables +| Global variable | Type | Description | +|-----------------------|--------------|----------------------------------------------------------------| +| CLIENT_HOST | VARCHAR(255) | Host name of the current client as returned by the system | +| CLIENT_IPADDR | VARCHAR(128) | IP address of the current client as returned by the system | +| CLIENT_PORT | INTEGER | Port used by the current client to communicate with the server | +| PACKAGE_NAME | VARCHAR(128) | Name of the currently running package | +| PACKAGE_SCHEMA | VARCHAR(128) | Schema name of the currently running package | +| PACKAGE_VERSION | VARCHAR(64) | Version identifier of the currently running package | +| ROUTINE_SCHEMA | VARCHAR(128) | Schema name of the currently running routine | +| ROUTINE_SPECIFIC_NAME | VARCHAR(128) | Name of the currently running routine | +| ROUTINE_TYPE | CHAR(1) | Type of the currently running routine | + ## 3.3 VERIFY\_GROUP\_FOR\_USER function -The VERIFY\_GROUP\_FOR\_USER function was added in IBM i 7.2. Although it is primarily +The VERIFY\_GROUP\_FOR\_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION\_USER, USER, or CURRENT\_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error. -If a special register value is in the list of user profiles or it is a member of a group profile +If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value. Here is an example of using the VERIFY\_GROUP\_FOR\_USER function: -- 1. -- 2. -- 3. +- 1. There are user profiles for MGR, JANE, JUDY, and TONY. +- 2. The user profile JANE specifies a group profile of MGR. +- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1: ``` -VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') -``` - -``` -VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') +VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY') ``` RETURN @@ -223,21 +312,21 @@ RETURN CASE ``` -WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 +WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ; ``` -- 2. -- - -- - -- - -- - +- 2. The other column to mask in this example is the TAX\_ID information. In this example, the rules to enforce include the following ones: +- -Human Resources can see the unmasked TAX\_ID of the employees. +- -Employees can see only their own unmasked TAX\_ID. +- -Managers see a masked version of TAX\_ID with the first five characters replaced with the X character (for example, XXX-XX-1234). +- -Any other person sees the entire TAX\_ID as masked, for example, XXX-XX-XXXX. - To implement this column mask, run the SQL statement that is shown in Example 3-9. ``` -CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES +CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; ``` -- 3. +- 3. Figure 3-10 shows the masks that are created in the HR\_SCHEMA. Figure 3-10 Column masks shown in System i Navigator @@ -245,25 +334,29 @@ Figure 3-10 Column masks shown in System i Navigator ## 3.6.6 Activating RCAC -Now that you have created the row permission and the two column masks, RCAC must be +Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps: -- 1. +- 1. Run the SQL statements that are shown in Example 3-10. ## Example 3-10 Activating RCAC on the EMPLOYEES table - /* Active Row Access Control (permissions) */ - /* Active Column Access Control (masks) */ +ALTER TABLE HR\_SCHEMA.EMPLOYEES + +ACTIVATE ROW ACCESS CONTROL + ACTIVATE COLUMN ACCESS CONTROL; -- 2. +- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR\_SCHEMA  Tables , right-click the EMPLOYEES table, and click Definition . Figure 3-11 Selecting the EMPLOYEES table from System i Navigator -- 2. -- 3. +- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. +- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause. Figure 4-68 Visual Explain with RCAC enabled @@ -274,33 +367,31 @@ Figure 4-69 Index advice with no RCAC ``` -THEN C . CUSTOMER_TAX_ID +THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ; ``` -``` -CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C -``` +Back cover -## Row and Column Access Control +## Row and Column Access Control Support in IBM DB2 for i -Implement roles and +Implement roles and separation of duties -This IBM Redpaper publication provides information about the IBM i 7.2 +Leverage row permissions on the database -Leverage row +This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. -Protect columns by +Protect columns by defining column masks -This paper is intended for database engineers, data-centric application +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. -INTERNATIONAL +INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION -BUILDING TECHNICAL +BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE -IBM Redbooks are developed +IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment. -For more information: \ No newline at end of file +For more information: ibm.com /redbooks \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json b/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json index 3925de2b..ef7b4b5a 100644 --- a/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json +++ b/tests/data/groundtruth/docling_v2/redp5110_sampled.pages.json @@ -1 +1 @@ -[{"page_no": 0, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 36.9, "r_y0": 765.105, "r_x1": 98.598, "r_y1": 765.105, "r_x2": 98.598, "r_y2": 751.23, "r_x3": 36.9, "r_y3": 751.23, "coord_origin": "TOPLEFT"}, "text": "ibm.com", "orig": "ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 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. . . . . . . . . . . . . 8", "orig": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 405.857, "r_x1": 547.193, "r_y1": 405.857, "r_x2": 547.193, "r_y2": 396.644, "r_x3": 151.197, "r_y3": 396.644, "coord_origin": "TOPLEFT"}, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 418.337, "r_x1": 547.186, "r_y1": 418.337, "r_x2": 547.186, "r_y2": 409.124, "r_x3": 151.197, "r_y3": 409.124, "coord_origin": "TOPLEFT"}, "text": "2.1.4 Database 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Row and Column Access Control", "orig": "Chapter 3. 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. . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 515.356, "r_x1": 547.163, "r_y1": 515.356, "r_x2": 547.163, "r_y2": 506.143, "r_x3": 151.197, "r_y3": 506.143, "coord_origin": "TOPLEFT"}, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "orig": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 527.836, "r_x1": 547.127, "r_y1": 527.836, "r_x2": 547.127, "r_y2": 518.623, "r_x3": 151.197, "r_y3": 518.623, "coord_origin": "TOPLEFT"}, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, "coord_origin": "TOPLEFT"}, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 565.335, "r_x1": 547.185, "r_y1": 565.335, "r_x2": 547.185, "r_y2": 556.1220000000001, "r_x3": 151.197, "r_y3": 556.1220000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "orig": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. 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A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. 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He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. Hernando can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 680.837, "r_x1": 320.585, "r_y1": 680.837, "r_x2": 320.585, "r_y2": 671.624, "r_x3": 263.4, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": "contacted at ", "orig": "contacted at ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.64, "r_y0": 680.473, "r_x1": 410.578, "r_y1": 680.473, "r_x2": 410.578, "r_y2": 672.072, "r_x3": 320.64, "r_y3": 672.072, "coord_origin": "TOPLEFT"}, "text": "hbedoya@us.ibm.com", "orig": "hbedoya@us.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.579, "r_y0": 680.837, "r_x1": 413.348, "r_y1": 680.837, "r_x2": 413.348, "r_y2": 671.624, "r_x3": 410.579, "r_y3": 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, "coord_origin": "TOPLEFT"}, "text": "global businesses of all sizes. The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 538.939, "r_x1": 250.2, "r_y1": 538.939, "r_x2": 250.2, "r_y2": 529.726, "r_x3": 151.2, 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.075252532958984, "t": 70.57737731933594, "r": 239.620361328125, "b": 237.9579315185547, "coord_origin": "TOPLEFT"}, "confidence": 0.7509335279464722, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 12, "page_no": 4, "cluster": {"id": 12, "label": "text", "bbox": {"l": 500.4, "t": 93.16899999999998, "r": 522.618, "b": 130.13199999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.7085074782371521, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.4, "r_y0": 130.13199999999995, "r_x1": 522.618, "r_y1": 130.13199999999995, "r_x2": 522.618, "r_y2": 93.16899999999998, "r_x3": 500.4, "r_y3": 93.16899999999998, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "section_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88599999999997, "r": 549.157, "b": 278.918, "coord_origin": "TOPLEFT"}, "confidence": 0.9371159076690674, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Securing and protecting IBM DB2"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 348.709, "r": 542.266, "b": 357.922, "coord_origin": "TOPLEFT"}, "confidence": 0.9868631362915039, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting"}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 442.728, "r": 525.074, "b": 451.941, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing"}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 500.687, "r": 547.404, "b": 509.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics"}, {"label": "list_item", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 530.175, "r": 141.78, "b": 538.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9373421669006348, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 7, "page_no": 4, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 542.174, "r": 141.78, "b": 550.575, "coord_origin": "TOPLEFT"}, "confidence": 0.9334256649017334, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 550.575, "r_x1": 141.78, "r_y1": 550.575, "r_x2": 141.78, "r_y2": 542.174, "r_x3": 136.8, "r_y3": 542.174, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 3, "page_no": 4, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 554.174, "r": 141.78, "b": 562.5740000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.9462205767631531, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 562.5740000000001, "r_x1": 141.78, "r_y1": 562.5740000000001, "r_x2": 141.78, "r_y2": 554.174, "r_x3": 136.8, "r_y3": 554.174, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "footnote", "id": 13, "page_no": 4, "cluster": {"id": 13, "label": "footnote", "bbox": {"l": 136.8, "t": 715.84, "r": 146.34, "b": 721.204, "coord_origin": "TOPLEFT"}, "confidence": 0.5138102173805237, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 721.204, "r_x1": 146.34, "r_y1": 721.204, "r_x2": 146.34, "r_y2": 715.84, "r_x3": 136.8, "r_y3": 715.84, "coord_origin": "TOPLEFT"}, "text": "1 ", "orig": "1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "footnote", "id": 9, "page_no": 4, "cluster": {"id": 9, "label": "footnote", "bbox": {"l": 136.8, "t": 725.574, "r": 143.908, "b": 731.457, "coord_origin": "TOPLEFT"}, "confidence": 0.7965563535690308, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 731.457, "r_x1": 143.908, "r_y1": 731.457, "r_x2": 143.908, "r_y2": 725.574, "r_x3": 136.8, "r_y3": 725.574, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.075252532958984, "t": 70.57737731933594, "r": 239.620361328125, "b": 237.9579315185547, "coord_origin": "TOPLEFT"}, "confidence": 0.7509335279464722, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.68, "t": 754.849, "r": 547.218, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "page_footer", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9364771246910095, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 127.822, "r_x1": 324.463, "r_y1": 127.822, "r_x2": 324.463, "r_y2": 118.60900000000004, "r_x3": 136.8, "r_y3": 118.60900000000004, "coord_origin": "TOPLEFT"}, "text": "information assets that must be described:", "orig": "information assets that must be described:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 74.34100000000001, "r": 267.39, "b": 89.10400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.60900000000004, "r": 545.014, "b": 115.822, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 136.096, "r": 141.78, "b": 144.49699999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 151.199, "t": 188.62800000000004, "r": 541.654, "b": 197.841, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 151.199, "t": 277.60699999999997, "r": 542.001, "b": 286.82, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). 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323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.629, "r": 530.314, "b": 479.842, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system"}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.648, "r": 513.913, "b": 537.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news"}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.667, "r": 547.652, "b": 607.88, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default"}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 541.139, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. 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command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 123.74099999999999, "r_x1": 253.222, "r_y1": 123.74099999999999, "r_x2": 253.222, "r_y2": 114.52800000000002, "r_x3": 151.2, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Work Function Usage (", "orig": "Work Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.26, "r_y0": 123.42399999999998, "r_x1": 298.2, "r_y1": 123.42399999999998, "r_x2": 298.2, "r_y2": 114.976, "r_x3": 253.26, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "WRKFCNUSG", "orig": "WRKFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 298.201, "r_y0": 123.74099999999999, "r_x1": 301.517, "r_y1": 123.74099999999999, "r_x2": 301.517, "r_y2": 114.52800000000002, "r_x3": 298.201, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 135.74099999999999, "r_x1": 265.099, "r_y1": 135.74099999999999, "r_x2": 265.099, "r_y2": 126.52800000000002, "r_x3": 151.201, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Change Function Usage (", "orig": "Change Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.081, "r_y0": 135.42399999999998, "r_x1": 310.02, "r_y1": 135.42399999999998, "r_x2": 310.02, "r_y2": 126.976, "r_x3": 265.081, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.081, "r_y0": 135.74099999999999, "r_x1": 313.398, "r_y1": 135.74099999999999, "r_x2": 313.398, "r_y2": 126.52800000000002, "r_x3": 310.081, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 147.74099999999999, "r_x1": 262.535, "r_y1": 147.74099999999999, "r_x2": 262.535, "r_y2": 138.52800000000002, "r_x3": 151.201, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Display Function Usage (", "orig": "Display Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.561, "r_y0": 147.42399999999998, "r_x1": 307.5, "r_y1": 147.42399999999998, "r_x2": 307.5, "r_y2": 138.976, "r_x3": 262.561, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "DSPFCNUSG", "orig": "DSPFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 307.5, "r_y0": 147.74099999999999, "r_x1": 310.817, "r_y1": 147.74099999999999, "r_x2": 310.817, "r_y2": 138.52800000000002, "r_x3": 307.5, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 673.762, "r_x1": 547.592, "r_y1": 673.762, "r_x2": 547.592, "r_y2": 664.549, "r_x3": 136.8, "r_y3": 664.549, "coord_origin": "TOPLEFT"}, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 697.761, "r_x1": 544.288, "r_y1": 697.761, "r_x2": 544.288, "r_y2": 688.548, "r_x3": 136.8, "r_y3": 688.548, "coord_origin": "TOPLEFT"}, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "orig": "administrative functions to be divided across individuals without overlapping responsibilities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 709.761, "r_x1": 536.25, "r_y1": 709.761, "r_x2": 536.25, "r_y2": 700.548, "r_x3": 136.8, "r_y3": 700.548, "coord_origin": "TOPLEFT"}, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "orig": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 304.863, "r_x1": 202.253, "r_y1": 304.863, "r_x2": 202.253, "r_y2": 296.538, "r_x3": 142.8, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Column name", "orig": "Column name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.809, "r_y0": 304.863, "r_x1": 257.219, "r_y1": 304.863, "r_x2": 257.219, "r_y2": 296.538, "r_x3": 216.809, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Data type", "orig": "Data type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.475, "r_y0": 304.863, "r_x1": 338.903, "r_y1": 304.863, "r_x2": 338.903, "r_y2": 296.538, "r_x3": 289.475, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Description", "orig": "Description", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 323.883, "r_x1": 203.243, "r_y1": 323.883, "r_x2": 203.243, "r_y2": 315.558, "r_x3": 142.8, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "FUNCTION_ID", "orig": "FUNCTION_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.785, "r_y0": 323.883, "r_x1": 276.014, "r_y1": 323.883, "r_x2": 276.014, "r_y2": 315.558, "r_x3": 216.785, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(30)", "orig": "VARCHAR(30)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.458, "r_y0": 323.883, "r_x1": 359.865, "r_y1": 323.883, "r_x2": 359.865, "r_y2": 315.558, "r_x3": 289.458, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "ID of the function.", "orig": "ID of the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 342.843, "r_x1": 198.685, "r_y1": 342.843, "r_x2": 198.685, "r_y2": 334.518, "r_x3": 142.8, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "USER_NAME", "orig": "USER_NAME", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.741, "r_y0": 342.843, "r_x1": 275.939, "r_y1": 342.843, "r_x2": 275.939, "r_y2": 334.518, "r_x3": 216.741, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(10)", "orig": "VARCHAR(10)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.382, "r_y0": 342.843, "r_x1": 515.035, "r_y1": 342.843, "r_x2": 515.035, "r_y2": 334.518, "r_x3": 289.382, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "Name of the user profile that has a usage setting for this ", "orig": "Name of the user profile that has a usage setting for this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 353.883, "r_x1": 323.439, "r_y1": 353.883, "r_x2": 323.439, "r_y2": 345.558, "r_x3": 289.44, "r_y3": 345.558, "coord_origin": "TOPLEFT"}, "text": "function.", "orig": "function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 372.844, "r_x1": 173.997, "r_y1": 372.844, "r_x2": 173.997, "r_y2": 364.519, "r_x3": 142.8, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "USAGE", "orig": "USAGE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.774, "r_y0": 372.844, "r_x1": 270.993, "r_y1": 372.844, "r_x2": 270.993, "r_y2": 364.519, "r_x3": 216.774, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(7)", "orig": "VARCHAR(7)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.416, "r_y0": 372.844, "r_x1": 346.901, "r_y1": 372.844, "r_x2": 346.901, "r_y2": 364.519, "r_x3": 289.416, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "Usage setting:", "orig": "Usage setting:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 383.555, "r_x1": 293.94, "r_y1": 383.555, "r_x2": 293.94, "r_y2": 375.964, "r_x3": 289.44, "r_y3": 375.964, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 383.884, "r_x1": 535.18, "r_y1": 383.884, "r_x2": 535.18, "r_y2": 375.559, "r_x3": 303.84, "r_y3": 375.559, "coord_origin": "TOPLEFT"}, "text": "ALLOWED: The user profile is allowed to use the function.", "orig": "ALLOWED: The user profile is allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 394.535, "r_x1": 293.94, "r_y1": 394.535, "r_x2": 293.94, "r_y2": 386.944, "r_x3": 289.44, "r_y3": 386.944, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 394.864, "r_x1": 539.121, "r_y1": 394.864, "r_x2": 539.121, "r_y2": 386.539, "r_x3": 303.84, "r_y3": 386.539, "coord_origin": "TOPLEFT"}, "text": "DENIED: The user profile is not allowed to use the function.", "orig": "DENIED: The user profile is not allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 413.884, "r_x1": 196.235, "r_y1": 413.884, "r_x2": 196.235, "r_y2": 405.559, "r_x3": 142.8, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "USER_TYPE", "orig": "USER_TYPE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.752, "r_y0": 413.884, "r_x1": 271.009, "r_y1": 413.884, "r_x2": 271.009, "r_y2": 405.559, "r_x3": 216.752, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(5)", "orig": "VARCHAR(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.432, "r_y0": 413.884, "r_x1": 367.811, "r_y1": 413.884, "r_x2": 367.811, "r_y2": 405.559, "r_x3": 289.432, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "Type of user profile:", "orig": "Type of user profile:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 424.534, "r_x1": 293.94, "r_y1": 424.534, "r_x2": 293.94, "r_y2": 416.944, "r_x3": 289.44, "r_y3": 416.944, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 424.864, "r_x1": 434.795, "r_y1": 424.864, "r_x2": 434.795, "r_y2": 416.539, "r_x3": 303.84, "r_y3": 416.539, "coord_origin": "TOPLEFT"}, "text": "USER: The user profile is a user.", "orig": "USER: The user profile is a user.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 435.514, "r_x1": 293.94, "r_y1": 435.514, "r_x2": 293.94, "r_y2": 427.924, "r_x3": 289.44, "r_y3": 427.924, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 435.844, "r_x1": 448.132, "r_y1": 435.844, "r_x2": 448.132, "r_y2": 427.519, "r_x3": 303.84, "r_y3": 427.519, "coord_origin": "TOPLEFT"}, "text": "GROUP: The user profile is a group.", "orig": "GROUP: The user profile is a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.976, "r": 141.78, "b": 123.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.976, "r": 141.781, "b": 135.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.976, "r": 141.781, "b": 147.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 255.049, "b": 169.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 254.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 461.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 529.043, "b": 661.762, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [], "children": []}]}, "tablestructure": {"table_map": {"1": {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.976, "r": 141.78, "b": 123.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.976, "r": 141.781, "b": 135.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.976, "r": 141.781, "b": 147.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 255.049, "b": 169.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, the following"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 254.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1"}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 461.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is"}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 529.043, "b": 661.762, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational"}, {"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "body": [{"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.976, "r": 141.78, "b": 123.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.976, "r": 141.781, "b": 135.37699999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.976, "r": 141.781, "b": 147.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 255.049, "b": 169.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, the following"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 254.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1"}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 461.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is"}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 529.043, "b": 661.762, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational"}, {"label": "table", "id": 1, "page_no": 7, "cluster": {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "headers": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.32, "r_y0": 763.863, "r_x1": 523.523, "r_y1": 763.863, "r_x2": 523.523, "r_y2": 755.538, "r_x3": 355.32, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 542.703, "r_y1": 80.72199999999998, "r_x2": 542.703, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 513.687, "r_y1": 92.72199999999998, "r_x2": 513.687, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. ", "orig": "table. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.1, "r_y0": 220.70000000000005, "r_x1": 232.869, "r_y1": 220.70000000000005, "r_x2": 232.869, "r_y2": 211.48699999999997, "r_x3": 230.1, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70000000000005, "r_x1": 346.314, "r_y1": 232.70000000000005, "r_x2": 346.314, "r_y2": 223.48699999999997, "r_x3": 136.8, "r_y3": 223.48699999999997, "coord_origin": "TOPLEFT"}, "text": "authority and can be given to a user or a group.", "orig": "authority and can be given to a user or a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.71900000000005, "r_x1": 529.471, "r_y1": 266.71900000000005, "r_x2": 529.471, "r_y2": 257.506, "r_x3": 136.8, "r_y3": 257.506, "coord_origin": "TOPLEFT"}, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "orig": "a user is allowed to access in a table and whether a user is allowed to see information in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.71900000000005, "r_x1": 253.462, "r_y1": 278.71900000000005, "r_x2": 253.462, "r_y2": 269.506, "r_x3": 136.8, "r_y3": 269.506, "coord_origin": "TOPLEFT"}, "text": "certain columns of a table.", "orig": "certain columns of a table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 312.738, "r_x1": 539.816, "r_y1": 312.738, "r_x2": 539.816, "r_y2": 303.525, "r_x3": 136.8, "r_y3": 303.525, "coord_origin": "TOPLEFT"}, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 370.697, "r_x1": 343.798, "r_y1": 370.697, "r_x2": 343.798, "r_y2": 361.484, "r_x3": 136.8, "r_y3": 361.484, "coord_origin": "TOPLEFT"}, "text": "the different CL commands and DB2 for i tools.", "orig": "the different CL commands and DB2 for i tools.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 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usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.8, "t": 245.50599999999997, "r": 545.805, "b": 254.71900000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 291.525, "r": 533.79, "b": 300.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function"}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 349.485, "r": 543.076, "b": 358.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to"}, {"label": "caption", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 64.8, "t": 383.538, "r": 391.767, "b": 391.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9337189793586731, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 391.863, "r_x1": 391.767, "r_y1": 391.863, "r_x2": 391.767, "r_y2": 383.538, "r_x3": 64.8, "r_y3": 383.538, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"label": "page_footer", "id": 9, "page_no": 8, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8987301588058472, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "11"}, {"label": "page_footer", "id": 7, "page_no": 8, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 355.32, "t": 755.538, "r": 523.523, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9508126378059387, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.32, "r_y0": 763.863, "r_x1": 523.523, "r_y1": 763.863, "r_x2": 523.523, "r_y2": 755.538, "r_x3": 355.32, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 64.41139221191406, "t": 393.6136169433594, "r": 547.3950805664062, "b": 721.60791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9899571537971497, "cells": [], "children": []}, "text": null, "otsl_seq": ["fcel", "ched", "ched", "ched", "ched", "ched", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "fcel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "fcel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "body": [{"label": "text", "id": 2, "page_no": 8, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 542.703, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9789126515388489, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 542.703, "r_y1": 80.72199999999998, "r_x2": 542.703, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to"}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 153.52800000000002, "r": 544.17, "b": 162.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities,"}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 211.48699999999997, "r": 538.66, "b": 220.70000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special"}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.8, "t": 245.50599999999997, "r": 545.805, "b": 254.71900000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows"}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 291.525, "r": 533.79, "b": 300.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function"}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 349.485, "r": 543.076, "b": 358.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to"}, {"label": "caption", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 64.8, "t": 383.538, "r": 391.767, "b": 391.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9337189793586731, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 391.863, "r_x1": 391.767, "r_y1": 391.863, "r_x2": 391.767, "r_y2": 383.538, "r_x3": 64.8, "r_y3": 383.538, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 64.41139221191406, "t": 393.6136169433594, "r": 547.3950805664062, "b": 721.60791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9899571537971497, "cells": [], "children": []}, "text": null, "otsl_seq": ["fcel", "ched", "ched", "ched", "ched", "ched", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "fcel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "fcel", "fcel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", "fcel", "ecel", "ecel", "nl", "rhed", "fcel", "ecel", 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Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 2. Roles and separation of duties"}]}}, {"page_no": 9, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "15", "orig": "15", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 179.591, "r_y1": 80.72199999999998, "r_x2": 179.591, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The SQL ", "orig": "The SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.58, "r_y0": 80.40499999999997, "r_x1": 264.479, "r_y1": 80.40499999999997, "r_x2": 264.479, "r_y2": 71.957, "r_x3": 179.58, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION", "orig": "CREATE PERMISSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.54, "r_y0": 80.72199999999998, "r_x1": 528.74, "r_y1": 80.72199999999998, "r_x2": 528.74, "r_y2": 71.50900000000001, "r_x3": 264.54, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": " statement that is shown in Figure 3-1 is used to define and ", "orig": " statement that is shown in Figure 3-1 is used to define and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 341.703, "r_y1": 92.72199999999998, "r_x2": 341.703, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "initially enable or disable the row access rules.", "orig": "initially enable or disable the row access rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 422.463, "r_x1": 341.985, "r_y1": 422.463, "r_x2": 341.985, "r_y2": 414.138, "r_x3": 136.8, "r_y3": 414.138, "coord_origin": "TOPLEFT"}, "text": "Figure 3-1 CREATE PERMISSION SQL statement", "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.044, "r_x1": 215.383, "r_y1": 451.044, "r_x2": 215.383, "r_y2": 439.944, "r_x3": 136.8, "r_y3": 439.944, "coord_origin": "TOPLEFT"}, "text": "Column mask", "orig": "Column mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 464.302, "r_x1": 542.776, "r_y1": 464.302, "r_x2": 542.776, "r_y2": 455.089, "r_x3": 136.8, "r_y3": 455.089, "coord_origin": "TOPLEFT"}, "text": "A column mask is a database object that manifests a column value access control rule for a ", "orig": "A column mask is a database object that manifests a column value access control rule for a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 476.302, "r_x1": 537.85, "r_y1": 476.302, "r_x2": 537.85, "r_y2": 467.089, "r_x3": 136.8, "r_y3": 467.089, "coord_origin": "TOPLEFT"}, "text": "specific column in a specific table. It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. For example, a teller can see only the last four digits of a tax ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 500.301, "r_x1": 231.178, "r_y1": 500.301, "r_x2": 231.178, "r_y2": 491.088, "r_x3": 136.8, "r_y3": 491.088, "coord_origin": "TOPLEFT"}, "text": "identification number.", "orig": "identification number.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 149.37099999999998, "r_x1": 251.916, "r_y1": 149.37099999999998, "r_x2": 251.916, "r_y2": 140.20799999999997, "r_x3": 148.134, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION ", "orig": "CREATE PERMISSION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 346.565, "r_y0": 129.22400000000005, "r_x1": 530.748, "r_y1": 129.22400000000005, "r_x2": 530.748, "r_y2": 121.88599999999997, "r_x3": 346.565, "r_y3": 121.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Names the row permission for row access control", "orig": "Names the row permission for row access control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 190.75400000000002, "r_x1": 163.458, "r_y1": 190.75400000000002, "r_x2": 163.458, "r_y2": 181.591, "r_x3": 148.134, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "ON", "orig": "ON", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.584, "r_y0": 190.74300000000005, "r_x1": 174.303, "r_y1": 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"rect": {"r_x0": 311.32, "r_y0": 174.05600000000004, "r_x1": 529.937, "r_y1": 174.05600000000004, "r_x2": 529.937, "r_y2": 166.71799999999996, "r_x3": 311.32, "r_y3": 166.71799999999996, "coord_origin": "TOPLEFT"}, "text": "Identifies the table on which the row permission is created", "orig": "Identifies the table on which the row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 232.13699999999994, "r_x1": 163.113, "r_y1": 232.13699999999994, "r_x2": 163.113, "r_y2": 222.97400000000005, "r_x3": 148.134, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "AS ", "orig": "AS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.687, "r_y0": 232.12599999999998, "r_x1": 171.406, "r_y1": 232.12599999999998, "r_x2": 171.406, "r_y2": 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"r_y2": 246.51800000000003, "r_x3": 321.563, "r_y3": 246.51800000000003, "coord_origin": "TOPLEFT"}, "text": "Indicates that a row permission is created", "orig": "Indicates that a row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.597, "r_y0": 274.06500000000005, "r_x1": 530.955, "r_y1": 274.06500000000005, "r_x2": 530.955, "r_y2": 266.726, "r_x3": 321.597, "r_y3": 266.726, "coord_origin": "TOPLEFT"}, "text": "Specifies a condition that can be true, false, or unknown", "orig": "Specifies a condition that can be true, false, or unknown", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 301.109, "r_x1": 183.43, "r_y1": 301.109, "r_x2": 183.43, "r_y2": 291.945, "r_x3": 148.134, "r_y3": 291.945, "coord_origin": "TOPLEFT"}, "text": "WHERE", 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Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.245, "r_y0": 197.88300000000004, "r_x1": 425.656, "r_y1": 197.88300000000004, "r_x2": 425.656, "r_y2": 189.558, "r_x3": 230.245, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "The authorization ID that initiated the connection.", "orig": "The authorization ID that initiated the connection.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.732, "r_y0": 395.376, "r_x1": 216.376, "r_y1": 395.376, "r_x2": 216.376, "r_y2": 387.442, "r_x3": 140.732, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": "Signed on as ALICE", "orig": "Signed on as ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.708, "r_y0": 395.376, "r_x1": 169.958, "r_y1": 395.376, "r_x2": 169.958, "r_y2": 387.442, "r_x3": 167.708, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", 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"text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49800000000005, "r": 372.618, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": 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variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and"}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "headers": [{"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 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Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 118.86300000000006, "r_x1": 134.993, "r_y1": 118.86300000000006, "r_x2": 134.993, "r_y2": 110.53800000000001, "r_x3": 70.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Global variable", "orig": "Global variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.889, "r_y0": 118.86300000000006, "r_x1": 223.349, "r_y1": 118.86300000000006, "r_x2": 223.349, "r_y2": 110.53800000000001, "r_x3": 202.889, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Type", "orig": "Type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.825, "r_y0": 118.86300000000006, "r_x1": 331.345, "r_y1": 118.86300000000006, "r_x2": 331.345, "r_y2": 110.53800000000001, "r_x3": 281.825, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Description", "orig": "Description", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 137.82299999999998, "r_x1": 132.729, "r_y1": 137.82299999999998, "r_x2": 132.729, "r_y2": 129.49800000000005, "r_x3": 70.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "CLIENT_HOST", "orig": "CLIENT_HOST", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.89, "r_y0": 137.82299999999998, "r_x1": 267.085, "r_y1": 137.82299999999998, "r_x2": 267.085, "r_y2": 129.49800000000005, "r_x3": 202.89, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(255)", "orig": "VARCHAR(255)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.847, "r_y0": 137.82299999999998, "r_x1": 510.176, "r_y1": 137.82299999999998, "r_x2": 510.176, "r_y2": 129.49800000000005, "r_x3": 281.847, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Host name of the current client as returned by the system", "orig": "Host name of the current client as returned by the system", "text_direction": 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156.84299999999996, "r_x2": 509.611, "r_y2": 148.51800000000003, "r_x3": 281.845, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "IP address of the current client as returned by the system", "orig": "IP address of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 175.86300000000006, "r_x1": 134.965, "r_y1": 175.86300000000006, "r_x2": 134.965, "r_y2": 167.538, "r_x3": 70.8, "r_y3": 167.538, "coord_origin": "TOPLEFT"}, "text": "CLIENT_PORT ", "orig": "CLIENT_PORT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.903, "r_y0": 175.86300000000006, "r_x1": 242.816, "r_y1": 175.86300000000006, "r_x2": 242.816, "r_y2": 167.538, "r_x3": 202.903, "r_y3": 167.538, "coord_origin": "TOPLEFT"}, "text": "INTEGER", "orig": 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lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.8, "t": 93.49800000000005, "r": 201.196, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 322.201, "r": 384.345, "b": 336.964, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.529, "r": 542.844, "b": 363.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 525.156, "b": 457.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile"}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, 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"r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 145.089, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 145.099, "b": 554.719, 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11, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9187920093536377, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9505079388618469, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [], "children": []}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 0, "num_cols": 0, "table_cells": []}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 342.519, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8370978832244873, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.8, "t": 93.49800000000005, "r": 201.196, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 322.201, "r": 384.345, "b": 336.964, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.529, "r": 542.844, "b": 363.742, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily"}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 525.156, "b": 457.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile"}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, "r": 458.427, "b": 503.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 511.546, "r": 145.089, "b": 520.759, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 145.089, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 145.099, "b": 554.719, 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78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "20"}, {"label": "page_footer", "id": 6, "page_no": 11, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9505079388618469, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 12, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.35699999999997, "r_x1": 311.697, "r_y1": 116.35699999999997, "r_x2": 311.697, "r_y2": 107.957, "r_x3": 136.8, "r_y3": 107.957, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 164.356, "r_x1": 311.697, "r_y1": 164.356, "r_x2": 311.697, "r_y2": 155.95600000000002, "r_x3": 136.8, "r_y3": 155.95600000000002, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 564.375, "r_x1": 141.78, "r_y1": 564.375, "r_x2": 141.78, "r_y2": 555.975, "r_x3": 136.8, "r_y3": 555.975, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 466.615, "b": 104.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 145.098, "b": 284.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.039, "t": 304.485, "r": 157.577, "b": 313.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.039, "t": 321.525, "r": 157.577, "b": 330.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 157.577, "b": 347.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 157.577, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 381.657, "b": 432.377, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 466.615, "b": 104.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 145.098, "b": 284.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.039, 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"confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 157.577, "b": 347.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 157.577, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 381.657, "b": 432.377, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "27"}, {"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, 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"coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 157.577, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-"}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 381.657, "b": 432.377, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, 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"bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.699, "r_y0": 401.72, "r_x1": 446.468, "r_y1": 401.72, "r_x2": 446.468, "r_y2": 392.507, "r_x3": 443.699, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 522.418, "r_y0": 401.72, "r_x1": 525.187, "r_y1": 401.72, "r_x2": 525.187, "r_y2": 392.507, "r_x3": 522.418, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 145.089, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.8, "t": 173.538, "r": 293.146, "b": 181.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.8, "t": 202.375, "r": 203.983, "b": 214.36300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.529, "r": 537.1, "b": 237.74199999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 145.089, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": 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"r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 145.089, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": 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3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": 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"text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 145.089, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.957, "r": 376.677, "b": 329.357, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Column Access Control (masks) */"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.956, "r": 291.718, "b": 365.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.508, "r": 145.12, "b": 389.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.8, "t": 649.038, "r": 347.441, "b": 657.363, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. 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CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . 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VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . 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CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 2, "label": "code", "bbox": {"l": 64.8, "t": 71.94299999999998, "r": 177.119, "b": 79.53399999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"index": -1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, 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"Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 249.32000000000005, "r_x1": 547.129, "r_y1": 249.32000000000005, "r_x2": 547.129, "r_y2": 240.10699999999997, "r_x3": 136.798, "r_y3": 240.10699999999997, "coord_origin": "TOPLEFT"}, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "orig": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 271.82000000000005, "r_x1": 373.148, "r_y1": 271.82000000000005, "r_x2": 373.148, 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. Roles and separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 338.997, "r_y0": 356.358, "r_x1": 547.204, "r_y1": 356.358, "r_x2": 547.204, "r_y2": 347.145, "r_x3": 338.997, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 368.358, "r_x1": 547.214, "r_y1": 368.358, "r_x2": 547.214, "r_y2": 359.145, "r_x3": 136.797, "r_y3": 359.145, "coord_origin": "TOPLEFT"}, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "orig": "2.1 Roles . . . . . . . . . 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. . . . . . . . . . . . . 8", "orig": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 405.857, "r_x1": 547.193, "r_y1": 405.857, "r_x2": 547.193, "r_y2": 396.644, "r_x3": 151.197, "r_y3": 396.644, "coord_origin": "TOPLEFT"}, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 418.337, "r_x1": 547.186, "r_y1": 418.337, "r_x2": 547.186, "r_y2": 409.124, "r_x3": 151.197, "r_y3": 409.124, "coord_origin": "TOPLEFT"}, "text": "2.1.4 Database 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Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 502.816, "r_x1": 547.173, "r_y1": 502.816, "r_x2": 547.173, "r_y2": 493.603, "r_x3": 136.797, "r_y3": 493.603, "coord_origin": "TOPLEFT"}, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "orig": "3.1 Explanation of RCAC and the concept of access control . . . . . . . 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"TOPLEFT"}, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 565.335, "r_x1": 547.185, "r_y1": 565.335, "r_x2": 547.185, "r_y2": 556.1220000000001, "r_x3": 151.197, "r_y3": 556.1220000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "orig": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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262.60699999999997, "coord_origin": "TOPLEFT"}, "text": "Chapter 1. Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 375.118, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.797, "t": 347.145, "r": 336.783, "b": 356.358, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. 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"TOPLEFT"}, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 430.817, "r_x1": 547.167, "r_y1": 430.817, "r_x2": 547.167, "r_y2": 421.604, "r_x3": 151.197, "r_y3": 421.604, "coord_origin": "TOPLEFT"}, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 136.797, "t": 481.603, "r": 348.65, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 490.816, "r_x1": 348.65, "r_y1": 490.816, "r_x2": 348.65, "r_y2": 481.603, "r_x3": 136.797, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 350.097, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 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column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 151.197, "t": 518.623, "r": 547.127, "b": 527.836, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 527.836, "r_x1": 547.127, "r_y1": 527.836, "r_x2": 547.127, "r_y2": 518.623, "r_x3": 151.197, "r_y3": 518.623, "coord_origin": "TOPLEFT"}, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.797, "t": 531.162, "r": 547.154, "b": 540.375, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 577.875, "r_x1": 547.129, "r_y1": 577.875, "r_x2": 547.129, "r_y2": 568.662, "r_x3": 136.797, "r_y3": 568.662, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "orig": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": 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"TOPLEFT"}, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 136.797, "t": 606.1610000000001, "r": 547.215, "b": 615.374, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 615.374, "r_x1": 547.215, "r_y1": 615.374, "r_x2": 547.215, "r_y2": 606.1610000000001, "r_x3": 136.797, "r_y3": 606.1610000000001, "coord_origin": "TOPLEFT"}, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. 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Row and Column Access Control", "orig": "Chapter 3. 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. 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, "coord_origin": "TOPLEFT"}, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 565.335, "r_x1": 547.185, "r_y1": 565.335, "r_x2": 547.185, "r_y2": 556.1220000000001, "r_x3": 151.197, "r_y3": 556.1220000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "orig": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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262.60699999999997, "coord_origin": "TOPLEFT"}, "text": "Chapter 1. Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 375.118, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.797, "t": 347.145, "r": 336.783, "b": 356.358, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 136.797, "t": 481.603, "r": 348.65, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 490.816, "r_x1": 348.65, "r_y1": 490.816, "r_x2": 348.65, "r_y2": 481.603, "r_x3": 136.797, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 350.097, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 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"coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 577.875, "r_x1": 547.129, "r_y1": 577.875, "r_x2": 547.129, "r_y2": 568.662, "r_x3": 136.797, "r_y3": 568.662, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "orig": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": 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. . . . . . . 24", "orig": "3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 48, "label": "text", "bbox": {"l": 151.197, "t": 656.14, "r": 547.16, "b": 665.353, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 665.353, "r_x1": 547.16, "r_y1": 665.353, "r_x2": 547.16, "r_y2": 656.14, "r_x3": 151.197, "r_y3": 656.14, "coord_origin": "TOPLEFT"}, "text": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25", "orig": "3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 49, "label": "text", "bbox": {"l": 151.197, "t": 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. . . . . . 29", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 151.197, "t": 706.12, "r": 547.13, "b": 715.333, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 715.333, "r_x1": 547.13, "r_y1": 715.333, "r_x2": 547.13, "r_y2": 706.12, "r_x3": 151.197, "r_y3": 706.12, "coord_origin": "TOPLEFT"}, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "orig": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl"], "num_rows": 41, "num_cols": 1, "table_cells": [{"bbox": {"l": 136.8, "t": 132.649, "r": 547.19, "b": 141.86199999999997, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 145.12800000000004, "r": 547.184, "b": 154.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 167.62800000000004, "r": 547.199, "b": 176.841, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 190.12800000000004, "r": 547.188, "b": 199.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 347.145, "r": 547.204, "b": 356.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 359.145, "r": 547.214, "b": 368.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 371.625, "r": 547.164, "b": 380.838, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 384.105, "r": 547.156, "b": 393.318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 396.644, "r": 547.193, "b": 405.857, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 409.124, "r": 547.186, "b": 418.337, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 434.144, "r": 547.176, "b": 443.357, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 446.624, "r": 547.158, "b": 455.837, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 459.104, "r": 547.215, "b": 468.317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 506.143, "r": 547.163, "b": 515.356, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, 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29, "end_row_offset_idx": 30, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 556.1220000000001, "r": 547.185, "b": 565.335, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 30, "end_row_offset_idx": 31, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 31, "end_row_offset_idx": 32, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.3 VERIFY_GROUP_FOR_USER 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you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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262.60699999999997, "coord_origin": "TOPLEFT"}, "text": "Chapter 1. Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 375.118, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.797, "t": 347.145, "r": 336.783, "b": 356.358, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. 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"TOPLEFT"}, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 430.817, "r_x1": 547.167, "r_y1": 430.817, "r_x2": 547.167, "r_y2": 421.604, "r_x3": 151.197, "r_y3": 421.604, "coord_origin": "TOPLEFT"}, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 136.797, "t": 481.603, "r": 348.65, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 490.816, "r_x1": 348.65, "r_y1": 490.816, "r_x2": 348.65, "r_y2": 481.603, "r_x3": 136.797, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 350.097, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 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column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 151.197, "t": 518.623, "r": 547.127, "b": 527.836, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 527.836, "r_x1": 547.127, "r_y1": 527.836, "r_x2": 547.127, "r_y2": 518.623, "r_x3": 151.197, "r_y3": 518.623, "coord_origin": "TOPLEFT"}, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.797, "t": 531.162, "r": 547.154, "b": 540.375, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 577.875, "r_x1": 547.129, "r_y1": 577.875, "r_x2": 547.129, "r_y2": 568.662, "r_x3": 136.797, "r_y3": 568.662, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "orig": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": 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"TOPLEFT"}, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 136.797, "t": 606.1610000000001, "r": 547.215, "b": 615.374, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 615.374, "r_x1": 547.215, "r_y1": 615.374, "r_x2": 547.215, "r_y2": 606.1610000000001, "r_x3": 136.797, "r_y3": 606.1610000000001, "coord_origin": "TOPLEFT"}, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . 29", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 151.197, "t": 706.12, "r": 547.13, "b": 715.333, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 715.333, "r_x1": 547.13, "r_y1": 715.333, "r_x2": 547.13, "r_y2": 706.12, "r_x3": 151.197, "r_y3": 706.12, "coord_origin": "TOPLEFT"}, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "orig": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl"], "num_rows": 41, "num_cols": 1, "table_cells": [{"bbox": {"l": 136.8, "t": 132.649, "r": 547.19, "b": 141.86199999999997, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 145.12800000000004, "r": 547.184, "b": 154.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 167.62800000000004, "r": 547.199, "b": 176.841, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 190.12800000000004, "r": 547.188, "b": 199.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 347.145, "r": 547.204, "b": 356.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 359.145, "r": 547.214, "b": 368.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 371.625, "r": 547.164, "b": 380.838, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 384.105, "r": 547.156, "b": 393.318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 396.644, "r": 547.193, "b": 405.857, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 409.124, "r": 547.186, "b": 418.337, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 434.144, "r": 547.176, "b": 443.357, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 446.624, "r": 547.158, "b": 455.837, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 459.104, "r": 547.215, "b": 468.317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 506.143, "r": 547.163, "b": 515.356, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, 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190.12800000000004, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 249.32000000000005, "r_x1": 547.129, "r_y1": 249.32000000000005, "r_x2": 547.129, "r_y2": 240.10699999999997, "r_x3": 136.798, "r_y3": 240.10699999999997, "coord_origin": "TOPLEFT"}, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "orig": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 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Securing and protecting IBM DB2 data", "orig": "Chapter 1. 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 211.82100000000003, "r_x1": 547.188, "r_y1": 211.82100000000003, "r_x2": 547.188, "r_y2": 202.60799999999995, "r_x3": 136.798, "r_y3": 202.60799999999995, "coord_origin": "TOPLEFT"}, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "orig": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 224.36, "r_x1": 547.146, "r_y1": 224.36, "r_x2": 547.146, "r_y2": 215.14700000000005, "r_x3": 136.798, "r_y3": 215.14700000000005, "coord_origin": "TOPLEFT"}, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 236.84000000000003, "r_x1": 547.189, "r_y1": 236.84000000000003, "r_x2": 547.189, "r_y2": 227.62699999999995, "r_x3": 136.798, "r_y3": 227.62699999999995, "coord_origin": "TOPLEFT"}, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "orig": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "text", "bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 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262.60699999999997, "coord_origin": "TOPLEFT"}, "text": "Chapter 1. Securing and protecting IBM DB2 data", "orig": "Chapter 1. Securing and protecting IBM DB2 data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "text", "bbox": {"l": 375.118, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 375.118, "r_y0": 271.82000000000005, "r_x1": 547.207, "r_y1": 271.82000000000005, "r_x2": 547.207, "r_y2": 262.60699999999997, "r_x3": 375.118, "r_y3": 262.60699999999997, "coord_origin": "TOPLEFT"}, "text": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "orig": " . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 17, "label": "text", "bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 283.82, "r_x1": 547.216, "r_y1": 283.82, "r_x2": 547.216, "r_y2": 274.60699999999997, "r_x3": 136.798, "r_y3": 274.60699999999997, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "text", "bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 296.359, "r_x1": 547.201, "r_y1": 296.359, "r_x2": 547.201, "r_y2": 287.146, "r_x3": 136.798, "r_y3": 287.146, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "orig": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.798, "r_y0": 308.839, "r_x1": 547.216, "r_y1": 308.839, "r_x2": 547.216, "r_y2": 299.626, "r_x3": 136.798, "r_y3": 299.626, "coord_origin": "TOPLEFT"}, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "orig": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 20, "label": "text", "bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 321.319, "r_x1": 547.156, "r_y1": 321.319, "r_x2": 547.156, "r_y2": 312.106, "r_x3": 151.197, "r_y3": 312.106, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "orig": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 21, "label": "text", "bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 333.859, "r_x1": 547.197, "r_y1": 333.859, "r_x2": 547.197, "r_y2": 324.646, "r_x3": 151.197, "r_y3": 324.646, "coord_origin": "TOPLEFT"}, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "orig": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.797, "t": 347.145, "r": 336.783, "b": 356.358, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 356.358, "r_x1": 336.783, "r_y1": 356.358, "r_x2": 336.783, "r_y2": 347.145, "r_x3": 136.797, "r_y3": 347.145, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties", "orig": "Chapter 2. 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"TOPLEFT"}, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 29, "label": "text", "bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 430.817, "r_x1": 547.167, "r_y1": 430.817, "r_x2": 547.167, "r_y2": 421.604, "r_x3": 151.197, "r_y3": 421.604, "coord_origin": "TOPLEFT"}, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "orig": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 33, "label": "text", "bbox": {"l": 136.797, "t": 481.603, "r": 348.65, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 490.816, "r_x1": 348.65, "r_y1": 490.816, "r_x2": 348.65, "r_y2": 481.603, "r_x3": 136.797, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control", "orig": "Chapter 3. Row and Column Access Control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 34, "label": "text", "bbox": {"l": 350.097, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 350.097, "r_y0": 490.816, "r_x1": 547.205, "r_y1": 490.816, "r_x2": 547.205, "r_y2": 481.603, "r_x3": 350.097, "r_y3": 481.603, "coord_origin": "TOPLEFT"}, "text": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "orig": ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 35, "label": "text", "bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 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column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 37, "label": "text", "bbox": {"l": 151.197, "t": 518.623, "r": 547.127, "b": 527.836, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 527.836, "r_x1": 547.127, "r_y1": 527.836, "r_x2": 547.127, "r_y2": 518.623, "r_x3": 151.197, "r_y3": 518.623, "coord_origin": "TOPLEFT"}, "text": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "orig": "3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.797, "t": 531.162, "r": 547.154, "b": 540.375, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 540.375, "r_x1": 547.154, "r_y1": 540.375, "r_x2": 547.154, "r_y2": 531.162, "r_x3": 136.797, "r_y3": 531.162, "coord_origin": "TOPLEFT"}, "text": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "orig": "3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 151.197, "t": 543.642, "r": 547.169, "b": 552.855, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 552.855, "r_x1": 547.169, "r_y1": 552.855, "r_x2": 547.169, "r_y2": 543.642, "r_x3": 151.197, "r_y3": 543.642, 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 41, "label": "text", "bbox": {"l": 136.797, "t": 568.662, "r": 547.129, "b": 577.875, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 577.875, "r_x1": 547.129, "r_y1": 577.875, "r_x2": 547.129, "r_y2": 568.662, "r_x3": 136.797, "r_y3": 568.662, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "orig": "3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 136.797, "t": 581.141, "r": 547.221, "b": 590.354, "coord_origin": 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"TOPLEFT"}, "text": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . . . . . . 22", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 136.797, "t": 606.1610000000001, "r": 547.215, "b": 615.374, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.797, "r_y0": 615.374, "r_x1": 547.215, "r_y1": 615.374, "r_x2": 547.215, "r_y2": 606.1610000000001, "r_x3": 136.797, "r_y3": 606.1610000000001, "coord_origin": "TOPLEFT"}, "text": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22", "orig": "3.6 Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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. . . . . . 29", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 52, "label": "text", "bbox": {"l": 151.197, "t": 706.12, "r": 547.13, "b": 715.333, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.197, "r_y0": 715.333, "r_x1": 547.13, "r_y1": 715.333, "r_x2": 547.13, "r_y2": 706.12, "r_x3": 151.197, "r_y3": 706.12, "coord_origin": "TOPLEFT"}, "text": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "orig": "3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . . . . . . 32", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl", "rhed", "fcel", "nl"], "num_rows": 41, "num_cols": 1, "table_cells": [{"bbox": {"l": 136.8, "t": 132.649, "r": 547.19, "b": 141.86199999999997, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 145.12800000000004, "r": 547.184, "b": 154.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 167.62800000000004, "r": 547.199, "b": 176.841, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.799, "t": 190.12800000000004, "r": 547.188, "b": 199.341, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 202.60799999999995, "r": 547.188, "b": 211.82100000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 215.14700000000005, "r": 547.146, "b": 224.36, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 5, "end_row_offset_idx": 6, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 227.62699999999995, "r": 547.189, "b": 236.84000000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 6, "end_row_offset_idx": 7, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 240.10699999999997, "r": 547.129, "b": 249.32000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 7, "end_row_offset_idx": 8, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 262.60699999999997, "r": 547.207, "b": 271.82000000000005, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 8, "end_row_offset_idx": 9, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 274.60699999999997, "r": 547.216, "b": 283.82, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 9, "end_row_offset_idx": 10, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 287.146, "r": 547.201, "b": 296.359, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 10, "end_row_offset_idx": 11, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.798, "t": 299.626, "r": 547.216, "b": 308.839, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 11, "end_row_offset_idx": 12, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 312.106, "r": 547.156, "b": 321.319, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 12, "end_row_offset_idx": 13, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 324.646, "r": 547.197, "b": 333.859, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 13, "end_row_offset_idx": 14, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 5", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 347.145, "r": 547.204, "b": 356.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 14, "end_row_offset_idx": 15, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 359.145, "r": 547.214, "b": 368.358, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 15, "end_row_offset_idx": 16, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1 Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 371.625, "r": 547.164, "b": 380.838, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 16, "end_row_offset_idx": 17, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 384.105, "r": 547.156, "b": 393.318, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 17, "end_row_offset_idx": 18, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . . . . . . . 8", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 396.644, "r": 547.193, "b": 405.857, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 18, "end_row_offset_idx": 19, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 409.124, "r": 547.186, "b": 418.337, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 19, "end_row_offset_idx": 20, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 421.604, "r": 547.167, "b": 430.817, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 20, "end_row_offset_idx": 21, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . . . . . . . 9", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 434.144, "r": 547.176, "b": 443.357, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 21, "end_row_offset_idx": 22, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 446.624, "r": 547.158, "b": 455.837, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 22, "end_row_offset_idx": 23, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 459.104, "r": 547.215, "b": 468.317, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 23, "end_row_offset_idx": 24, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 481.603, "r": 547.205, "b": 490.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 24, "end_row_offset_idx": 25, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 136.797, "t": 493.603, "r": 547.173, "b": 502.816, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 25, "end_row_offset_idx": 26, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, {"bbox": {"l": 151.197, "t": 506.143, "r": 547.163, "b": 515.356, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 26, "end_row_offset_idx": 27, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 14", "column_header": false, "row_header": true, "row_section": false}, 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. 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A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. In the years since, Jim has held ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 480.86, "r_x1": 520.251, "r_y1": 480.86, "r_x2": 520.251, "r_y2": 471.647, "r_x3": 263.4, "r_y3": 471.647, "coord_origin": "TOPLEFT"}, "text": "numerous technical roles, including independent software ", "orig": "numerous technical roles, including independent software ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 492.86, "r_x1": 490.706, "r_y1": 492.86, "r_x2": 490.706, "r_y2": 483.647, "r_x3": 263.4, "r_y3": 483.647, "coord_origin": "TOPLEFT"}, "text": "vendors technical support on a broad range of IBM ", "orig": "vendors technical support on a broad range of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 504.86, "r_x1": 530.964, "r_y1": 504.86, "r_x2": 530.964, "r_y2": 495.647, "r_x3": 263.4, "r_y3": 495.647, "coord_origin": "TOPLEFT"}, "text": "technologies and products, and supporting customers in the ", "orig": "technologies and products, and supporting customers in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 516.86, "r_x1": 501.633, "r_y1": 516.86, "r_x2": 501.633, "r_y2": 507.647, "r_x3": 263.4, "r_y3": 507.647, "coord_origin": "TOPLEFT"}, "text": "IBM Executive Briefing Center and IBM Project Office.", "orig": "IBM Executive Briefing Center and IBM Project Office.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 536.8389999999999, "r_x1": 348.368, "r_y1": 536.8389999999999, "r_x2": 348.368, "r_y2": 527.626, "r_x3": 263.4, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": "Hernando Bedoya", "orig": "Hernando Bedoya", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.419, "r_y0": 536.8389999999999, "r_x1": 512.352, "r_y1": 536.8389999999999, "r_x2": 512.352, "r_y2": 527.626, "r_x3": 348.419, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": " is a Senior IT Specialist at STG Lab ", "orig": " is a Senior IT Specialist at STG Lab ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 548.8389999999999, "r_x1": 519.272, "r_y1": 548.8389999999999, "r_x2": 519.272, "r_y2": 539.626, "r_x3": 263.4, "r_y3": 539.626, "coord_origin": "TOPLEFT"}, "text": "Services and Training in Rochester, Minnesota. He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. Hernando can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 680.837, "r_x1": 320.585, "r_y1": 680.837, "r_x2": 320.585, "r_y2": 671.624, "r_x3": 263.4, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": "contacted at ", "orig": "contacted at ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.64, "r_y0": 680.473, "r_x1": 410.578, "r_y1": 680.473, "r_x2": 410.578, "r_y2": 672.072, "r_x3": 320.64, "r_y3": 672.072, "coord_origin": "TOPLEFT"}, "text": "hbedoya@us.ibm.com", "orig": "hbedoya@us.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.579, "r_y0": 680.837, "r_x1": 413.348, "r_y1": 680.837, "r_x2": 413.348, "r_y2": 671.624, "r_x3": 410.579, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 538.86, "t": 754.849, "r": 547.208, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.904093861579895, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "section_header", "bbox": {"l": 64.8, "t": 73.84799999999996, "r": 151.469, "b": 96.048, "coord_origin": "TOPLEFT"}, "confidence": 0.9174711108207703, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 132.649, "r": 547.557, "b": 201.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 189.861, "r_x1": 547.557, "r_y1": 189.861, "r_x2": 547.557, "r_y2": 180.64800000000002, "r_x3": 136.8, "r_y3": 180.64800000000002, "coord_origin": "TOPLEFT"}, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "orig": "of defining, creating, and implementing the row permissions and column masks in a relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 201.861, "r_x1": 238.276, "r_y1": 201.861, "r_x2": 238.276, "r_y2": 192.64800000000002, "r_x3": 136.8, "r_y3": 192.64800000000002, "coord_origin": "TOPLEFT"}, "text": "database environment.", "orig": "database environment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 214.60699999999997, "r": 546.415, "b": 259.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.82000000000005, "r_x1": 524.194, "r_y1": 223.82000000000005, "r_x2": 524.194, "r_y2": 214.60699999999997, "r_x3": 136.8, "r_y3": 214.60699999999997, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application developers, and ", "orig": "This paper is intended for database engineers, data-centric application developers, and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.82000000000005, "r_x1": 546.415, "r_y1": 235.82000000000005, "r_x2": 546.415, "r_y2": 226.60699999999997, "r_x3": 136.8, "r_y3": 226.60699999999997, "coord_origin": "TOPLEFT"}, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "orig": "security officers who want to design and implement RCAC as a part of their data control and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 247.82000000000005, "r_x1": 521.264, "r_y1": 247.82000000000005, "r_x2": 521.264, "r_y2": 238.60699999999997, "r_x3": 136.8, "r_y3": 238.60699999999997, "coord_origin": "TOPLEFT"}, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "section_header", "bbox": {"l": 64.8, "t": 288.301, "r": 125.338, "b": 303.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.629, "r": 547.246, "b": 341.842, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 263.4, "t": 375.649, "r": 541.74, "b": 516.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. 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He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.86, "t": 754.849, "r": 547.208, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.904093861579895, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "xi"}, {"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.8, "t": 73.84799999999996, "r": 151.469, "b": 96.048, "coord_origin": "TOPLEFT"}, "confidence": 0.9174711108207703, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 132.649, "r": 547.557, "b": 201.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 189.861, "r_x1": 547.557, "r_y1": 189.861, "r_x2": 547.557, "r_y2": 180.64800000000002, "r_x3": 136.8, "r_y3": 180.64800000000002, "coord_origin": "TOPLEFT"}, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "orig": "of defining, creating, and implementing the row permissions and column masks in a relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 201.861, "r_x1": 238.276, "r_y1": 201.861, "r_x2": 238.276, "r_y2": 192.64800000000002, "r_x3": 136.8, "r_y3": 192.64800000000002, "coord_origin": "TOPLEFT"}, "text": "database environment.", "orig": "database environment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 214.60699999999997, "r": 546.415, "b": 259.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.82000000000005, "r_x1": 524.194, "r_y1": 223.82000000000005, "r_x2": 524.194, "r_y2": 214.60699999999997, "r_x3": 136.8, "r_y3": 214.60699999999997, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application developers, and ", "orig": "This paper is intended for database engineers, data-centric application developers, and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.82000000000005, "r_x1": 546.415, "r_y1": 235.82000000000005, "r_x2": 546.415, "r_y2": 226.60699999999997, "r_x3": 136.8, "r_y3": 226.60699999999997, "coord_origin": "TOPLEFT"}, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "orig": "security officers who want to design and implement RCAC as a part of their data control and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 247.82000000000005, "r_x1": 521.264, "r_y1": 247.82000000000005, "r_x2": 521.264, "r_y2": 238.60699999999997, "r_x3": 136.8, "r_y3": 238.60699999999997, "coord_origin": "TOPLEFT"}, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.8, "t": 288.301, "r": 125.338, "b": 303.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.629, "r": 547.246, "b": 341.842, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.4, "t": 375.649, "r": 541.74, "b": 516.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. In the years since, Jim has held ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 480.86, "r_x1": 520.251, "r_y1": 480.86, "r_x2": 520.251, "r_y2": 471.647, "r_x3": 263.4, "r_y3": 471.647, "coord_origin": "TOPLEFT"}, "text": "numerous technical roles, including independent software ", "orig": "numerous technical roles, including independent software ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 492.86, "r_x1": 490.706, "r_y1": 492.86, "r_x2": 490.706, "r_y2": 483.647, "r_x3": 263.4, "r_y3": 483.647, "coord_origin": "TOPLEFT"}, "text": "vendors technical support on a broad range of IBM ", "orig": "vendors technical support on a broad range of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 504.86, "r_x1": 530.964, "r_y1": 504.86, "r_x2": 530.964, "r_y2": 495.647, "r_x3": 263.4, "r_y3": 495.647, "coord_origin": "TOPLEFT"}, "text": "technologies and products, and supporting customers in the ", "orig": "technologies and products, and supporting customers in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 516.86, "r_x1": 501.633, "r_y1": 516.86, "r_x2": 501.633, "r_y2": 507.647, "r_x3": 263.4, "r_y3": 507.647, "coord_origin": "TOPLEFT"}, "text": "IBM Executive Briefing Center and IBM Project Office.", "orig": "IBM Executive Briefing Center and IBM Project Office.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.4, "t": 527.626, "r": 541.582, "b": 680.837, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 536.8389999999999, "r_x1": 348.368, "r_y1": 536.8389999999999, "r_x2": 348.368, "r_y2": 527.626, "r_x3": 263.4, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": "Hernando Bedoya", "orig": "Hernando Bedoya", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.419, "r_y0": 536.8389999999999, "r_x1": 512.352, "r_y1": 536.8389999999999, "r_x2": 512.352, "r_y2": 527.626, "r_x3": 348.419, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": " is a Senior IT Specialist at STG Lab ", "orig": " is a Senior IT Specialist at STG Lab ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 548.8389999999999, "r_x1": 519.272, "r_y1": 548.8389999999999, "r_x2": 519.272, "r_y2": 539.626, "r_x3": 263.4, "r_y3": 539.626, "coord_origin": "TOPLEFT"}, "text": "Services and Training in Rochester, Minnesota. He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. Hernando can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 680.837, "r_x1": 320.585, "r_y1": 680.837, "r_x2": 320.585, "r_y2": 671.624, "r_x3": 263.4, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": "contacted at ", "orig": "contacted at ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.64, "r_y0": 680.473, "r_x1": 410.578, "r_y1": 680.473, "r_x2": 410.578, "r_y2": 672.072, "r_x3": 320.64, "r_y3": 672.072, "coord_origin": "TOPLEFT"}, "text": "hbedoya@us.ibm.com", "orig": "hbedoya@us.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.579, "r_y0": 680.837, "r_x1": 413.348, "r_y1": 680.837, "r_x2": 413.348, "r_y2": 671.624, "r_x3": 410.579, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.4144744873047, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9871657490730286, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "section_header", "id": 9, "page_no": 3, "cluster": {"id": 9, "label": "section_header", "bbox": {"l": 64.8, "t": 73.84799999999996, "r": 151.469, "b": 96.048, "coord_origin": "TOPLEFT"}, "confidence": 0.9174711108207703, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 96.048, "r_x1": 151.469, "r_y1": 96.048, "r_x2": 151.469, "r_y2": 73.84799999999996, "r_x3": 64.8, "r_y3": 73.84799999999996, "coord_origin": "TOPLEFT"}, "text": "Preface", "orig": "Preface", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Preface"}, {"label": "text", "id": 2, "page_no": 3, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 132.649, "r": 547.557, "b": 201.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9869155883789062, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 141.86199999999997, "r_x1": 542.928, "r_y1": 141.86199999999997, "r_x2": 542.928, "r_y2": 132.649, "r_x3": 136.8, "r_y3": 132.649, "coord_origin": "TOPLEFT"}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "orig": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 153.861, "r_x1": 526.664, "r_y1": 153.861, "r_x2": 526.664, "r_y2": 144.64800000000002, "r_x3": 136.8, "r_y3": 144.64800000000002, "coord_origin": "TOPLEFT"}, "text": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "orig": "DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 165.861, "r_x1": 536.771, "r_y1": 165.861, "r_x2": 536.771, "r_y2": 156.64800000000002, "r_x3": 136.8, "r_y3": 156.64800000000002, "coord_origin": "TOPLEFT"}, "text": "function and advantages of controlling access to data in a comprehensive and transparent ", "orig": "function and advantages of controlling access to data in a comprehensive and transparent ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 177.861, "r_x1": 544.689, "r_y1": 177.861, "r_x2": 544.689, "r_y2": 168.64800000000002, "r_x3": 136.8, "r_y3": 168.64800000000002, "coord_origin": "TOPLEFT"}, "text": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "orig": "way. This publication helps you understand the capabilities of RCAC and provides examples ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 189.861, "r_x1": 547.557, "r_y1": 189.861, "r_x2": 547.557, "r_y2": 180.64800000000002, "r_x3": 136.8, "r_y3": 180.64800000000002, "coord_origin": "TOPLEFT"}, "text": "of defining, creating, and implementing the row permissions and column masks in a relational ", "orig": "of defining, creating, and implementing the row permissions and column masks in a relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 201.861, "r_x1": 238.276, "r_y1": 201.861, "r_x2": 238.276, "r_y2": 192.64800000000002, "r_x3": 136.8, "r_y3": 192.64800000000002, "coord_origin": "TOPLEFT"}, "text": "database environment.", "orig": "database environment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This IBMfi Redpaper\u2122 publication provides information about the IBM i 7.2 feature of IBM DB2fi for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 3, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 214.60699999999997, "r": 546.415, "b": 259.82000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9872201681137085, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.82000000000005, "r_x1": 524.194, "r_y1": 223.82000000000005, "r_x2": 524.194, "r_y2": 214.60699999999997, "r_x3": 136.8, "r_y3": 214.60699999999997, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application developers, and ", "orig": "This paper is intended for database engineers, data-centric application developers, and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.82000000000005, "r_x1": 546.415, "r_y1": 235.82000000000005, "r_x2": 546.415, "r_y2": 226.60699999999997, "r_x3": 136.8, "r_y3": 226.60699999999997, "coord_origin": "TOPLEFT"}, "text": "security officers who want to design and implement RCAC as a part of their data control and ", "orig": "security officers who want to design and implement RCAC as a part of their data control and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 247.82000000000005, "r_x1": 521.264, "r_y1": 247.82000000000005, "r_x2": 521.264, "r_y2": 238.60699999999997, "r_x3": 136.8, "r_y3": 238.60699999999997, "coord_origin": "TOPLEFT"}, "text": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "orig": "governance policy. A solid background in IBM i object level security, DB2 for i relational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 259.82000000000005, "r_x1": 321.654, "r_y1": 259.82000000000005, "r_x2": 321.654, "r_y2": 250.60699999999997, "r_x3": 136.8, "r_y3": 250.60699999999997, "coord_origin": "TOPLEFT"}, "text": "database concepts, and SQL is assumed.", "orig": "database concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed."}, {"label": "section_header", "id": 8, "page_no": 3, "cluster": {"id": 8, "label": "section_header", "bbox": {"l": 64.8, "t": 288.301, "r": 125.338, "b": 303.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9255507588386536, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 303.064, "r_x1": 125.338, "r_y1": 303.064, "r_x2": 125.338, "r_y2": 288.301, "r_x3": 64.8, "r_y3": 288.301, "coord_origin": "TOPLEFT"}, "text": "Authors", "orig": "Authors", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Authors"}, {"label": "text", "id": 6, "page_no": 3, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 320.629, "r": 547.246, "b": 341.842, "coord_origin": "TOPLEFT"}, "confidence": 0.9713318347930908, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.842, "r_x1": 547.246, "r_y1": 329.842, "r_x2": 547.246, "r_y2": 320.629, "r_x3": 136.8, "r_y3": 320.629, "coord_origin": "TOPLEFT"}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "orig": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.842, "r_x1": 505.021, "r_y1": 341.842, "r_x2": 505.021, "r_y2": 332.629, "r_x3": 136.8, "r_y3": 332.629, "coord_origin": "TOPLEFT"}, "text": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "orig": "the International Technical Support Organization (ITSO), Rochester, Minnesota US.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US."}, {"label": "text", "id": 4, "page_no": 3, "cluster": {"id": 4, "label": "text", "bbox": {"l": 263.4, "t": 375.649, "r": 541.74, "b": 516.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9842760562896729, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 384.862, "r_x1": 335.697, "r_y1": 384.862, "r_x2": 335.697, "r_y2": 375.649, "r_x3": 263.4, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": "Jim Bainbridge", "orig": "Jim Bainbridge", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 335.699, "r_y0": 384.862, "r_x1": 529.351, "r_y1": 384.862, "r_x2": 529.351, "r_y2": 375.649, "r_x3": 335.699, "r_y3": 375.649, "coord_origin": "TOPLEFT"}, "text": " is a senior DB2 consultant on the DB2 for i ", "orig": " is a senior DB2 consultant on the DB2 for i ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 396.862, "r_x1": 511.516, "r_y1": 396.862, "r_x2": 511.516, "r_y2": 387.649, "r_x3": 263.4, "r_y3": 387.649, "coord_origin": "TOPLEFT"}, "text": "Center of Excellence team in the IBM Lab Services and ", "orig": "Center of Excellence team in the IBM Lab Services and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 408.861, "r_x1": 499.086, "r_y1": 408.861, "r_x2": 499.086, "r_y2": 399.648, "r_x3": 263.4, "r_y3": 399.648, "coord_origin": "TOPLEFT"}, "text": "Training organization. His primary role is training and ", "orig": "Training organization. His primary role is training and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 420.861, "r_x1": 522.529, "r_y1": 420.861, "r_x2": 522.529, "r_y2": 411.648, "r_x3": 263.4, "r_y3": 411.648, "coord_origin": "TOPLEFT"}, "text": "implementation services for IBM DB2 Web Query for i and ", "orig": "implementation services for IBM DB2 Web Query for i and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 432.861, "r_x1": 541.74, "r_y1": 432.861, "r_x2": 541.74, "r_y2": 423.648, "r_x3": 263.4, "r_y3": 423.648, "coord_origin": "TOPLEFT"}, "text": "business analytics. Jim began his career with IBM 30 years ago ", "orig": "business analytics. Jim began his career with IBM 30 years ago ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 444.861, "r_x1": 534.723, "r_y1": 444.861, "r_x2": 534.723, "r_y2": 435.648, "r_x3": 263.4, "r_y3": 435.648, "coord_origin": "TOPLEFT"}, "text": "in the IBM Rochester Development Lab, where he developed ", "orig": "in the IBM Rochester Development Lab, where he developed ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 456.861, "r_x1": 541.413, "r_y1": 456.861, "r_x2": 541.413, "r_y2": 447.648, "r_x3": 263.4, "r_y3": 447.648, "coord_origin": "TOPLEFT"}, "text": "cooperative processing products that paired IBM PCs with IBM ", "orig": "cooperative processing products that paired IBM PCs with IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 468.86, "r_x1": 528.919, "r_y1": 468.86, "r_x2": 528.919, "r_y2": 459.647, "r_x3": 263.4, "r_y3": 459.647, "coord_origin": "TOPLEFT"}, "text": "S/36 and AS/.400 systems. In the years since, Jim has held ", "orig": "S/36 and AS/.400 systems. In the years since, Jim has held ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 480.86, "r_x1": 520.251, "r_y1": 480.86, "r_x2": 520.251, "r_y2": 471.647, "r_x3": 263.4, "r_y3": 471.647, "coord_origin": "TOPLEFT"}, "text": "numerous technical roles, including independent software ", "orig": "numerous technical roles, including independent software ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 492.86, "r_x1": 490.706, "r_y1": 492.86, "r_x2": 490.706, "r_y2": 483.647, "r_x3": 263.4, "r_y3": 483.647, "coord_origin": "TOPLEFT"}, "text": "vendors technical support on a broad range of IBM ", "orig": "vendors technical support on a broad range of IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 504.86, "r_x1": 530.964, "r_y1": 504.86, "r_x2": 530.964, "r_y2": 495.647, "r_x3": 263.4, "r_y3": 495.647, "coord_origin": "TOPLEFT"}, "text": "technologies and products, and supporting customers in the ", "orig": "technologies and products, and supporting customers in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 516.86, "r_x1": 501.633, "r_y1": 516.86, "r_x2": 501.633, "r_y2": 507.647, "r_x3": 263.4, "r_y3": 507.647, "coord_origin": "TOPLEFT"}, "text": "IBM Executive Briefing Center and IBM Project Office.", "orig": "IBM Executive Briefing Center and IBM Project Office.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office."}, {"label": "text", "id": 5, "page_no": 3, "cluster": {"id": 5, "label": "text", "bbox": {"l": 263.4, "t": 527.626, "r": 541.582, "b": 680.837, "coord_origin": "TOPLEFT"}, "confidence": 0.9823779463768005, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 536.8389999999999, "r_x1": 348.368, "r_y1": 536.8389999999999, "r_x2": 348.368, "r_y2": 527.626, "r_x3": 263.4, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": "Hernando Bedoya", "orig": "Hernando Bedoya", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 348.419, "r_y0": 536.8389999999999, "r_x1": 512.352, "r_y1": 536.8389999999999, "r_x2": 512.352, "r_y2": 527.626, "r_x3": 348.419, "r_y3": 527.626, "coord_origin": "TOPLEFT"}, "text": " is a Senior IT Specialist at STG Lab ", "orig": " is a Senior IT Specialist at STG Lab ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 548.8389999999999, "r_x1": 519.272, "r_y1": 548.8389999999999, "r_x2": 519.272, "r_y2": 539.626, "r_x3": 263.4, "r_y3": 539.626, "coord_origin": "TOPLEFT"}, "text": "Services and Training in Rochester, Minnesota. He writes ", "orig": "Services and Training in Rochester, Minnesota. He writes ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 560.8389999999999, "r_x1": 538.412, "r_y1": 560.8389999999999, "r_x2": 538.412, "r_y2": 551.626, "r_x3": 263.4, "r_y3": 551.626, "coord_origin": "TOPLEFT"}, "text": "extensively and teaches IBM classes worldwide in all areas of ", "orig": "extensively and teaches IBM classes worldwide in all areas of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 572.8389999999999, "r_x1": 533.966, "r_y1": 572.8389999999999, "r_x2": 533.966, "r_y2": 563.626, "r_x3": 263.4, "r_y3": 563.626, "coord_origin": "TOPLEFT"}, "text": "DB2 for i. Before joining STG Lab Services, he worked in the ", "orig": "DB2 for i. Before joining STG Lab Services, he worked in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 584.8389999999999, "r_x1": 496.954, "r_y1": 584.8389999999999, "r_x2": 496.954, "r_y2": 575.626, "r_x3": 263.4, "r_y3": 575.626, "coord_origin": "TOPLEFT"}, "text": "ITSO for nine years writing multiple IBM Redbooksfi ", "orig": "ITSO for nine years writing multiple IBM Redbooksfi ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 596.838, "r_x1": 520.394, "r_y1": 596.838, "r_x2": 520.394, "r_y2": 587.625, "r_x3": 263.4, "r_y3": 587.625, "coord_origin": "TOPLEFT"}, "text": "publications. He also worked for IBM Colombia as an IBM ", "orig": "publications. He also worked for IBM Colombia as an IBM ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 608.838, "r_x1": 535.94, "r_y1": 608.838, "r_x2": 535.94, "r_y2": 599.625, "r_x3": 263.4, "r_y3": 599.625, "coord_origin": "TOPLEFT"}, "text": "AS/400fi IT Specialist doing presales support for the Andean ", "orig": "AS/400fi IT Specialist doing presales support for the Andean ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 620.838, "r_x1": 541.582, "r_y1": 620.838, "r_x2": 541.582, "r_y2": 611.625, "r_x3": 263.4, "r_y3": 611.625, "coord_origin": "TOPLEFT"}, "text": "countries. He has 28 years of experience in the computing field ", "orig": "countries. He has 28 years of experience in the computing field ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 632.838, "r_x1": 541.573, "r_y1": 632.838, "r_x2": 541.573, "r_y2": 623.625, "r_x3": 263.4, "r_y3": 623.625, "coord_origin": "TOPLEFT"}, "text": "and has taught database classes in Colombian universities. He ", "orig": "and has taught database classes in Colombian universities. He ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 644.838, "r_x1": 523.231, "r_y1": 644.838, "r_x2": 523.231, "r_y2": 635.625, "r_x3": 263.4, "r_y3": 635.625, "coord_origin": "TOPLEFT"}, "text": "holds a Master's degree in Computer Science from EAFIT, ", "orig": "holds a Master's degree in Computer Science from EAFIT, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 656.837, "r_x1": 524.783, "r_y1": 656.837, "r_x2": 524.783, "r_y2": 647.624, "r_x3": 263.4, "r_y3": 647.624, "coord_origin": "TOPLEFT"}, "text": "Colombia. His areas of expertise are database technology, ", "orig": "Colombia. His areas of expertise are database technology, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 668.837, "r_x1": 508.28, "r_y1": 668.837, "r_x2": 508.28, "r_y2": 659.624, "r_x3": 263.4, "r_y3": 659.624, "coord_origin": "TOPLEFT"}, "text": "performance, and data warehousing. Hernando can be ", "orig": "performance, and data warehousing. Hernando can be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 263.4, "r_y0": 680.837, "r_x1": 320.585, "r_y1": 680.837, "r_x2": 320.585, "r_y2": 671.624, "r_x3": 263.4, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": "contacted at ", "orig": "contacted at ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 320.64, "r_y0": 680.473, "r_x1": 410.578, "r_y1": 680.473, "r_x2": 410.578, "r_y2": 672.072, "r_x3": 320.64, "r_y3": 672.072, "coord_origin": "TOPLEFT"}, "text": "hbedoya@us.ibm.com", "orig": "hbedoya@us.ibm.com", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 410.579, "r_y0": 680.837, "r_x1": 413.348, "r_y1": 680.837, "r_x2": 413.348, "r_y2": 671.624, "r_x3": 410.579, "r_y3": 671.624, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooksfi publications. He also worked for IBM Colombia as an IBM AS/400fi IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com ."}, {"label": "picture", "id": 3, "page_no": 3, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 142.52883911132812, "t": 375.0449523925781, "r": 251.47850036621094, "b": 503.20648193359375, "coord_origin": "TOPLEFT"}, "confidence": 0.9862572550773621, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 3, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 145.4144744873047, "t": 527.2447509765625, "r": 252.08840942382812, "b": 635.383056640625, "coord_origin": "TOPLEFT"}, "confidence": 0.9871657490730286, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 7, "page_no": 3, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9469243884086609, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 10, "page_no": 3, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 538.86, "t": 754.849, "r": 547.208, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.904093861579895, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 538.86, "r_y0": 764.062, "r_x1": 547.208, "r_y1": 764.062, "r_x2": 547.208, "r_y2": 754.849, "r_x3": 538.86, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "xi", "orig": "xi", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "xi"}]}}, {"page_no": 4, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, "coord_origin": "TOPLEFT"}, "text": "global businesses of all sizes. The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 538.939, "r_x1": 250.2, "r_y1": 538.939, "r_x2": 250.2, "r_y2": 529.726, "r_x3": 151.2, "r_y3": 529.726, "coord_origin": "TOPLEFT"}, "text": "Security fundamentals", "orig": "Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 550.575, "r_x1": 141.78, "r_y1": 550.575, "r_x2": 141.78, "r_y2": 542.174, "r_x3": 136.8, "r_y3": 542.174, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 550.939, "r_x1": 282.955, "r_y1": 550.939, "r_x2": 282.955, "r_y2": 541.726, "r_x3": 151.2, "r_y3": 541.726, "coord_origin": "TOPLEFT"}, "text": "Current state of IBM i security", "orig": "Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, 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"text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 143.88, "r_y0": 734.68, "r_x1": 231.872, "r_y1": 734.68, "r_x2": 231.872, "r_y2": 727.949, "r_x3": 143.88, "r_y3": 727.949, "coord_origin": "TOPLEFT"}, "text": "http://www.ponemon.org", "orig": "http://www.ponemon.org", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 231.84, "r_y0": 734.972, "r_x1": 234.059, "r_y1": 734.972, "r_x2": 234.059, "r_y2": 727.59, "r_x3": 231.84, "r_y3": 727.59, "coord_origin": "TOPLEFT"}, "text": "/", "orig": "/", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 6, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9364771246910095, "cells": 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "page_footer", "bbox": {"l": 541.68, "t": 754.849, "r": 547.218, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 19, "label": "text", "bbox": {"l": 81.0, "t": 268.543, "r": 115.155, "b": 274.981, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88599999999997, "r": 549.157, "b": 309.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9371159076690674, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 348.709, "r": 547.503, "b": 429.921, "coord_origin": "TOPLEFT"}, "confidence": 0.9868631362915039, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, "coord_origin": "TOPLEFT"}, "text": "global businesses of all sizes. The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 442.728, "r": 527.156, "b": 487.94, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 500.687, "r": 547.404, "b": 521.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 529.726, "r": 250.2, "b": 538.939, "coord_origin": "TOPLEFT"}, "confidence": 0.9373421669006348, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, 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All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.68, "t": 754.849, "r": 547.218, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}, {"label": "text", "id": 19, "page_no": 4, "cluster": {"id": 19, "label": "text", "bbox": {"l": 81.0, "t": 268.543, "r": 115.155, "b": 274.981, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 1."}, {"label": "section_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88599999999997, "r": 549.157, "b": 309.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9371159076690674, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Securing and protecting IBM DB2 data"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 348.709, "r": 547.503, "b": 429.921, "coord_origin": "TOPLEFT"}, "confidence": 0.9868631362915039, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, "coord_origin": "TOPLEFT"}, "text": "global businesses of all sizes. The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 442.728, "r": 527.156, "b": 487.94, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 500.687, "r": 547.404, "b": 521.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. 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"text": "2 http://www.ponemon.org /"}, {"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.075252532958984, "t": 70.57737731933594, "r": 239.620361328125, "b": 237.9579315185547, "coord_origin": "TOPLEFT"}, "confidence": 0.7509335279464722, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 19, "page_no": 4, "cluster": {"id": 19, "label": "text", "bbox": {"l": 81.0, "t": 268.543, "r": 115.155, "b": 274.981, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 81.0, "r_y0": 274.981, "r_x1": 115.155, "r_y1": 274.981, "r_x2": 115.155, "r_y2": 268.543, "r_x3": 81.0, "r_y3": 268.543, "coord_origin": "TOPLEFT"}, "text": "Chapter 1.", "orig": "Chapter 1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 1."}, {"label": "section_header", "id": 5, "page_no": 4, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 136.8, "t": 254.88599999999997, "r": 549.157, "b": 309.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9371159076690674, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.918, "r_x1": 549.157, "r_y1": 278.918, "r_x2": 549.157, "r_y2": 254.88599999999997, "r_x3": 136.8, "r_y3": 254.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Securing and protecting IBM DB2 ", "orig": "Securing and protecting IBM DB2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 309.878, "r_x1": 190.277, "r_y1": 309.878, "r_x2": 190.277, "r_y2": 285.847, "r_x3": 136.8, "r_y3": 285.847, "coord_origin": "TOPLEFT"}, "text": "data", "orig": "data", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Securing and protecting IBM DB2 data"}, {"label": "text", "id": 0, "page_no": 4, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 348.709, "r": 547.503, "b": 429.921, "coord_origin": "TOPLEFT"}, "confidence": 0.9868631362915039, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 357.922, "r_x1": 542.266, "r_y1": 357.922, "r_x2": 542.266, "r_y2": 348.709, "r_x3": 136.8, "r_y3": 348.709, "coord_origin": "TOPLEFT"}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "orig": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 369.922, "r_x1": 426.796, "r_y1": 369.922, "r_x2": 426.796, "r_y2": 360.709, "r_x3": 136.801, "r_y3": 360.709, 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The Identity Theft Resource Center", "orig": "global businesses of all sizes. The Identity Theft Resource Center", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 426.84, "r_y0": 365.492, "r_x1": 544.975, "r_y1": 369.922, "r_x2": 544.975, "r_y2": 360.709, "r_x3": 426.84, "r_y3": 358.11, "coord_origin": "TOPLEFT"}, "text": "1 reports that almost 5000 ", "orig": "1 reports that almost 5000 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 381.922, "r_x1": 529.547, "r_y1": 381.922, "r_x2": 529.547, "r_y2": 372.709, "r_x3": 136.8, "r_y3": 372.709, "coord_origin": "TOPLEFT"}, "text": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "orig": "data breaches have occurred since 2005, exposing over 600 million records of data. The ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 393.921, "r_x1": 528.11, "r_y1": 393.921, "r_x2": 528.11, "r_y2": 384.708, "r_x3": 136.8, "r_y3": 384.708, "coord_origin": "TOPLEFT"}, "text": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "orig": "financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 528.12, "r_y0": 389.492, "r_x1": 535.329, "r_y1": 393.922, "r_x2": 535.329, "r_y2": 384.709, "r_x3": 528.12, "r_y3": 382.11, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 405.922, "r_x1": 521.653, "r_y1": 405.922, "r_x2": 521.653, "r_y2": 396.709, "r_x3": 136.8, "r_y3": 396.709, "coord_origin": "TOPLEFT"}, "text": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "orig": "revealed that the average cost of a data breach increased in 2013 by 15% globally and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 417.921, "r_x1": 547.14, "r_y1": 417.921, "r_x2": 547.14, "r_y2": 408.708, "r_x3": 136.8, "r_y3": 408.708, "coord_origin": "TOPLEFT"}, "text": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "orig": "resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 429.921, "r_x1": 547.503, "r_y1": 429.921, "r_x2": 547.503, "r_y2": 420.708, "r_x3": 136.8, "r_y3": 420.708, "coord_origin": "TOPLEFT"}, "text": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "orig": "each lost record containing sensitive information increased more than 9% to $145 per record. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Recent news headlines are filled with reports of data breaches and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record."}, {"label": "text", "id": 1, "page_no": 4, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 442.728, "r": 527.156, "b": 487.94, "coord_origin": "TOPLEFT"}, "confidence": 0.9865838885307312, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.941, "r_x1": 525.074, "r_y1": 451.941, "r_x2": 525.074, "r_y2": 442.728, "r_x3": 136.8, "r_y3": 442.728, "coord_origin": "TOPLEFT"}, "text": "Businesses must make a serious effort to secure their data and recognize that securing ", "orig": "Businesses must make a serious effort to secure their data and recognize that securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 463.941, "r_x1": 518.277, "r_y1": 463.941, "r_x2": 518.277, "r_y2": 454.728, "r_x3": 136.8, "r_y3": 454.728, "coord_origin": "TOPLEFT"}, "text": "information assets is a cost of doing business. In many parts of the world and in many ", "orig": "information assets is a cost of doing business. In many parts of the world and in many ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 475.94, "r_x1": 527.156, "r_y1": 475.94, "r_x2": 527.156, "r_y2": 466.727, "r_x3": 136.8, "r_y3": 466.727, "coord_origin": "TOPLEFT"}, "text": "industries, securing the data is required by law and subject to audits. Data security is no ", "orig": "industries, securing the data is required by law and subject to audits. Data security is no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 487.94, "r_x1": 296.275, "r_y1": 487.94, "r_x2": 296.275, "r_y2": 478.727, "r_x3": 136.8, "r_y3": 478.727, "coord_origin": "TOPLEFT"}, "text": "longer an option; it is a requirement.", "orig": "longer an option; it is a requirement.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement."}, {"label": "text", "id": 2, "page_no": 4, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 500.687, "r": 547.404, "b": 521.9, "coord_origin": "TOPLEFT"}, "confidence": 0.9734062552452087, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 509.9, "r_x1": 547.404, "r_y1": 509.9, "r_x2": 547.404, "r_y2": 500.687, "r_x3": 136.8, "r_y3": 500.687, "coord_origin": "TOPLEFT"}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "orig": "This chapter describes how you can secure and protect data in DB2 for i. The following topics ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 521.9, "r_x1": 257.248, "r_y1": 521.9, "r_x2": 257.248, "r_y2": 512.687, "r_x3": 136.8, "r_y3": 512.687, "coord_origin": "TOPLEFT"}, "text": "are covered in this chapter:", "orig": "are covered in this chapter:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter:"}, {"label": "list_item", "id": 4, "page_no": 4, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 529.726, "r": 250.2, "b": 538.939, "coord_origin": "TOPLEFT"}, "confidence": 0.9373421669006348, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 538.575, "r_x1": 141.78, "r_y1": 538.575, "r_x2": 141.78, "r_y2": 530.175, "r_x3": 136.8, "r_y3": 530.175, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 538.939, "r_x1": 250.2, "r_y1": 538.939, "r_x2": 250.2, "r_y2": 529.726, "r_x3": 151.2, "r_y3": 529.726, "coord_origin": "TOPLEFT"}, "text": "Security fundamentals", "orig": "Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, 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"text": "2 http://www.ponemon.org /"}, {"label": "picture", "id": 11, "page_no": 4, "cluster": {"id": 11, "label": "picture", "bbox": {"l": 32.075252532958984, "t": 70.57737731933594, "r": 239.620361328125, "b": 237.9579315185547, "coord_origin": "TOPLEFT"}, "confidence": 0.7509335279464722, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 6, "page_no": 4, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 64.8, "t": 755.538, "r": 257.252, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9364771246910095, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 763.863, "r_x1": 257.252, "r_y1": 763.863, "r_x2": 257.252, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "' Copyright IBM Corp. 2014. All rights reserved.", "orig": "' Copyright IBM Corp. 2014. All rights reserved.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "' Copyright IBM Corp. 2014. All rights reserved."}, {"label": "page_footer", "id": 8, "page_no": 4, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 541.68, "t": 754.849, "r": 547.218, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8511815071105957, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 541.68, "r_y0": 764.062, "r_x1": 547.218, "r_y1": 764.062, "r_x2": 547.218, "r_y2": 754.849, "r_x3": 541.68, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "1", "orig": "1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1"}]}}, {"page_no": 5, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 127.822, "r_x1": 324.463, "r_y1": 127.822, "r_x2": 324.463, "r_y2": 118.60900000000004, "r_x3": 136.8, "r_y3": 118.60900000000004, "coord_origin": "TOPLEFT"}, "text": "information assets that must be described:", "orig": "information assets that must be described:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 10, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 74.34100000000001, "r": 267.39, "b": 89.10400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.60900000000004, "r": 545.014, "b": 127.822, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 127.822, "r_x1": 324.463, "r_y1": 127.822, "r_x2": 324.463, "r_y2": 118.60900000000004, "r_x3": 136.8, "r_y3": 118.60900000000004, "coord_origin": "TOPLEFT"}, "text": "information assets that must be described:", "orig": "information assets that must be described:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.48699999999997, "r": 547.354, "b": 180.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 151.199, "t": 188.62800000000004, "r": 547.57, "b": 269.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 151.199, "t": 277.60699999999997, "r": 542.001, "b": 286.82, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.799, "t": 294.487, "r": 547.407, "b": 375.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 388.607, "r": 535.371, "b": 409.82, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 438.301, "r": 323.37, "b": 453.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.629, "r": 547.567, "b": 515.841, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.648, "r": 547.593, "b": 585.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.667, "r": 547.652, "b": 679.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"index": 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false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 74.34100000000001, "r": 267.39, "b": 89.10400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.60900000000004, "r": 545.014, "b": 127.822, "coord_origin": "TOPLEFT"}, 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false}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.48699999999997, "r": 547.354, "b": 180.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.199, "t": 188.62800000000004, "r": 547.57, "b": 269.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.199, "t": 277.60699999999997, "r": 542.001, "b": 286.82, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.799, "t": 294.487, "r": 547.407, "b": 375.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 388.607, "r": 535.371, "b": 409.82, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 438.301, "r": 323.37, "b": 453.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.629, "r": 547.567, "b": 515.841, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.648, "r": 547.593, "b": 585.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.667, "r": 547.652, "b": 679.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "body": [{"label": "section_header", "id": 6, "page_no": 5, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 74.34100000000001, "r": 267.39, "b": 89.10400000000004, "coord_origin": "TOPLEFT"}, "confidence": 0.9651358723640442, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 89.10400000000004, "r_x1": 267.39, "r_y1": 89.10400000000004, "r_x2": 267.39, "r_y2": 74.34100000000001, "r_x3": 64.8, "r_y3": 74.34100000000001, "coord_origin": "TOPLEFT"}, "text": "1.1 Security fundamentals", "orig": "1.1 Security fundamentals", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.1 Security fundamentals"}, {"label": "text", "id": 5, "page_no": 5, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 106.60900000000004, "r": 545.014, "b": 127.822, "coord_origin": "TOPLEFT"}, "confidence": 0.9663435220718384, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 115.822, "r_x1": 545.014, "r_y1": 115.822, "r_x2": 545.014, "r_y2": 106.60900000000004, "r_x3": 136.8, "r_y3": 106.60900000000004, "coord_origin": "TOPLEFT"}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing ", "orig": "Before reviewing database security techniques, there are two fundamental steps in securing ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 127.822, "r_x1": 324.463, "r_y1": 127.822, "r_x2": 324.463, "r_y2": 118.60900000000004, "r_x3": 136.8, "r_y3": 118.60900000000004, "coord_origin": "TOPLEFT"}, "text": "information assets that must be described:", "orig": "information assets that must be described:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described:"}, {"label": "list_item", "id": 4, "page_no": 5, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 135.48699999999997, "r": 547.354, "b": 180.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9835494756698608, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 144.49699999999996, "r_x1": 141.78, "r_y1": 144.49699999999996, "r_x2": 141.78, "r_y2": 136.096, "r_x3": 136.8, "r_y3": 136.096, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 144.861, "r_x1": 406.686, "r_y1": 144.861, "r_x2": 406.686, "r_y2": 135.64800000000002, "r_x3": 151.2, "r_y3": 135.64800000000002, "coord_origin": "TOPLEFT"}, "text": "First, and most important, is the definition of a company's ", "orig": "First, and most important, is the definition of a company's ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 406.68, "r_y0": 145.07000000000005, "r_x1": 471.038, "r_y1": 145.07000000000005, "r_x2": 471.038, "r_y2": 135.48699999999997, "r_x3": 406.68, "r_y3": 135.48699999999997, "coord_origin": "TOPLEFT"}, "text": "security policy", "orig": "security policy", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 470.04, "r_y0": 144.86199999999997, "r_x1": 520.547, "r_y1": 144.86199999999997, "r_x2": 520.547, "r_y2": 135.649, "r_x3": 470.04, "r_y3": 135.649, "coord_origin": "TOPLEFT"}, "text": ". Without a ", "orig": ". Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 156.861, "r_x1": 547.354, "r_y1": 156.861, "r_x2": 547.354, "r_y2": 147.64800000000002, "r_x3": 151.199, "r_y3": 147.64800000000002, "coord_origin": "TOPLEFT"}, "text": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "orig": "security policy, there is no definition of what are acceptable practices for using, accessing, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 168.861, "r_x1": 531.029, "r_y1": 168.861, "r_x2": 531.029, "r_y2": 159.64800000000002, "r_x3": 151.199, "r_y3": 159.64800000000002, "coord_origin": "TOPLEFT"}, "text": "and storing information by who, what, when, where, and how. A security policy should ", "orig": "and storing information by who, what, when, where, and how. A security policy should ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 180.861, "r_x1": 463.329, "r_y1": 180.861, "r_x2": 463.329, "r_y2": 171.64800000000002, "r_x3": 151.199, "r_y3": 171.64800000000002, "coord_origin": "TOPLEFT"}, "text": "minimally address three things: confidentiality, integrity, and availability.", "orig": "minimally address three things: confidentiality, integrity, and availability.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability."}, {"label": "list_item", "id": 11, "page_no": 5, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 151.199, "t": 188.62800000000004, "r": 547.57, "b": 269.84000000000003, "coord_origin": "TOPLEFT"}, "confidence": 0.8077318072319031, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 197.841, "r_x1": 541.654, "r_y1": 197.841, "r_x2": 541.654, "r_y2": 188.62800000000004, "r_x3": 151.199, "r_y3": 188.62800000000004, "coord_origin": "TOPLEFT"}, "text": "The monitoring and assessment of adherence to the security policy determines whether ", "orig": "The monitoring and assessment of adherence to the security policy determines whether ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 209.841, "r_x1": 534.778, "r_y1": 209.841, "r_x2": 534.778, "r_y2": 200.62800000000004, "r_x3": 151.199, "r_y3": 200.62800000000004, "coord_origin": "TOPLEFT"}, "text": "your security strategy is working. Often, IBM security consultants are asked to perform ", "orig": "your security strategy is working. Often, IBM security consultants are asked to perform ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 221.84000000000003, "r_x1": 545.807, "r_y1": 221.84000000000003, "r_x2": 545.807, "r_y2": 212.62699999999995, "r_x3": 151.199, "r_y3": 212.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security assessments for companies without regard to the security policy. Although these ", "orig": "security assessments for companies without regard to the security policy. Although these ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 233.84000000000003, "r_x1": 547.57, "r_y1": 233.84000000000003, "r_x2": 547.57, "r_y2": 224.62699999999995, "r_x3": 151.199, "r_y3": 224.62699999999995, "coord_origin": "TOPLEFT"}, "text": "assessments can be useful for observing how the system is defined and how data is being ", "orig": "assessments can be useful for observing how the system is defined and how data is being ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 245.84000000000003, "r_x1": 543.924, "r_y1": 245.84000000000003, "r_x2": 543.924, "r_y2": 236.62699999999995, "r_x3": 151.199, "r_y3": 236.62699999999995, "coord_origin": "TOPLEFT"}, "text": "accessed, they cannot determine the level of security without a security policy. Without a ", "orig": "accessed, they cannot determine the level of security without a security policy. Without a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 257.84000000000003, "r_x1": 547.388, "r_y1": 257.84000000000003, "r_x2": 547.388, "r_y2": 248.62699999999995, "r_x3": 151.199, "r_y3": 248.62699999999995, "coord_origin": "TOPLEFT"}, "text": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "orig": "security policy, it really is not an assessment as much as it is a baseline for monitoring the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 269.84000000000003, "r_x1": 371.879, "r_y1": 269.84000000000003, "r_x2": 371.879, "r_y2": 260.62699999999995, "r_x3": 151.199, "r_y3": 260.62699999999995, "coord_origin": "TOPLEFT"}, "text": "changes in the security settings that are captured.", "orig": "changes in the security settings that are captured.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured."}, {"label": "text", "id": 12, "page_no": 5, "cluster": {"id": 12, "label": "text", "bbox": {"l": 151.199, "t": 277.60699999999997, "r": 542.001, "b": 286.82, "coord_origin": "TOPLEFT"}, "confidence": 0.7967224717140198, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 286.82, "r_x1": 542.001, "r_y1": 286.82, "r_x2": 542.001, "r_y2": 277.60699999999997, "r_x3": 151.199, "r_y3": 277.60699999999997, "coord_origin": "TOPLEFT"}, "text": "A security policy is what defines whether the system and its settings are secure (or not). ", "orig": "A security policy is what defines whether the system and its settings are secure (or not). ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A security policy is what defines whether the system and its settings are secure (or not)."}, {"label": "list_item", "id": 3, "page_no": 5, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.799, "t": 294.487, "r": 547.407, "b": 375.861, "coord_origin": "TOPLEFT"}, "confidence": 0.9837487936019897, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.799, "r_y0": 303.495, "r_x1": 141.779, "r_y1": 303.495, "r_x2": 141.779, "r_y2": 295.094, "r_x3": 136.799, "r_y3": 295.094, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.199, "r_y0": 303.859, "r_x1": 425.869, "r_y1": 303.859, "r_x2": 425.869, "r_y2": 294.646, "r_x3": 151.199, "r_y3": 294.646, "coord_origin": "TOPLEFT"}, "text": "The second fundamental in securing data assets is the use of ", "orig": "The second fundamental in securing data assets is the use of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 425.82, "r_y0": 304.07, "r_x1": 501.604, "r_y1": 304.07, "r_x2": 501.604, "r_y2": 294.487, "r_x3": 425.82, "r_y3": 294.487, "coord_origin": "TOPLEFT"}, "text": "resource security", "orig": "resource security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 500.64, "r_y0": 303.862, "r_x1": 514.508, "r_y1": 303.862, "r_x2": 514.508, "r_y2": 294.649, "r_x3": 500.64, "r_y3": 294.649, "coord_origin": "TOPLEFT"}, "text": ". If ", "orig": ". If ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.862, "r_x1": 537.883, "r_y1": 315.862, "r_x2": 537.883, "r_y2": 306.649, "r_x3": 151.2, "r_y3": 306.649, "coord_origin": "TOPLEFT"}, "text": "implemented properly, resource security prevents data breaches from both internal and ", "orig": "implemented properly, resource security prevents data breaches from both internal and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 327.861, "r_x1": 541.346, "r_y1": 327.861, "r_x2": 541.346, "r_y2": 318.648, "r_x3": 151.2, "r_y3": 318.648, "coord_origin": "TOPLEFT"}, "text": "external intrusions. Resource security controls are closely tied to the part of the security ", "orig": "external intrusions. Resource security controls are closely tied to the part of the security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 339.861, "r_x1": 547.407, "r_y1": 339.861, "r_x2": 547.407, "r_y2": 330.648, "r_x3": 151.2, "r_y3": 330.648, "coord_origin": "TOPLEFT"}, "text": "policy that defines who should have access to what information resources. A hacker might ", "orig": "policy that defines who should have access to what information resources. A hacker might ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 351.861, "r_x1": 534.87, "r_y1": 351.861, "r_x2": 534.87, "r_y2": 342.648, "r_x3": 151.2, "r_y3": 342.648, "coord_origin": "TOPLEFT"}, "text": "be good enough to get through your company firewalls and sift his way through to your ", "orig": "be good enough to get through your company firewalls and sift his way through to your ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 363.861, "r_x1": 517.016, "r_y1": 363.861, "r_x2": 517.016, "r_y2": 354.648, "r_x3": 151.2, "r_y3": 354.648, "coord_origin": "TOPLEFT"}, "text": "system, but if they do not have explicit access to your database, the hacker cannot ", "orig": "system, but if they do not have explicit access to your database, the hacker cannot ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 375.861, "r_x1": 314.04, "r_y1": 375.861, "r_x2": 314.04, "r_y2": 366.648, "r_x3": 151.2, "r_y3": 366.648, "coord_origin": "TOPLEFT"}, "text": "compromise your information assets.", "orig": "compromise your information assets.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets."}, {"label": "text", "id": 8, "page_no": 5, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 388.607, "r": 535.371, "b": 409.82, "coord_origin": "TOPLEFT"}, "confidence": 0.9621998071670532, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 397.82, "r_x1": 535.371, "r_y1": 397.82, "r_x2": 535.371, "r_y2": 388.607, "r_x3": 136.8, "r_y3": 388.607, "coord_origin": "TOPLEFT"}, "text": "With your eyes now open to the importance of securing information assets, the rest of this ", "orig": "With your eyes now open to the importance of securing information assets, the rest of this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 409.82, "r_x1": 532.764, "r_y1": 409.82, "r_x2": 532.764, "r_y2": 400.607, "r_x3": 136.8, "r_y3": 400.607, "coord_origin": "TOPLEFT"}, "text": "chapter reviews the methods that are available for securing database resources on IBM i. ", "orig": "chapter reviews the methods that are available for securing database resources on IBM i. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i."}, {"label": "section_header", "id": 7, "page_no": 5, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 438.301, "r": 323.37, "b": 453.064, "coord_origin": "TOPLEFT"}, "confidence": 0.9650285243988037, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 453.064, "r_x1": 323.37, "r_y1": 453.064, "r_x2": 323.37, "r_y2": 438.301, "r_x3": 64.8, "r_y3": 438.301, "coord_origin": "TOPLEFT"}, "text": "1.2 Current state of IBM i security", "orig": "1.2 Current state of IBM i security", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.2 Current state of IBM i security"}, {"label": "text", "id": 1, "page_no": 5, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 470.629, "r": 547.567, "b": 515.841, "coord_origin": "TOPLEFT"}, "confidence": 0.9848750233650208, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 479.842, "r_x1": 530.314, "r_y1": 479.842, "r_x2": 530.314, "r_y2": 470.629, "r_x3": 136.8, "r_y3": 470.629, "coord_origin": "TOPLEFT"}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "orig": "Because of the inherently secure nature of IBM i, many clients rely on the default system ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 491.842, "r_x1": 547.567, "r_y1": 491.842, "r_x2": 547.567, "r_y2": 482.629, "r_x3": 136.8, "r_y3": 482.629, "coord_origin": "TOPLEFT"}, "text": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "orig": "settings to protect their business data that is stored in DB2 for i. In most cases, this means no ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.841, "r_x1": 547.324, "r_y1": 503.841, "r_x2": 547.324, "r_y2": 494.628, "r_x3": 136.8, "r_y3": 494.628, "coord_origin": "TOPLEFT"}, "text": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "orig": "data protection because the default setting for the Create default public authority (QCRTAUT) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.841, "r_x1": 257.036, "r_y1": 515.841, "r_x2": 257.036, "r_y2": 506.628, "r_x3": 136.8, "r_y3": 506.628, "coord_origin": "TOPLEFT"}, "text": "system value is *CHANGE.", "orig": "system value is *CHANGE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE."}, {"label": "text", "id": 2, "page_no": 5, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 528.648, "r": 547.593, "b": 585.86, "coord_origin": "TOPLEFT"}, "confidence": 0.9848474860191345, "cells": [{"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.861, "r_x1": 513.913, "r_y1": 537.861, "r_x2": 513.913, "r_y2": 528.648, "r_x3": 136.8, "r_y3": 528.648, "coord_origin": "TOPLEFT"}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "orig": "Even more disturbing is that many IBM i clients remain in this state, despite the news ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 549.861, "r_x1": 547.593, "r_y1": 549.861, "r_x2": 547.593, "r_y2": 540.648, "r_x3": 136.8, "r_y3": 540.648, "coord_origin": "TOPLEFT"}, "text": "headlines and the significant costs that are involved with databases being compromised. This ", "orig": "headlines and the significant costs that are involved with databases being compromised. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 561.86, "r_x1": 546.284, "r_y1": 561.86, "r_x2": 546.284, "r_y2": 552.6469999999999, "r_x3": 136.8, "r_y3": 552.6469999999999, "coord_origin": "TOPLEFT"}, "text": "default security configuration makes it quite challenging to implement basic security policies. ", "orig": "default security configuration makes it quite challenging to implement basic security policies. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 573.86, "r_x1": 545.089, "r_y1": 573.86, "r_x2": 545.089, "r_y2": 564.6469999999999, "r_x3": 136.8, "r_y3": 564.6469999999999, "coord_origin": "TOPLEFT"}, "text": "A tighter implementation is required if you really want to protect one of your company's most ", "orig": "A tighter implementation is required if you really want to protect one of your company's most ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 585.86, "r_x1": 287.791, "r_y1": 585.86, "r_x2": 287.791, "r_y2": 576.6469999999999, "r_x3": 136.8, "r_y3": 576.6469999999999, "coord_origin": "TOPLEFT"}, "text": "valuable assets, which is the data.", "orig": "valuable assets, which is the data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data."}, {"label": "text", "id": 0, "page_no": 5, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 598.667, "r": 547.652, "b": 679.878, "coord_origin": "TOPLEFT"}, "confidence": 0.9870820045471191, "cells": [{"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 607.88, "r_x1": 547.652, "r_y1": 607.88, "r_x2": 547.652, "r_y2": 598.667, "r_x3": 136.8, "r_y3": 598.667, "coord_origin": "TOPLEFT"}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "orig": "Traditionally, IBM i applications have employed menu-based security to counteract this default ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 619.879, "r_x1": 538.686, "r_y1": 619.879, "r_x2": 538.686, "r_y2": 610.6659999999999, "r_x3": 136.8, "r_y3": 610.6659999999999, "coord_origin": "TOPLEFT"}, "text": "configuration that gives all users access to the data. The theory is that data is protected by ", "orig": "configuration that gives all users access to the data. The theory is that data is protected by ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 631.879, "r_x1": 520.363, "r_y1": 631.879, "r_x2": 520.363, "r_y2": 622.6659999999999, "r_x3": 136.8, "r_y3": 622.6659999999999, "coord_origin": "TOPLEFT"}, "text": "the menu options controlling what database operations that the user can perform. This ", "orig": "the menu options controlling what database operations that the user can perform. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 643.879, "r_x1": 502.78, "r_y1": 643.879, "r_x2": 502.78, "r_y2": 634.6659999999999, "r_x3": 136.8, "r_y3": 634.6659999999999, "coord_origin": "TOPLEFT"}, "text": "approach is ineffective, even if the user profile is restricted from running interactive ", "orig": "approach is ineffective, even if the user profile is restricted from running interactive ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 655.879, "r_x1": 545.174, "r_y1": 655.879, "r_x2": 545.174, "r_y2": 646.6659999999999, "r_x3": 136.8, "r_y3": 646.6659999999999, "coord_origin": "TOPLEFT"}, "text": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "orig": "commands. The reason is that in today's connected world there are a multitude of interfaces ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 667.879, "r_x1": 547.302, "r_y1": 667.879, "r_x2": 547.302, "r_y2": 658.6659999999999, "r_x3": 136.8, "r_y3": 658.6659999999999, "coord_origin": "TOPLEFT"}, "text": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "orig": "into the system, from web browsers to PC clients, that bypass application menus. If there are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 679.878, "r_x1": 526.009, "r_y1": 679.878, "r_x2": 526.009, "r_y2": 670.665, "r_x3": 136.8, "r_y3": 670.665, "coord_origin": "TOPLEFT"}, "text": "no object-level controls, users of these newer interfaces have an open door to your data.", "orig": "no object-level controls, users of these newer interfaces have an open door to your data.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data."}], "headers": [{"label": "page_footer", "id": 10, "page_no": 5, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8889444470405579, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "2 ", "orig": "2 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2"}, {"label": "page_footer", "id": 9, "page_no": 5, "cluster": {"id": 9, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9476422667503357, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 6, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 544.312, "b": 152.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 173.33500000000004, "r": 301.46, "b": 185.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 199.48900000000003, "r": 541.576, "b": 256.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 547.69, "b": 314.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 327.527, "r": 547.541, "b": 360.739, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.462, "b": 700.143, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 10, "label": "text", "bbox": {"l": 180.959, "t": 383.25, "r": 212.54, "b": 389.388, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 170.006, "t": 390.746, "r": 226.926, "b": 396.884, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 6, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 544.312, "b": 152.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 173.33500000000004, "r": 301.46, "b": 185.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 199.48900000000003, "r": 541.576, "b": 256.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 547.69, "b": 314.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 327.527, "r": 547.541, "b": 360.739, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.462, "b": 700.143, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, "coord_origin": "TOPLEFT"}, "confidence": 0.9838992357254028, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 10, "label": "text", "bbox": {"l": 180.959, "t": 383.25, "r": 212.54, "b": 389.388, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.959, "r_y0": 389.388, "r_x1": 212.54, "r_y1": 389.388, "r_x2": 212.54, "r_y2": 383.25, "r_x3": 180.959, "r_y3": 383.25, "coord_origin": "TOPLEFT"}, "text": "User with ", "orig": "User with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 170.006, "t": 390.746, "r": 226.926, "b": 396.884, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 170.006, "r_y0": 396.884, "r_x1": 226.926, "r_y1": 396.884, "r_x2": 226.926, "r_y2": 390.746, "r_x3": 170.006, "r_y3": 390.746, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access ", "orig": "*ALLOBJ access ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "text", "id": 1, "page_no": 6, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 544.312, "b": 152.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9872740507125854, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 541.139, "r_y1": 80.72199999999998, "r_x2": 541.139, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "orig": "Many businesses are trying to limit data access to a need-to-know basis. This security goal ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 544.312, "r_y1": 92.72199999999998, "r_x2": 544.312, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "means that users should be given access only to the minimum set of data that is required to ", "orig": "means that users should be given access only to the minimum set of data that is required to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.721, "r_x1": 540.952, "r_y1": 104.721, "r_x2": 540.952, "r_y2": 95.50800000000004, "r_x3": 136.8, "r_y3": 95.50800000000004, "coord_origin": "TOPLEFT"}, "text": "perform their job. Often, users with object-level access are given access to row and column ", "orig": "perform their job. Often, users with object-level access are given access to row and column ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 538.284, "r_y1": 116.721, "r_x2": 538.284, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "values that are beyond what their business task requires because that object-level security ", "orig": "values that are beyond what their business task requires because that object-level security ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 530.239, "r_y1": 128.721, "r_x2": 530.239, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "orig": "provides an all-or-nothing solution. For example, object-level controls allow a manager to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 536.272, "r_y1": 140.721, "r_x2": 536.272, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "access data about all employees. Most security policies limit a manager to accessing data ", "orig": "access data about all employees. Most security policies limit a manager to accessing data ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.721, "r_x1": 319.007, "r_y1": 152.721, "r_x2": 319.007, "r_y2": 143.50800000000004, "r_x3": 136.8, "r_y3": 143.50800000000004, "coord_origin": "TOPLEFT"}, "text": "only for the employees that they manage.", "orig": "only for the employees that they manage.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage."}, {"label": "section_header", "id": 5, "page_no": 6, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 173.33500000000004, "r": 301.46, "b": 185.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9626136422157288, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 185.32299999999998, "r_x1": 301.46, "r_y1": 185.32299999999998, "r_x2": 301.46, "r_y2": 173.33500000000004, "r_x3": 64.8, "r_y3": 173.33500000000004, "coord_origin": "TOPLEFT"}, "text": "1.3.1 Existing row and column control", "orig": "1.3.1 Existing row and column control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1.3.1 Existing row and column control"}, {"label": "text", "id": 2, "page_no": 6, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 199.48900000000003, "r": 541.576, "b": 256.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9858147501945496, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 208.702, "r_x1": 534.91, "r_y1": 208.702, "r_x2": 534.91, "r_y2": 199.48900000000003, "r_x3": 136.8, "r_y3": 199.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "orig": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.702, "r_x1": 480.482, "r_y1": 220.702, "r_x2": 480.482, "r_y2": 211.48900000000003, "r_x3": 136.8, "r_y3": 211.48900000000003, "coord_origin": "TOPLEFT"}, "text": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "orig": "views (or logical files) and application logic, as shown in Figure 1-2. However, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70100000000002, "r_x1": 541.576, "r_y1": 232.70100000000002, "r_x2": 541.576, "r_y2": 223.48800000000006, "r_x3": 136.8, "r_y3": 223.48800000000006, "coord_origin": "TOPLEFT"}, "text": "application-based logic is easy to bypass with all of the different data access interfaces that ", "orig": "application-based logic is easy to bypass with all of the different data access interfaces that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 244.70100000000002, "r_x1": 537.403, "r_y1": 244.70100000000002, "r_x2": 537.403, "r_y2": 235.48800000000006, "r_x3": 136.8, "r_y3": 235.48800000000006, "coord_origin": "TOPLEFT"}, "text": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "orig": "are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 256.701, "r_x1": 242.216, "r_y1": 256.701, "r_x2": 242.216, "r_y2": 247.48800000000006, "r_x3": 136.8, "r_y3": 247.48800000000006, "coord_origin": "TOPLEFT"}, "text": "and System i Navigator.", "orig": "and System i Navigator.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator."}, {"label": "text", "id": 0, "page_no": 6, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 547.69, "b": 314.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9875384569168091, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 526.893, "r_y1": 278.721, "r_x2": 526.893, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "orig": "Using SQL views to limit access to a subset of the data in a table also has its own set of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 290.72, "r_x1": 531.78, "r_y1": 290.72, "r_x2": 531.78, "r_y2": 281.507, "r_x3": 136.8, "r_y3": 281.507, "coord_origin": "TOPLEFT"}, "text": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "orig": "challenges. First, there is the complexity of managing all of the SQL view objects that are ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 302.72, "r_x1": 547.69, "r_y1": 302.72, "r_x2": 547.69, "r_y2": 293.507, "r_x3": 136.8, "r_y3": 293.507, "coord_origin": "TOPLEFT"}, "text": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "orig": "used for securing data access. Second, scaling a view-based security solution can be difficult ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 314.72, "r_x1": 421.829, "r_y1": 314.72, "r_x2": 421.829, "r_y2": 305.507, "r_x3": 136.8, "r_y3": 305.507, "coord_origin": "TOPLEFT"}, "text": "as the amount of data grows and the number of users increases.", "orig": "as the amount of data grows and the number of users increases.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases."}, {"label": "text", "id": 4, "page_no": 6, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 327.527, "r": 547.541, "b": 360.739, "coord_origin": "TOPLEFT"}, "confidence": 0.975760281085968, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.74, "r_x1": 536.476, "r_y1": 336.74, "r_x2": 536.476, "r_y2": 327.527, "r_x3": 136.8, "r_y3": 327.527, "coord_origin": "TOPLEFT"}, "text": "Even if you are willing to live with these performance and management issues, a user with ", "orig": "Even if you are willing to live with these performance and management issues, a user with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 348.739, "r_x1": 547.541, "r_y1": 348.739, "r_x2": 547.541, "r_y2": 339.526, "r_x3": 136.8, "r_y3": 339.526, "coord_origin": "TOPLEFT"}, "text": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "orig": "*ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 360.739, "r_x1": 397.891, "r_y1": 360.739, "r_x2": 397.891, "r_y2": 351.526, "r_x3": 136.8, "r_y3": 351.526, "coord_origin": "TOPLEFT"}, "text": "bypass the security controls that are built into an SQL view.", "orig": "bypass the security controls that are built into an SQL view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view."}, {"label": "caption", "id": 7, "page_no": 6, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 691.818, "r": 316.462, "b": 700.143, "coord_origin": "TOPLEFT"}, "confidence": 0.9457826614379883, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 700.143, "r_x1": 316.462, "r_y1": 700.143, "r_x2": 316.462, "r_y2": 691.818, "r_x3": 136.8, "r_y3": 691.818, "coord_origin": "TOPLEFT"}, "text": "Figure 1-2 Existing row and column controls", "orig": "Figure 1-2 Existing row and column controls", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 1-2 Existing row and column controls"}, {"label": "picture", "id": 3, "page_no": 6, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 135.92466735839844, "t": 375.9272155761719, "r": 546.4456176757812, "b": 688.6098022460938, 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"cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 72.822, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8578535318374634, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 72.822, "r_y1": 763.863, "r_x2": 72.822, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "4 ", "orig": "4 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "4"}, {"label": "page_footer", "id": 6, "page_no": 6, "cluster": {"id": 6, "label": "page_footer", "bbox": {"l": 87.84, "t": 755.538, "r": 328.556, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9491620659828186, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 87.84, "r_y0": 763.863, "r_x1": 328.556, "r_y1": 763.863, "r_x2": 328.556, "r_y2": 755.538, "r_x3": 87.84, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 7, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 123.74099999999999, "r_x1": 253.222, "r_y1": 123.74099999999999, "r_x2": 253.222, "r_y2": 114.52800000000002, "r_x3": 151.2, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Work Function Usage (", "orig": "Work Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.26, "r_y0": 123.42399999999998, "r_x1": 298.2, "r_y1": 123.42399999999998, "r_x2": 298.2, "r_y2": 114.976, "r_x3": 253.26, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "WRKFCNUSG", "orig": "WRKFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 298.201, "r_y0": 123.74099999999999, "r_x1": 301.517, "r_y1": 123.74099999999999, "r_x2": 301.517, "r_y2": 114.52800000000002, "r_x3": 298.201, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 135.74099999999999, "r_x1": 265.099, "r_y1": 135.74099999999999, "r_x2": 265.099, "r_y2": 126.52800000000002, "r_x3": 151.201, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Change Function Usage (", "orig": "Change Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.081, "r_y0": 135.42399999999998, "r_x1": 310.02, "r_y1": 135.42399999999998, "r_x2": 310.02, "r_y2": 126.976, "r_x3": 265.081, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.081, "r_y0": 135.74099999999999, "r_x1": 313.398, "r_y1": 135.74099999999999, "r_x2": 313.398, "r_y2": 126.52800000000002, "r_x3": 310.081, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 147.74099999999999, "r_x1": 262.535, "r_y1": 147.74099999999999, "r_x2": 262.535, "r_y2": 138.52800000000002, "r_x3": 151.201, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Display Function Usage (", "orig": "Display Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.561, "r_y0": 147.42399999999998, "r_x1": 307.5, "r_y1": 147.42399999999998, "r_x2": 307.5, "r_y2": 138.976, "r_x3": 262.561, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "DSPFCNUSG", "orig": "DSPFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 307.5, "r_y0": 147.74099999999999, "r_x1": 310.817, "r_y1": 147.74099999999999, "r_x2": 310.817, "r_y2": 138.52800000000002, "r_x3": 307.5, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 673.762, "r_x1": 547.592, "r_y1": 673.762, "r_x2": 547.592, "r_y2": 664.549, "r_x3": 136.8, "r_y3": 664.549, "coord_origin": "TOPLEFT"}, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 697.761, "r_x1": 544.288, "r_y1": 697.761, "r_x2": 544.288, "r_y2": 688.548, "r_x3": 136.8, "r_y3": 688.548, "coord_origin": "TOPLEFT"}, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "orig": "administrative functions to be divided across individuals without overlapping responsibilities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 709.761, "r_x1": 536.25, "r_y1": 709.761, "r_x2": 536.25, "r_y2": 700.548, "r_x3": 136.8, "r_y3": 700.548, "coord_origin": "TOPLEFT"}, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "orig": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 304.863, "r_x1": 202.253, "r_y1": 304.863, "r_x2": 202.253, "r_y2": 296.538, "r_x3": 142.8, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Column name", "orig": "Column name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.809, "r_y0": 304.863, "r_x1": 257.219, "r_y1": 304.863, "r_x2": 257.219, "r_y2": 296.538, "r_x3": 216.809, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Data type", "orig": "Data type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.475, "r_y0": 304.863, "r_x1": 338.903, "r_y1": 304.863, "r_x2": 338.903, "r_y2": 296.538, "r_x3": 289.475, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Description", "orig": "Description", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 323.883, "r_x1": 203.243, "r_y1": 323.883, "r_x2": 203.243, "r_y2": 315.558, "r_x3": 142.8, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "FUNCTION_ID", "orig": "FUNCTION_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.785, "r_y0": 323.883, "r_x1": 276.014, "r_y1": 323.883, "r_x2": 276.014, "r_y2": 315.558, "r_x3": 216.785, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(30)", "orig": "VARCHAR(30)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.458, "r_y0": 323.883, "r_x1": 359.865, "r_y1": 323.883, "r_x2": 359.865, "r_y2": 315.558, "r_x3": 289.458, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "ID of the function.", "orig": "ID of the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 342.843, "r_x1": 198.685, "r_y1": 342.843, "r_x2": 198.685, "r_y2": 334.518, "r_x3": 142.8, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "USER_NAME", "orig": "USER_NAME", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.741, "r_y0": 342.843, "r_x1": 275.939, "r_y1": 342.843, "r_x2": 275.939, "r_y2": 334.518, "r_x3": 216.741, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(10)", "orig": "VARCHAR(10)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.382, "r_y0": 342.843, "r_x1": 515.035, "r_y1": 342.843, "r_x2": 515.035, "r_y2": 334.518, "r_x3": 289.382, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "Name of the user profile that has a usage setting for this ", "orig": "Name of the user profile that has a usage setting for this ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 353.883, "r_x1": 323.439, "r_y1": 353.883, "r_x2": 323.439, "r_y2": 345.558, "r_x3": 289.44, "r_y3": 345.558, "coord_origin": "TOPLEFT"}, "text": "function.", "orig": "function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 372.844, "r_x1": 173.997, "r_y1": 372.844, "r_x2": 173.997, "r_y2": 364.519, "r_x3": 142.8, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "USAGE", "orig": "USAGE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.774, "r_y0": 372.844, "r_x1": 270.993, "r_y1": 372.844, "r_x2": 270.993, "r_y2": 364.519, "r_x3": 216.774, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(7)", "orig": "VARCHAR(7)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.416, "r_y0": 372.844, "r_x1": 346.901, "r_y1": 372.844, "r_x2": 346.901, "r_y2": 364.519, "r_x3": 289.416, "r_y3": 364.519, "coord_origin": "TOPLEFT"}, "text": "Usage setting:", "orig": "Usage setting:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 383.555, "r_x1": 293.94, "r_y1": 383.555, "r_x2": 293.94, "r_y2": 375.964, "r_x3": 289.44, "r_y3": 375.964, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 383.884, "r_x1": 535.18, "r_y1": 383.884, "r_x2": 535.18, "r_y2": 375.559, "r_x3": 303.84, "r_y3": 375.559, "coord_origin": "TOPLEFT"}, "text": "ALLOWED: The user profile is allowed to use the function.", "orig": "ALLOWED: The user profile is allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 394.535, "r_x1": 293.94, "r_y1": 394.535, "r_x2": 293.94, "r_y2": 386.944, "r_x3": 289.44, "r_y3": 386.944, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 57, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 394.864, "r_x1": 539.121, "r_y1": 394.864, "r_x2": 539.121, "r_y2": 386.539, "r_x3": 303.84, "r_y3": 386.539, "coord_origin": "TOPLEFT"}, "text": "DENIED: The user profile is not allowed to use the function.", "orig": "DENIED: The user profile is not allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 58, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 413.884, "r_x1": 196.235, "r_y1": 413.884, "r_x2": 196.235, "r_y2": 405.559, "r_x3": 142.8, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "USER_TYPE", "orig": "USER_TYPE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 59, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.752, "r_y0": 413.884, "r_x1": 271.009, "r_y1": 413.884, "r_x2": 271.009, "r_y2": 405.559, "r_x3": 216.752, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(5)", "orig": "VARCHAR(5)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 60, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.432, "r_y0": 413.884, "r_x1": 367.811, "r_y1": 413.884, "r_x2": 367.811, "r_y2": 405.559, "r_x3": 289.432, "r_y3": 405.559, "coord_origin": "TOPLEFT"}, "text": "Type of user profile:", "orig": "Type of user profile:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 61, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 424.534, "r_x1": 293.94, "r_y1": 424.534, "r_x2": 293.94, "r_y2": 416.944, "r_x3": 289.44, "r_y3": 416.944, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 62, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 424.864, "r_x1": 434.795, "r_y1": 424.864, "r_x2": 434.795, "r_y2": 416.539, "r_x3": 303.84, "r_y3": 416.539, "coord_origin": "TOPLEFT"}, "text": "USER: The user profile is a user.", "orig": "USER: The user profile is a user.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 63, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 435.514, "r_x1": 293.94, "r_y1": 435.514, "r_x2": 293.94, "r_y2": 427.924, "r_x3": 289.44, "r_y3": 427.924, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 435.844, "r_x1": 448.132, "r_y1": 435.844, "r_x2": 448.132, "r_y2": 427.519, "r_x3": 303.84, "r_y3": 427.519, "coord_origin": "TOPLEFT"}, "text": "GROUP: The user profile is a group.", "orig": "GROUP: The user profile is a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52800000000002, "r": 301.517, "b": 123.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 123.74099999999999, "r_x1": 253.222, "r_y1": 123.74099999999999, "r_x2": 253.222, "r_y2": 114.52800000000002, "r_x3": 151.2, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Work Function Usage (", "orig": "Work Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.26, "r_y0": 123.42399999999998, "r_x1": 298.2, "r_y1": 123.42399999999998, "r_x2": 298.2, "r_y2": 114.976, "r_x3": 253.26, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "WRKFCNUSG", "orig": "WRKFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 298.201, "r_y0": 123.74099999999999, "r_x1": 301.517, "r_y1": 123.74099999999999, "r_x2": 301.517, "r_y2": 114.52800000000002, "r_x3": 298.201, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.52800000000002, "r": 313.398, "b": 135.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 135.74099999999999, "r_x1": 265.099, "r_y1": 135.74099999999999, "r_x2": 265.099, "r_y2": 126.52800000000002, "r_x3": 151.201, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Change Function Usage (", "orig": "Change Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.081, "r_y0": 135.42399999999998, "r_x1": 310.02, "r_y1": 135.42399999999998, "r_x2": 310.02, "r_y2": 126.976, "r_x3": 265.081, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.081, "r_y0": 135.74099999999999, "r_x1": 313.398, "r_y1": 135.74099999999999, "r_x2": 313.398, "r_y2": 126.52800000000002, "r_x3": 310.081, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.52800000000002, "r": 310.817, "b": 147.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 147.74099999999999, "r_x1": 262.535, "r_y1": 147.74099999999999, "r_x2": 262.535, "r_y2": 138.52800000000002, "r_x3": 151.201, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Display Function Usage (", "orig": "Display Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.561, "r_y0": 147.42399999999998, "r_x1": 307.5, "r_y1": 147.42399999999998, "r_x2": 307.5, "r_y2": 138.976, "r_x3": 262.561, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "DSPFCNUSG", "orig": "DSPFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 307.5, "r_y0": 147.74099999999999, "r_x1": 310.817, "r_y1": 147.74099999999999, "r_x2": 310.817, "r_y2": 138.52800000000002, "r_x3": 307.5, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 512.547, "b": 181.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 266.722, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 473.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.8, "t": 515.957, "r": 241.739, "b": 524.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 136.8, "t": 527.956, "r": 221.699, "b": 536.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 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Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 697.761, "r_x1": 544.288, "r_y1": 697.761, "r_x2": 544.288, "r_y2": 688.548, "r_x3": 136.8, "r_y3": 688.548, "coord_origin": "TOPLEFT"}, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "orig": "administrative functions to be divided across individuals without overlapping responsibilities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 709.761, "r_x1": 536.25, "r_y1": 709.761, "r_x2": 536.25, "r_y2": 700.548, "r_x3": 136.8, "r_y3": 700.548, "coord_origin": "TOPLEFT"}, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "orig": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "table", "bbox": {"l": 135.52462768554688, "t": 289.7252197265625, "r": 545.8714599609375, "b": 442.050537109375, "coord_origin": "TOPLEFT"}, "confidence": 0.9851690530776978, "cells": [{"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 304.863, "r_x1": 202.253, "r_y1": 304.863, "r_x2": 202.253, "r_y2": 296.538, "r_x3": 142.8, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Column name", "orig": "Column name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.809, "r_y0": 304.863, "r_x1": 257.219, "r_y1": 304.863, "r_x2": 257.219, "r_y2": 296.538, "r_x3": 216.809, "r_y3": 296.538, "coord_origin": "TOPLEFT"}, "text": "Data type", "orig": "Data type", "text_direction": 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"VARCHAR(30)", "orig": "VARCHAR(30)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.458, "r_y0": 323.883, "r_x1": 359.865, "r_y1": 323.883, "r_x2": 359.865, "r_y2": 315.558, "r_x3": 289.458, "r_y3": 315.558, "coord_origin": "TOPLEFT"}, "text": "ID of the function.", "orig": "ID of the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 342.843, "r_x1": 198.685, "r_y1": 342.843, "r_x2": 198.685, "r_y2": 334.518, "r_x3": 142.8, "r_y3": 334.518, "coord_origin": "TOPLEFT"}, "text": "USER_NAME", "orig": "USER_NAME", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 216.741, "r_y0": 342.843, "r_x1": 275.939, "r_y1": 342.843, "r_x2": 275.939, "r_y2": 334.518, "r_x3": 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"from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 383.555, "r_x1": 293.94, "r_y1": 383.555, "r_x2": 293.94, "r_y2": 375.964, "r_x3": 289.44, "r_y3": 375.964, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 55, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 383.884, "r_x1": 535.18, "r_y1": 383.884, "r_x2": 535.18, "r_y2": 375.559, "r_x3": 303.84, "r_y3": 375.559, "coord_origin": "TOPLEFT"}, "text": "ALLOWED: The user profile is allowed to use the function.", "orig": "ALLOWED: The user profile is allowed to use the function.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 56, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 289.44, "r_y0": 394.535, "r_x1": 293.94, "r_y1": 394.535, "r_x2": 293.94, "r_y2": 386.944, "r_x3": 289.44, "r_y3": 386.944, 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435.514, "r_x1": 293.94, "r_y1": 435.514, "r_x2": 293.94, "r_y2": 427.924, "r_x3": 289.44, "r_y3": 427.924, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 63, "label": "text", "bbox": {"l": 303.84, "t": 427.519, "r": 448.132, "b": 435.844, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 64, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 303.84, "r_y0": 435.844, "r_x1": 448.132, "r_y1": 435.844, "r_x2": 448.132, "r_y2": 427.519, "r_x3": 303.84, "r_y3": 427.519, "coord_origin": "TOPLEFT"}, "text": "GROUP: The user profile is a group.", "orig": "GROUP: The user profile is a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "ched", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl", "fcel", "fcel", "fcel", "nl"], "num_rows": 5, "num_cols": 3, "table_cells": [{"bbox": {"l": 142.8, "t": 296.538, "r": 202.253, "b": 304.863, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Column name", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 216.809, "t": 296.538, "r": 257.219, "b": 304.863, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Data type", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 289.475, "t": 296.538, "r": 338.903, "b": 304.863, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Description", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 315.558, "r": 203.243, "b": 323.883, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "FUNCTION_ID", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.785, "t": 315.558, "r": 276.014, "b": 323.883, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(30)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.458, "t": 315.558, "r": 359.865, "b": 323.883, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "ID of the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 334.518, "r": 198.685, "b": 342.843, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_NAME", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.741, "t": 334.518, "r": 275.939, "b": 342.843, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(10)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.382, "t": 334.518, "r": 515.035, "b": 353.883, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Name of the user profile that has a usage setting for this function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 364.519, "r": 173.997, "b": 372.844, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USAGE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.774, "t": 364.519, "r": 270.993, "b": 372.844, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(7)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.416, "t": 364.519, "r": 539.121, "b": 394.864, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 3, "end_row_offset_idx": 4, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 405.559, "r": 196.235, "b": 413.884, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER_TYPE", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 216.752, "t": 405.559, "r": 271.009, "b": 413.884, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "VARCHAR(5)", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 289.432, "t": 405.559, "r": 448.132, "b": 435.844, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 4, "end_row_offset_idx": 5, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group.", "column_header": false, "row_header": false, "row_section": false}]}}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 14, "page_no": 7, "cluster": {"id": 14, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9166396856307983, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "10 ", "orig": "10 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "section_header", "id": 3, "page_no": 7, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 71.33500000000004, "r": 335.493, "b": 83.32299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9697521328926086, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 83.32299999999998, "r_x1": 335.493, "r_y1": 83.32299999999998, "r_x2": 335.493, "r_y2": 71.33500000000004, "r_x3": 64.8, "r_y3": 71.33500000000004, "coord_origin": "TOPLEFT"}, "text": "2.1.6 Change Function Usage CL command", "orig": "2.1.6 Change Function Usage CL command", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.6 Change Function Usage CL command"}, {"label": "text", "id": 9, "page_no": 7, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 97.48900000000003, "r": 547.251, "b": 106.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9551582932472229, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 106.702, "r_x1": 547.251, "r_y1": 106.702, "r_x2": 547.251, "r_y2": 97.48900000000003, "r_x3": 136.8, "r_y3": 97.48900000000003, "coord_origin": "TOPLEFT"}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:", "orig": "The following CL commands can be used to work with, display, or change function usage IDs:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The following CL commands can be used to work with, display, or change function usage IDs:"}, {"label": "list_item", "id": 12, "page_no": 7, "cluster": {"id": 12, "label": "list_item", "bbox": {"l": 136.8, "t": 114.52800000000002, "r": 301.517, "b": 123.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9410832524299622, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 123.37699999999995, "r_x1": 141.78, "r_y1": 123.37699999999995, "r_x2": 141.78, "r_y2": 114.976, "r_x3": 136.8, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 123.74099999999999, "r_x1": 253.222, "r_y1": 123.74099999999999, "r_x2": 253.222, "r_y2": 114.52800000000002, "r_x3": 151.2, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Work Function Usage (", "orig": "Work Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 253.26, "r_y0": 123.42399999999998, "r_x1": 298.2, "r_y1": 123.42399999999998, "r_x2": 298.2, "r_y2": 114.976, "r_x3": 253.26, "r_y3": 114.976, "coord_origin": "TOPLEFT"}, "text": "WRKFCNUSG", "orig": "WRKFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 298.201, "r_y0": 123.74099999999999, "r_x1": 301.517, "r_y1": 123.74099999999999, "r_x2": 301.517, "r_y2": 114.52800000000002, "r_x3": 298.201, "r_y3": 114.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 Work Function Usage ( WRKFCNUSG )"}, {"label": "list_item", "id": 11, "page_no": 7, "cluster": {"id": 11, "label": "list_item", "bbox": {"l": 136.801, "t": 126.52800000000002, "r": 313.398, "b": 135.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9418092370033264, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 135.37699999999995, "r_x1": 141.781, "r_y1": 135.37699999999995, "r_x2": 141.781, "r_y2": 126.976, "r_x3": 136.801, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 135.74099999999999, "r_x1": 265.099, "r_y1": 135.74099999999999, "r_x2": 265.099, "r_y2": 126.52800000000002, "r_x3": 151.201, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Change Function Usage (", "orig": "Change Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 265.081, "r_y0": 135.42399999999998, "r_x1": 310.02, "r_y1": 135.42399999999998, "r_x2": 310.02, "r_y2": 126.976, "r_x3": 265.081, "r_y3": 126.976, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.081, "r_y0": 135.74099999999999, "r_x1": 313.398, "r_y1": 135.74099999999999, "r_x2": 313.398, "r_y2": 126.52800000000002, "r_x3": 310.081, "r_y3": 126.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 Change Function Usage ( CHGFCNUSG )"}, {"label": "list_item", "id": 8, "page_no": 7, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.801, "t": 138.52800000000002, "r": 310.817, "b": 147.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9562349915504456, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 147.37599999999998, "r_x1": 141.781, "r_y1": 147.37599999999998, "r_x2": 141.781, "r_y2": 138.976, "r_x3": 136.801, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 147.74099999999999, "r_x1": 262.535, "r_y1": 147.74099999999999, "r_x2": 262.535, "r_y2": 138.52800000000002, "r_x3": 151.201, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": "Display Function Usage (", "orig": "Display Function Usage (", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 262.561, "r_y0": 147.42399999999998, "r_x1": 307.5, "r_y1": 147.42399999999998, "r_x2": 307.5, "r_y2": 138.976, "r_x3": 262.561, "r_y3": 138.976, "coord_origin": "TOPLEFT"}, "text": "DSPFCNUSG", "orig": "DSPFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 307.5, "r_y0": 147.74099999999999, "r_x1": 310.817, "r_y1": 147.74099999999999, "r_x2": 310.817, "r_y2": 138.52800000000002, "r_x3": 307.5, "r_y3": 138.52800000000002, "coord_origin": "TOPLEFT"}, "text": ")", "orig": ")", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 Display Function Usage ( DSPFCNUSG )"}, {"label": "text", "id": 4, "page_no": 7, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 160.48800000000006, "r": 512.547, "b": 181.70100000000002, "coord_origin": "TOPLEFT"}, "confidence": 0.9682500958442688, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 169.70100000000002, "r_x1": 255.049, "r_y1": 169.70100000000002, "r_x2": 255.049, "r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 266.722, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 473.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.8, "t": 515.957, "r": 241.739, "b": 524.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 136.8, "t": 527.956, "r": 221.699, "b": 536.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 547.592, "b": 709.761, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 673.762, "r_x1": 547.592, "r_y1": 673.762, "r_x2": 547.592, "r_y2": 664.549, "r_x3": 136.8, "r_y3": 664.549, "coord_origin": "TOPLEFT"}, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 697.761, "r_x1": 544.288, "r_y1": 697.761, "r_x2": 544.288, "r_y2": 688.548, "r_x3": 136.8, "r_y3": 688.548, "coord_origin": "TOPLEFT"}, "text": "administrative functions to be divided across individuals without overlapping responsibilities, ", "orig": "administrative functions to be divided across individuals without overlapping responsibilities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 709.761, "r_x1": 536.25, "r_y1": 709.761, "r_x2": 536.25, "r_y2": 700.548, "r_x3": 136.8, "r_y3": 700.548, "coord_origin": "TOPLEFT"}, "text": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "orig": "so that one user does not possess unlimited authority, such as with the *ALLOBJ authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. 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"r_y2": 160.48800000000006, "r_x3": 136.8, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": "For example, the following ", "orig": "For example, the following ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 255.0, "r_y0": 169.38400000000001, "r_x1": 300.0, "r_y1": 169.38400000000001, "r_x2": 300.0, "r_y2": 160.93600000000004, "r_x3": 255.0, "r_y3": 160.93600000000004, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG", "orig": "CHGFCNUSG", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 300.001, "r_y0": 169.70100000000002, "r_x1": 512.547, "r_y1": 169.70100000000002, "r_x2": 512.547, "r_y2": 160.48800000000006, "r_x3": 300.001, "r_y3": 160.48800000000006, "coord_origin": "TOPLEFT"}, "text": " command shows granting authorization to user ", "orig": " command shows granting authorization to user ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 181.70100000000002, "r_x1": 360.391, "r_y1": 181.70100000000002, "r_x2": 360.391, "r_y2": 172.48800000000006, "r_x3": 136.801, "r_y3": 172.48800000000006, "coord_origin": "TOPLEFT"}, "text": "HBEDOYA to administer and manage RCAC rules:", "orig": "HBEDOYA to administer and manage RCAC rules:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules:"}, {"label": "text", "id": 15, "page_no": 7, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.801, "t": 189.97500000000002, "r": 441.597, "b": 198.37599999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.905712902545929, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 198.37599999999998, "r_x1": 441.597, "r_y1": 198.37599999999998, "r_x2": 441.597, "r_y2": 189.97500000000002, "r_x3": 136.801, "r_y3": 189.97500000000002, "coord_origin": "TOPLEFT"}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "orig": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED)"}, {"label": "section_header", "id": 7, "page_no": 7, "cluster": {"id": 7, "label": "section_header", "bbox": {"l": 64.8, "t": 219.35500000000002, "r": 544.461, "b": 231.34299999999996, "coord_origin": "TOPLEFT"}, "confidence": 0.9636710286140442, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 231.34299999999996, "r_x1": 544.461, "r_y1": 231.34299999999996, "r_x2": 544.461, "r_y2": 219.35500000000002, "r_x3": 64.8, "r_y3": 219.35500000000002, "coord_origin": "TOPLEFT"}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "orig": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view"}, {"label": "text", "id": 2, "page_no": 7, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 245.50900000000001, "r": 519.527, "b": 266.722, "coord_origin": "TOPLEFT"}, "confidence": 0.9737311601638794, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.72199999999998, "r_x1": 519.527, "r_y1": 254.72199999999998, "r_x2": 519.527, "r_y2": 245.50900000000001, "r_x3": 136.8, "r_y3": 245.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "orig": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.722, "r_x1": 382.936, "r_y1": 266.722, "r_x2": 382.936, "r_y2": 257.509, "r_x3": 136.8, "r_y3": 257.509, "coord_origin": "TOPLEFT"}, "text": "describes the columns in the FUNCTION_USAGE view.", "orig": "describes the columns in the FUNCTION_USAGE view.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view."}, {"label": "caption", "id": 13, "page_no": 7, "cluster": {"id": 13, "label": "caption", "bbox": {"l": 136.8, "t": 279.558, "r": 283.949, "b": 287.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9196999669075012, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 287.883, "r_x1": 283.949, "r_y1": 287.883, "r_x2": 283.949, "r_y2": 279.558, "r_x3": 136.8, "r_y3": 279.558, "coord_origin": "TOPLEFT"}, "text": "Table 2-1 FUNCTION_USAGE view", "orig": "Table 2-1 FUNCTION_USAGE view", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-1 FUNCTION_USAGE view"}, {"label": "text", "id": 6, "page_no": 7, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 452.509, "r": 547.589, "b": 473.722, "coord_origin": "TOPLEFT"}, "confidence": 0.964775562286377, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 461.722, "r_x1": 547.589, "r_y1": 461.722, "r_x2": 547.589, "r_y2": 452.509, "r_x3": 136.8, "r_y3": 452.509, "coord_origin": "TOPLEFT"}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "orig": "To discover who has authorization to define and manage RCAC, you can use the query that is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 473.722, "r_x1": 237.742, "r_y1": 473.722, "r_x2": 237.742, "r_y2": 464.509, "r_x3": 136.8, "r_y3": 464.509, "coord_origin": "TOPLEFT"}, "text": "shown in Example 2-1.", "orig": "shown in Example 2-1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1."}, {"label": "caption", "id": 16, "page_no": 7, "cluster": {"id": 16, "label": "caption", "bbox": {"l": 136.8, "t": 486.558, "r": 462.374, "b": 494.883, "coord_origin": "TOPLEFT"}, "confidence": 0.8159958720207214, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 494.883, "r_x1": 462.374, "r_y1": 494.883, "r_x2": 462.374, "r_y2": 486.558, "r_x3": 136.8, "r_y3": 486.558, "coord_origin": "TOPLEFT"}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC", "orig": "Example 2-1 Query to determine who has authority to define and manage RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 2-1 Query to determine who has authority to define and manage RCAC"}, {"label": "key_value_region", "id": 18, "page_no": 7, "cluster": {"id": 18, "label": "key_value_region", "bbox": {"l": 136.8, "t": 503.957, "r": 331.677, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5808265805244446, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 19, "label": "text", "bbox": {"l": 136.8, "t": 503.957, "r": 251.699, "b": 512.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5631121397018433, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 512.357, "r_x1": 251.699, "r_y1": 512.357, "r_x2": 251.699, "r_y2": 503.957, "r_x3": 136.8, "r_y3": 503.957, "coord_origin": "TOPLEFT"}, "text": "SELECT function_id,", "orig": "SELECT function_id,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 38, "label": "text", "bbox": {"l": 136.8, "t": 515.957, "r": 241.739, "b": 524.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 524.357, "r_x1": 241.739, "r_y1": 524.357, "r_x2": 241.739, "r_y2": 515.957, "r_x3": 136.8, "r_y3": 515.957, "coord_origin": "TOPLEFT"}, "text": " user_name,", "orig": " user_name,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 39, "label": "text", "bbox": {"l": 136.8, "t": 527.956, "r": 221.699, "b": 536.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 536.357, "r_x1": 221.699, "r_y1": 536.357, "r_x2": 221.699, "r_y2": 527.956, "r_x3": 136.8, "r_y3": 527.956, "coord_origin": "TOPLEFT"}, "text": " usage,", "orig": " usage,", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 136.8, "t": 539.956, "r": 236.699, "b": 548.357, "coord_origin": "TOPLEFT"}, "confidence": 0.5346826314926147, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 548.357, "r_x1": 236.699, "r_y1": 548.357, "r_x2": 236.699, "r_y2": 539.956, "r_x3": 136.8, "r_y3": 539.956, "coord_origin": "TOPLEFT"}, "text": " user_type", "orig": " user_type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 27, "label": "text", "bbox": {"l": 136.8, "t": 551.956, "r": 261.718, "b": 560.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5035740733146667, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 560.356, "r_x1": 261.718, "r_y1": 560.356, "r_x2": 261.718, "r_y2": 551.956, "r_x3": 136.8, "r_y3": 551.956, "coord_origin": "TOPLEFT"}, "text": "FROM function_usage", "orig": "FROM function_usage", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 136.8, "t": 563.956, "r": 331.677, "b": 572.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5171723365783691, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 572.356, "r_x1": 331.677, "r_y1": 572.356, "r_x2": 331.677, "r_y2": 563.956, "r_x3": 136.8, "r_y3": 563.956, "coord_origin": "TOPLEFT"}, "text": "WHERE function_id='QIBM_DB_SECADM'", "orig": "WHERE function_id='QIBM_DB_SECADM'", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 26, "label": "text", "bbox": {"l": 136.8, "t": 575.956, "r": 241.739, "b": 584.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5167025327682495, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 584.356, "r_x1": 241.739, "r_y1": 584.356, "r_x2": 241.739, "r_y2": 575.956, "r_x3": 136.8, "r_y3": 575.956, "coord_origin": "TOPLEFT"}, "text": "ORDER BY user_name;", "orig": "ORDER BY user_name;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null}, {"label": "section_header", "id": 5, "page_no": 7, "cluster": {"id": 5, "label": "section_header", "bbox": {"l": 64.8, "t": 620.221, "r": 249.582, "b": 634.984, "coord_origin": "TOPLEFT"}, "confidence": 0.9656105041503906, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 634.984, "r_x1": 249.582, "r_y1": 634.984, "r_x2": 249.582, "r_y2": 620.221, "r_x3": 64.8, "r_y3": 620.221, "coord_origin": "TOPLEFT"}, "text": "2.2 Separation of duties", "orig": "2.2 Separation of duties", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2.2 Separation of duties"}, {"label": "text", "id": 0, "page_no": 7, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 652.549, "r": 547.592, "b": 709.761, "coord_origin": "TOPLEFT"}, "confidence": 0.9853070974349976, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 661.762, "r_x1": 529.043, "r_y1": 661.762, "r_x2": 529.043, "r_y2": 652.549, "r_x3": 136.8, "r_y3": 652.549, "coord_origin": "TOPLEFT"}, "text": "Separation of duties helps businesses comply with industry regulations or organizational ", "orig": "Separation of duties helps businesses comply with industry regulations or organizational ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 673.762, "r_x1": 547.592, "r_y1": 673.762, "r_x2": 547.592, "r_y2": 664.549, "r_x3": 136.8, "r_y3": 664.549, "coord_origin": "TOPLEFT"}, "text": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "orig": "requirements and simplifies the management of authorities. Separation of duties is commonly ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 685.761, "r_x1": 530.906, "r_y1": 685.761, "r_x2": 530.906, "r_y2": 676.548, "r_x3": 136.8, "r_y3": 676.548, "coord_origin": "TOPLEFT"}, "text": "used to prevent fraudulent activities or errors by a single person. It provides the ability for ", "orig": "used to prevent fraudulent activities or errors by a single person. 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"10"}, {"label": "page_footer", "id": 10, "page_no": 7, "cluster": {"id": 10, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9530044198036194, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 8, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 355.32, "r_y0": 763.863, "r_x1": 523.523, "r_y1": 763.863, "r_x2": 523.523, "r_y2": 755.538, "r_x3": 355.32, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 2. Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "11", "orig": "11", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 542.703, "r_y1": 80.72199999999998, "r_x2": 542.703, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "orig": "For example, assume that a business has assigned the duty to manage security on IBM i to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 513.687, "r_y1": 92.72199999999998, "r_x2": 513.687, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. ", "orig": "table. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.1, "r_y0": 220.70000000000005, "r_x1": 232.869, "r_y1": 220.70000000000005, "r_x2": 232.869, "r_y2": 211.48699999999997, "r_x3": 230.1, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70000000000005, "r_x1": 346.314, "r_y1": 232.70000000000005, "r_x2": 346.314, "r_y2": 223.48699999999997, "r_x3": 136.8, "r_y3": 223.48699999999997, "coord_origin": "TOPLEFT"}, "text": "authority and can be given to a user or a group.", "orig": "authority and can be given to a user or a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.71900000000005, "r_x1": 529.471, "r_y1": 266.71900000000005, "r_x2": 529.471, "r_y2": 257.506, "r_x3": 136.8, "r_y3": 257.506, "coord_origin": "TOPLEFT"}, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "orig": "a user is allowed to access in a table and whether a user is allowed to see information in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.71900000000005, "r_x1": 253.462, "r_y1": 278.71900000000005, "r_x2": 253.462, "r_y2": 269.506, "r_x3": 136.8, "r_y3": 269.506, "coord_origin": "TOPLEFT"}, "text": "certain columns of a table.", "orig": "certain columns of a table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 312.738, "r_x1": 539.816, "r_y1": 312.738, "r_x2": 539.816, "r_y2": 303.525, "r_x3": 136.8, "r_y3": 303.525, "coord_origin": "TOPLEFT"}, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 370.697, "r_x1": 343.798, "r_y1": 370.697, "r_x2": 343.798, "r_y2": 361.484, "r_x3": 136.8, "r_y3": 361.484, "coord_origin": "TOPLEFT"}, "text": "the different CL commands and DB2 for i tools.", "orig": "the different CL commands and DB2 for i tools.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 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Roles and separation of duties ", "orig": "Chapter 2. 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 153.52800000000002, "r": 547.673, "b": 198.74, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. 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The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 153.52800000000002, "r": 547.673, "b": 198.74, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. ", "orig": "table. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 211.48699999999997, "r": 538.66, "b": 232.70000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70000000000005, "r_x1": 346.314, "r_y1": 232.70000000000005, "r_x2": 346.314, "r_y2": 223.48699999999997, "r_x3": 136.8, "r_y3": 223.48699999999997, "coord_origin": "TOPLEFT"}, "text": "authority and can be given to a user or a group.", "orig": "authority and can be given to a user or a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.8, "t": 245.50599999999997, "r": 545.805, "b": 278.71900000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.71900000000005, "r_x1": 529.471, "r_y1": 266.71900000000005, "r_x2": 529.471, "r_y2": 257.506, "r_x3": 136.8, "r_y3": 257.506, "coord_origin": "TOPLEFT"}, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "orig": "a user is allowed to access in a table and whether a user is allowed to see information in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.71900000000005, "r_x1": 253.462, "r_y1": 278.71900000000005, "r_x2": 253.462, "r_y2": 269.506, "r_x3": 136.8, "r_y3": 269.506, "coord_origin": "TOPLEFT"}, "text": "certain columns of a table.", "orig": "certain columns of a table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 291.525, "r": 539.816, "b": 336.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 312.738, "r_x1": 539.816, "r_y1": 312.738, "r_x2": 539.816, "r_y2": 303.525, "r_x3": 136.8, "r_y3": 303.525, "coord_origin": "TOPLEFT"}, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 349.485, "r": 543.076, "b": 370.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 370.697, "r_x1": 343.798, "r_y1": 370.697, "r_x2": 343.798, "r_y2": 361.484, "r_x3": 136.8, "r_y3": 361.484, "coord_origin": "TOPLEFT"}, "text": "the different CL commands and DB2 for i tools.", "orig": "the different CL commands and DB2 for i tools.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools."}, {"label": "caption", "id": 8, "page_no": 8, "cluster": {"id": 8, "label": "caption", "bbox": {"l": 64.8, "t": 383.538, "r": 391.767, "b": 391.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9337189793586731, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 391.863, "r_x1": 391.767, "r_y1": 391.863, "r_x2": 391.767, "r_y2": 383.538, "r_x3": 64.8, "r_y3": 383.538, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "orig": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority"}, {"label": "table", "id": 0, "page_no": 8, "cluster": {"id": 0, "label": "table", "bbox": {"l": 64.41139221191406, "t": 393.6136169433594, "r": 547.3950805664062, "b": 721.60791015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9899571537971497, "cells": [{"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 408.843, "r_x1": 119.758, "r_y1": 408.843, "r_x2": 119.758, "r_y2": 400.518, "r_x3": 70.8, "r_y3": 400.518, "coord_origin": "TOPLEFT"}, "text": "User action", "orig": "User action", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.263, "r_y0": 487.02, "r_x1": 433.263, "r_y1": 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Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "orig": "Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.72199999999998, "r_x1": 509.728, "r_y1": 104.72199999999998, "r_x2": 509.728, "r_y2": 95.50900000000001, "r_x3": 136.8, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "orig": "privileges Theresa was granting to others. Therefore, to grant *USE privileges to the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.721, "r_x1": 528.211, "r_y1": 116.721, "r_x2": 528.211, "r_y2": 107.50800000000004, "r_x3": 136.8, "r_y3": 107.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "orig": "PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 128.721, "r_x1": 531.849, "r_y1": 128.721, "r_x2": 531.849, "r_y2": 119.50800000000004, "r_x3": 136.8, "r_y3": 119.50800000000004, "coord_origin": "TOPLEFT"}, "text": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "orig": "authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.721, "r_x1": 519.214, "r_y1": 140.721, "r_x2": 519.214, "r_y2": 131.50800000000004, "r_x3": 136.8, "r_y3": 131.50800000000004, "coord_origin": "TOPLEFT"}, "text": "PAYROLL table even though Theresa's job description was only to manage its security.", "orig": "PAYROLL table even though Theresa's job description was only to manage its security.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security."}, {"label": "text", "id": 6, "page_no": 8, "cluster": {"id": 6, "label": "text", "bbox": {"l": 136.8, "t": 153.52800000000002, "r": 547.673, "b": 198.74, "coord_origin": "TOPLEFT"}, "confidence": 0.9665538668632507, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 162.74099999999999, "r_x1": 544.17, "r_y1": 162.74099999999999, "r_x2": 544.17, "r_y2": 153.52800000000002, "r_x3": 136.8, "r_y3": 153.52800000000002, "coord_origin": "TOPLEFT"}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "orig": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 174.74, "r_x1": 540.671, "r_y1": 174.74, "r_x2": 540.671, "r_y2": 165.52700000000004, "r_x3": 136.8, "r_y3": 165.52700000000004, "coord_origin": "TOPLEFT"}, "text": "changes ownership, or changes the primary group without giving access to the object or, in ", "orig": "changes ownership, or changes the primary group without giving access to the object or, in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 186.74, "r_x1": 547.673, "r_y1": 186.74, "r_x2": 547.673, "r_y2": 177.52700000000004, "r_x3": 136.8, "r_y3": 177.52700000000004, "coord_origin": "TOPLEFT"}, "text": "the case of a database table, to the data that is in the table or allowing other operations on the ", "orig": "the case of a database table, to the data that is in the table or allowing other operations on the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 198.74, "r_x1": 163.628, "r_y1": 198.74, "r_x2": 163.628, "r_y2": 189.52700000000004, "r_x3": 136.8, "r_y3": 189.52700000000004, "coord_origin": "TOPLEFT"}, "text": "table. 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", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table."}, {"label": "text", "id": 4, "page_no": 8, "cluster": {"id": 4, "label": "text", "bbox": {"l": 136.8, "t": 211.48699999999997, "r": 538.66, "b": 232.70000000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9691813588142395, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 220.70000000000005, "r_x1": 538.66, "r_y1": 220.70000000000005, "r_x2": 538.66, "r_y2": 211.48699999999997, "r_x3": 136.8, "r_y3": 211.48699999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "orig": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 232.70000000000005, "r_x1": 346.314, "r_y1": 232.70000000000005, "r_x2": 346.314, "r_y2": 223.48699999999997, "r_x3": 136.8, "r_y3": 223.48699999999997, "coord_origin": "TOPLEFT"}, "text": "authority and can be given to a user or a group.", "orig": "authority and can be given to a user or a group.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group."}, {"label": "text", "id": 3, "page_no": 8, "cluster": {"id": 3, "label": "text", "bbox": {"l": 136.8, "t": 245.50599999999997, "r": 545.805, "b": 278.71900000000005, "coord_origin": "TOPLEFT"}, "confidence": 0.9729602336883545, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 254.71900000000005, "r_x1": 545.805, "r_y1": 254.71900000000005, "r_x2": 545.805, "r_y2": 245.50599999999997, "r_x3": 136.8, "r_y3": 245.50599999999997, "coord_origin": "TOPLEFT"}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "orig": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 266.71900000000005, "r_x1": 529.471, "r_y1": 266.71900000000005, "r_x2": 529.471, "r_y2": 257.506, "r_x3": 136.8, "r_y3": 257.506, "coord_origin": "TOPLEFT"}, "text": "a user is allowed to access in a table and whether a user is allowed to see information in ", "orig": "a user is allowed to access in a table and whether a user is allowed to see information in ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.71900000000005, "r_x1": 253.462, "r_y1": 278.71900000000005, "r_x2": 253.462, "r_y2": 269.506, "r_x3": 136.8, "r_y3": 269.506, "coord_origin": "TOPLEFT"}, "text": "certain columns of a table.", "orig": "certain columns of a table.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table."}, {"label": "text", "id": 1, "page_no": 8, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 291.525, "r": 539.816, "b": 336.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9815152883529663, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 300.738, "r_x1": 533.79, "r_y1": 300.738, "r_x2": 533.79, "r_y2": 291.525, "r_x3": 136.8, "r_y3": 291.525, "coord_origin": "TOPLEFT"}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "orig": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 312.738, "r_x1": 539.816, "r_y1": 312.738, "r_x2": 539.816, "r_y2": 303.525, "r_x3": 136.8, "r_y3": 303.525, "coord_origin": "TOPLEFT"}, "text": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "orig": "usage ID, but absolutely no other data privileges. The result is that the RCAC administrator ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 324.738, "r_x1": 534.523, "r_y1": 324.738, "r_x2": 534.523, "r_y2": 315.525, "r_x3": 136.8, "r_y3": 315.525, "coord_origin": "TOPLEFT"}, "text": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "orig": "can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 336.738, "r_x1": 227.021, "r_y1": 336.738, "r_x2": 227.021, "r_y2": 327.525, "r_x3": 136.8, "r_y3": 327.525, "coord_origin": "TOPLEFT"}, "text": "access to data itself.", "orig": "access to data itself.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself."}, {"label": "text", "id": 5, "page_no": 8, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 349.485, "r": 543.076, "b": 370.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9672255516052246, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 358.698, "r_x1": 543.076, "r_y1": 358.698, "r_x2": 543.076, "r_y2": 349.485, "r_x3": 136.8, "r_y3": 349.485, "coord_origin": "TOPLEFT"}, "text": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "orig": "Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 370.697, "r_x1": 343.798, "r_y1": 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Roles and separation of duties ", "orig": "Chapter 2. Roles and separation of duties ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 2. 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It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. 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Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9071078896522522, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "15", "orig": "15", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 528.74, "b": 92.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.872844398021698, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 179.591, "r_y1": 80.72199999999998, "r_x2": 179.591, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The SQL ", "orig": "The SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.58, "r_y0": 80.40499999999997, "r_x1": 264.479, "r_y1": 80.40499999999997, "r_x2": 264.479, "r_y2": 71.957, "r_x3": 179.58, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION", "orig": "CREATE PERMISSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.54, "r_y0": 80.72199999999998, "r_x1": 528.74, "r_y1": 80.72199999999998, "r_x2": 528.74, "r_y2": 71.50900000000001, "r_x3": 264.54, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": " statement that is shown in Figure 3-1 is used to define and ", "orig": " statement that is shown in Figure 3-1 is used to define and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 341.703, "r_y1": 92.72199999999998, "r_x2": 341.703, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "initially enable or disable the row access rules.", "orig": "initially enable or disable the row access rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "caption", "bbox": {"l": 136.8, "t": 414.138, "r": 341.985, "b": 422.463, "coord_origin": "TOPLEFT"}, "confidence": 0.938783586025238, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 422.463, "r_x1": 341.985, "r_y1": 422.463, "r_x2": 341.985, "r_y2": 414.138, "r_x3": 136.8, "r_y3": 414.138, "coord_origin": "TOPLEFT"}, "text": "Figure 3-1 CREATE PERMISSION SQL statement", "orig": "Figure 3-1 CREATE PERMISSION SQL statement", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 136.8, "t": 439.944, "r": 215.383, "b": 451.044, "coord_origin": "TOPLEFT"}, "confidence": 0.9477567076683044, "cells": [{"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 451.044, "r_x1": 215.383, "r_y1": 451.044, "r_x2": 215.383, "r_y2": 439.944, "r_x3": 136.8, "r_y3": 439.944, "coord_origin": "TOPLEFT"}, "text": "Column mask", "orig": "Column mask", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 455.089, "r": 542.776, "b": 500.301, "coord_origin": "TOPLEFT"}, "confidence": 0.9843975901603699, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 464.302, "r_x1": 542.776, "r_y1": 464.302, "r_x2": 542.776, "r_y2": 455.089, "r_x3": 136.8, "r_y3": 455.089, "coord_origin": "TOPLEFT"}, "text": "A column mask is a database object that manifests a column value access control rule for a ", "orig": "A column mask is a database object that manifests a column value access control rule for a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 476.302, "r_x1": 537.85, "r_y1": 476.302, "r_x2": 537.85, "r_y2": 467.089, "r_x3": 136.8, "r_y3": 467.089, "coord_origin": "TOPLEFT"}, "text": "specific column in a specific table. It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. For example, a teller can see only the last four digits of a tax ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 500.301, "r_x1": 231.178, "r_y1": 500.301, "r_x2": 231.178, "r_y2": 491.088, "r_x3": 136.8, "r_y3": 491.088, "coord_origin": "TOPLEFT"}, "text": "identification number.", "orig": "identification number.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 135.97177124023438, "t": 107.41075134277344, "r": 545.4180908203125, "b": 410.60931396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9788318872451782, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 149.37099999999998, "r_x1": 251.916, "r_y1": 149.37099999999998, "r_x2": 251.916, "r_y2": 140.20799999999997, "r_x3": 148.134, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION ", "orig": "CREATE PERMISSION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.867, "r_y0": 149.36, "r_x1": 257.586, "r_y1": 149.36, "r_x2": 257.586, "r_y2": 140.20799999999997, "r_x3": 251.867, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 257.592, "r_y0": 149.37099999999998, "r_x1": 337.002, "r_y1": 149.37099999999998, "r_x2": 337.002, "r_y2": 140.20799999999997, "r_x3": 257.592, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "permission name", "orig": "permission name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 337.012, 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"orig": "ON", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.584, "r_y0": 190.74300000000005, "r_x1": 174.303, "r_y1": 190.74300000000005, "r_x2": 174.303, "r_y2": 181.591, "r_x3": 168.584, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.309, "r_y0": 190.75400000000002, "r_x1": 226.875, "r_y1": 190.75400000000002, "r_x2": 226.875, "r_y2": 181.591, "r_x3": 174.309, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "table name", "orig": "table name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.865, "r_y0": 190.74300000000005, "r_x1": 232.584, "r_y1": 190.74300000000005, "r_x2": 232.584, "r_y2": 181.591, "r_x3": 226.865, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 311.32, "r_y0": 174.05600000000004, "r_x1": 529.937, "r_y1": 174.05600000000004, "r_x2": 529.937, "r_y2": 166.71799999999996, "r_x3": 311.32, "r_y3": 166.71799999999996, "coord_origin": "TOPLEFT"}, "text": "Identifies the table on which the row permission is created", "orig": "Identifies the table on which the row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 232.13699999999994, "r_x1": 163.113, "r_y1": 232.13699999999994, "r_x2": 163.113, "r_y2": 222.97400000000005, "r_x3": 148.134, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "AS ", "orig": "AS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.687, "r_y0": 232.12599999999998, "r_x1": 171.406, "r_y1": 232.12599999999998, "r_x2": 171.406, "r_y2": 222.97400000000005, "r_x3": 165.687, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 171.411, "r_y0": 232.13699999999994, "r_x1": 251.207, "r_y1": 232.13699999999994, "r_x2": 251.207, "r_y2": 222.97400000000005, "r_x3": 171.411, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "correlation name", "orig": "correlation name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.211, "r_y0": 232.12599999999998, "r_x1": 256.93, "r_y1": 232.12599999999998, "r_x2": 256.93, "r_y2": 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{"id": 2, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9526246786117554, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9071078896522522, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "15", "orig": "15", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "15"}, {"label": "caption", "id": 6, "page_no": 9, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 528.74, "b": 92.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.872844398021698, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 179.591, "r_y1": 80.72199999999998, "r_x2": 179.591, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "The SQL ", "orig": "The SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 179.58, "r_y0": 80.40499999999997, "r_x1": 264.479, "r_y1": 80.40499999999997, "r_x2": 264.479, "r_y2": 71.957, "r_x3": 179.58, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION", "orig": "CREATE PERMISSION", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 264.54, "r_y0": 80.72199999999998, "r_x1": 528.74, "r_y1": 80.72199999999998, "r_x2": 528.74, "r_y2": 71.50900000000001, "r_x3": 264.54, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": " statement that is shown in Figure 3-1 is used to define and ", "orig": " statement that is shown in Figure 3-1 is used to define and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.72199999999998, "r_x1": 341.703, "r_y1": 92.72199999999998, "r_x2": 341.703, "r_y2": 83.50900000000001, "r_x3": 136.8, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "initially enable or disable the row access rules.", "orig": "initially enable or disable the row access rules.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules."}, {"label": "caption", "id": 4, "page_no": 9, "cluster": {"id": 4, "label": "caption", "bbox": {"l": 136.8, "t": 414.138, "r": 341.985, "b": 422.463, "coord_origin": "TOPLEFT"}, "confidence": 0.938783586025238, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 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"from_ocr": false}], "children": []}, "text": "Column mask"}, {"label": "text", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 455.089, "r": 542.776, "b": 500.301, "coord_origin": "TOPLEFT"}, "confidence": 0.9843975901603699, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 464.302, "r_x1": 542.776, "r_y1": 464.302, "r_x2": 542.776, "r_y2": 455.089, "r_x3": 136.8, "r_y3": 455.089, "coord_origin": "TOPLEFT"}, "text": "A column mask is a database object that manifests a column value access control rule for a ", "orig": "A column mask is a database object that manifests a column value access control rule for a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 476.302, "r_x1": 537.85, "r_y1": 476.302, "r_x2": 537.85, "r_y2": 467.089, "r_x3": 136.8, "r_y3": 467.089, "coord_origin": 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It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. 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For example, a teller can see only the last four digits of a tax identification number."}, {"label": "picture", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 135.97177124023438, "t": 107.41075134277344, "r": 545.4180908203125, "b": 410.60931396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9788318872451782, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 149.37099999999998, "r_x1": 251.916, "r_y1": 149.37099999999998, "r_x2": 251.916, "r_y2": 140.20799999999997, "r_x3": 148.134, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION ", "orig": "CREATE PERMISSION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.867, "r_y0": 149.36, "r_x1": 257.586, "r_y1": 149.36, "r_x2": 257.586, "r_y2": 140.20799999999997, "r_x3": 251.867, "r_y3": 140.20799999999997, 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"text", "id": 0, "page_no": 9, "cluster": {"id": 0, "label": "text", "bbox": {"l": 136.8, "t": 455.089, "r": 542.776, "b": 500.301, "coord_origin": "TOPLEFT"}, "confidence": 0.9843975901603699, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 464.302, "r_x1": 542.776, "r_y1": 464.302, "r_x2": 542.776, "r_y2": 455.089, "r_x3": 136.8, "r_y3": 455.089, "coord_origin": "TOPLEFT"}, "text": "A column mask is a database object that manifests a column value access control rule for a ", "orig": "A column mask is a database object that manifests a column value access control rule for a ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 476.302, "r_x1": 537.85, "r_y1": 476.302, "r_x2": 537.85, "r_y2": 467.089, "r_x3": 136.8, "r_y3": 467.089, "coord_origin": "TOPLEFT"}, "text": "specific column in a specific table. It uses a CASE expression that describes what you see ", "orig": "specific column in a specific table. It uses a CASE expression that describes what you see ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.301, "r_x1": 538.818, "r_y1": 488.301, "r_x2": 538.818, "r_y2": 479.088, "r_x3": 136.8, "r_y3": 479.088, "coord_origin": "TOPLEFT"}, "text": "when you access the column. For example, a teller can see only the last four digits of a tax ", "orig": "when you access the column. For example, a teller can see only the last four digits of a tax ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 500.301, "r_x1": 231.178, "r_y1": 500.301, "r_x2": 231.178, "r_y2": 491.088, "r_x3": 136.8, "r_y3": 491.088, "coord_origin": "TOPLEFT"}, "text": "identification number.", "orig": "identification number.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number."}, {"label": "picture", "id": 1, "page_no": 9, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 135.97177124023438, "t": 107.41075134277344, "r": 545.4180908203125, "b": 410.60931396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9788318872451782, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 149.37099999999998, "r_x1": 251.916, "r_y1": 149.37099999999998, "r_x2": 251.916, "r_y2": 140.20799999999997, "r_x3": 148.134, "r_y3": 140.20799999999997, "coord_origin": "TOPLEFT"}, "text": "CREATE PERMISSION ", "orig": "CREATE PERMISSION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 251.867, "r_y0": 149.36, "r_x1": 257.586, "r_y1": 149.36, "r_x2": 257.586, "r_y2": 140.20799999999997, "r_x3": 251.867, "r_y3": 140.20799999999997, 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"r_x1": 530.748, "r_y1": 129.22400000000005, "r_x2": 530.748, "r_y2": 121.88599999999997, "r_x3": 346.565, "r_y3": 121.88599999999997, "coord_origin": "TOPLEFT"}, "text": "Names the row permission for row access control", "orig": "Names the row permission for row access control", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 190.75400000000002, "r_x1": 163.458, "r_y1": 190.75400000000002, "r_x2": 163.458, "r_y2": 181.591, "r_x3": 148.134, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "ON", "orig": "ON", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 168.584, "r_y0": 190.74300000000005, "r_x1": 174.303, "r_y1": 190.74300000000005, "r_x2": 174.303, "r_y2": 181.591, "r_x3": 168.584, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 174.309, "r_y0": 190.75400000000002, "r_x1": 226.875, "r_y1": 190.75400000000002, "r_x2": 226.875, "r_y2": 181.591, "r_x3": 174.309, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": "table name", "orig": "table name", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 226.865, "r_y0": 190.74300000000005, "r_x1": 232.584, "r_y1": 190.74300000000005, "r_x2": 232.584, "r_y2": 181.591, "r_x3": 226.865, "r_y3": 181.591, "coord_origin": "TOPLEFT"}, "text": ">", "orig": ">", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 311.32, "r_y0": 174.05600000000004, "r_x1": 529.937, "r_y1": 174.05600000000004, "r_x2": 529.937, "r_y2": 166.71799999999996, "r_x3": 311.32, "r_y3": 166.71799999999996, "coord_origin": "TOPLEFT"}, "text": "Identifies the table on which the row permission is created", "orig": "Identifies the table on which the row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 232.13699999999994, "r_x1": 163.113, "r_y1": 232.13699999999994, "r_x2": 163.113, "r_y2": 222.97400000000005, "r_x3": 148.134, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "AS ", "orig": "AS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.687, "r_y0": 232.12599999999998, "r_x1": 171.406, "r_y1": 232.12599999999998, "r_x2": 171.406, "r_y2": 222.97400000000005, "r_x3": 165.687, "r_y3": 222.97400000000005, "coord_origin": "TOPLEFT"}, "text": "<", "orig": "<", "text_direction": 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permission is created", "orig": "Indicates that a row permission is created", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 321.597, "r_y0": 274.06500000000005, "r_x1": 530.955, "r_y1": 274.06500000000005, "r_x2": 530.955, "r_y2": 266.726, "r_x3": 321.597, "r_y3": 266.726, "coord_origin": "TOPLEFT"}, "text": "Specifies a condition that can be true, false, or unknown", "orig": "Specifies a condition that can be true, false, or unknown", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.134, "r_y0": 301.109, "r_x1": 183.43, "r_y1": 301.109, "r_x2": 183.43, "r_y2": 291.945, "r_x3": 148.134, "r_y3": 291.945, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, 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"TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 9, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9071078896522522, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "15", "orig": "15", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "15"}]}}, {"page_no": 10, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.702, "r_x1": 538.503, "r_y1": 223.702, "r_x2": 538.503, "r_y2": 214.48900000000003, "r_x3": 136.8, "r_y3": 214.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.245, "r_y0": 197.88300000000004, "r_x1": 425.656, "r_y1": 197.88300000000004, "r_x2": 425.656, "r_y2": 189.558, "r_x3": 230.245, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "The authorization ID that initiated the connection.", "orig": "The authorization ID that initiated the connection.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 140.732, "r_y0": 395.376, "r_x1": 216.376, "r_y1": 395.376, "r_x2": 216.376, "r_y2": 387.442, "r_x3": 140.732, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": "Signed on as ALICE", "orig": "Signed on as ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 167.708, "r_y0": 395.376, "r_x1": 169.958, "r_y1": 395.376, "r_x2": 169.958, "r_y2": 387.442, "r_x3": 167.708, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 180.516, "r_y0": 395.376, "r_x1": 182.765, "r_y1": 395.376, "r_x2": 182.765, "r_y2": 387.442, "r_x3": 180.516, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 191.498, "r_y0": 395.376, "r_x1": 193.748, "r_y1": 395.376, "r_x2": 193.748, "r_y2": 387.442, "r_x3": 191.498, "r_y3": 387.442, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 419.266, "r_x1": 191.711, "r_y1": 419.266, "r_x2": 191.711, "r_y2": 411.332, "r_x3": 138.476, "r_y3": 411.332, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 431.211, "r_x1": 232.566, "r_y1": 431.211, "r_x2": 232.566, "r_y2": 423.277, "r_x3": 138.476, "r_y3": 423.277, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = ALICE", "orig": "CURRENT USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 455.1, "r_x1": 183.266, "r_y1": 455.1, "r_x2": 183.266, "r_y2": 447.167, "r_x3": 138.476, "r_y3": 447.167, "coord_origin": "TOPLEFT"}, "text": "CALL proc1", "orig": "CALL proc1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 481.976, "r_x1": 174.059, "r_y1": 481.976, "r_x2": 174.059, "r_y2": 474.043, "r_x3": 148.43, "r_y3": 474.043, "coord_origin": "TOPLEFT"}, "text": "Proc1:", "orig": "Proc1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.522, "r_y0": 493.921, "r_x1": 209.1, "r_y1": 493.921, "r_x2": 209.1, "r_y2": 485.988, "r_x3": 157.522, "r_y3": 485.988, "coord_origin": "TOPLEFT"}, "text": "Owner = JOE", "orig": "Owner = JOE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.522, "r_y0": 505.866, "r_x1": 281.693, "r_y1": 505.866, "r_x2": 281.693, "r_y2": 497.933, "r_x3": 157.522, "r_y3": 497.933, "coord_origin": "TOPLEFT"}, "text": "SET OPTION USRPRF=*OWNER", "orig": "SET OPTION USRPRF=*OWNER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 529.756, "r_x1": 201.665, "r_y1": 529.756, "r_x2": 201.665, "r_y2": 521.823, "r_x3": 148.43, "r_y3": 521.823, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 541.701, "r_x1": 234.579, "r_y1": 541.701, "r_x2": 234.579, "r_y2": 533.768, "r_x3": 148.43, "r_y3": 533.768, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = JOE", "orig": "CURRENT USER = JOE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 574.55, "r_x1": 191.711, "r_y1": 574.55, "r_x2": 191.711, "r_y2": 566.616, "r_x3": 138.476, "r_y3": 566.616, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 586.495, "r_x1": 232.566, "r_y1": 586.495, "r_x2": 232.566, "r_y2": 578.561, "r_x3": 138.476, "r_y3": 578.561, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = ALICE", "orig": "CURRENT USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9130511283874512, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 412.174, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.7875156402587891, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49800000000005, "r": 372.618, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48900000000003, "r": 538.503, "b": 235.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.702, "r_x1": 538.503, "r_y1": 223.702, "r_x2": 538.503, "r_y2": 214.48900000000003, "r_x3": 136.8, "r_y3": 214.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52800000000002, "r": 411.326, "b": 252.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50800000000004, "r": 453.245, "b": 269.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48800000000006, "r": 541.459, "b": 298.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.527, "r": 547.584, "b": 339.74, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.801, "t": 347.507, "r": 547.483, "b": 368.719, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.718, "r": 341.274, "b": 605.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 625.5550000000001, "r": 247.016, "b": 637.543, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.7090000000001, "r": 518.01, "b": 672.922, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.728, "r": 532.347, "b": 718.941, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.245, "r_y0": 197.88300000000004, "r_x1": 425.656, "r_y1": 197.88300000000004, "r_x2": 425.656, "r_y2": 189.558, "r_x3": 230.245, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "The authorization ID that initiated the connection.", "orig": "The authorization ID that initiated the connection.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": [{"id": 39, "label": "text", "bbox": {"l": 142.8, "t": 110.53800000000001, "r": 209.69, "b": 118.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 40, "label": 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"coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 142.8, "t": 140.539, "r": 212.711, "b": 148.86400000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 230.22, "t": 129.49800000000005, "r": 468.01, "b": 137.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 44, "label": "text", "bbox": {"l": 142.8, "t": 159.558, "r": 216.656, "b": 167.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 45, "label": "text", "bbox": {"l": 230.198, "t": 159.558, "r": 536.173, "b": 167.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 46, "label": "text", "bbox": {"l": 230.221, "t": 170.538, "r": 447.381, "b": 178.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 47, "label": "text", "bbox": {"l": 142.801, "t": 189.558, "r": 209.743, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 48, "label": "text", "bbox": {"l": 230.245, "t": 189.558, "r": 425.656, "b": 197.88300000000004, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.245, "r_y0": 197.88300000000004, "r_x1": 425.656, "r_y1": 197.88300000000004, "r_x2": 425.656, "r_y2": 189.558, "r_x3": 230.245, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "The authorization ID that initiated the connection.", "orig": "The authorization ID that initiated the connection.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": null, "otsl_seq": ["ched", "ched", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl", "fcel", "fcel", "nl"], "num_rows": 4, "num_cols": 2, "table_cells": [{"bbox": {"l": 142.8, "t": 110.53800000000001, "r": 209.69, "b": 118.86300000000006, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "Special register", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 230.189, "t": 110.53800000000001, "r": 319.933, "b": 118.86300000000006, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 0, "end_row_offset_idx": 1, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "Corresponding value", "column_header": true, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 129.49800000000005, "r": 212.711, "b": 148.86400000000003, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "USER or SESSION_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.22, "t": 129.49800000000005, "r": 468.01, "b": 137.82299999999998, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 1, "end_row_offset_idx": 2, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread excluding adopted authority.", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 142.8, "t": 159.558, "r": 216.656, "b": 167.88300000000004, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 0, "end_col_offset_idx": 1, "text": "CURRENT_USER", "column_header": false, "row_header": false, "row_section": false}, {"bbox": {"l": 230.198, "t": 159.558, "r": 536.173, "b": 178.86300000000006, "coord_origin": "TOPLEFT"}, "row_span": 1, "col_span": 1, "start_row_offset_idx": 2, "end_row_offset_idx": 3, "start_col_offset_idx": 1, "end_col_offset_idx": 2, "text": "The effective user of the thread including adopted authority. 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Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9130511283874512, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "19"}, {"label": "caption", "id": 14, "page_no": 10, "cluster": {"id": 14, "label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 412.174, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.7875156402587891, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49800000000005, "r": 372.618, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48900000000003, "r": 538.503, "b": 235.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.702, "r_x1": 538.503, "r_y1": 223.702, "r_x2": 538.503, "r_y2": 214.48900000000003, "r_x3": 136.8, "r_y3": 214.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52800000000002, "r": 411.326, "b": 252.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50800000000004, "r": 453.245, "b": 269.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48800000000006, "r": 541.459, "b": 298.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.527, "r": 547.584, "b": 339.74, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.801, "t": 347.507, "r": 547.483, "b": 368.719, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.718, "r": 341.274, "b": 605.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 625.5550000000001, "r": 247.016, "b": 637.543, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.7090000000001, "r": 518.01, "b": 672.922, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.728, "r": 532.347, "b": 718.941, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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"coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 42, "label": "text", "bbox": {"l": 142.8, "t": 140.539, "r": 212.711, "b": 148.86400000000003, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 43, "label": "text", "bbox": {"l": 230.22, "t": 129.49800000000005, "r": 468.01, "b": 137.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 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"label": "caption", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 412.174, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.7875156402587891, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 412.174, "r_y1": 80.72199999999998, "r_x2": 412.174, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 summarizes these special registers and their values.", "orig": "Table 3-1 summarizes these special registers and their values.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 summarizes these special registers and their values."}, {"label": "caption", "id": 12, "page_no": 10, "cluster": {"id": 12, "label": "caption", "bbox": {"l": 136.8, "t": 93.49800000000005, "r": 372.618, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9253708720207214, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 101.82299999999998, "r_x1": 372.618, "r_y1": 101.82299999999998, "r_x2": 372.618, "r_y2": 93.49800000000005, "r_x3": 136.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-1 Special registers and their corresponding values", "orig": "Table 3-1 Special registers and their corresponding values", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-1 Special registers and their corresponding values"}, {"label": "text", "id": 9, "page_no": 10, "cluster": {"id": 9, "label": "text", "bbox": {"l": 136.8, "t": 214.48900000000003, "r": 538.503, "b": 235.702, "coord_origin": "TOPLEFT"}, "confidence": 0.9466660022735596, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 223.702, "r_x1": 538.503, "r_y1": 223.702, "r_x2": 538.503, "r_y2": 214.48900000000003, "r_x3": 136.8, "r_y3": 214.48900000000003, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "orig": "Figure 3-5 shows the difference in the special register values when an adopted authority is ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 235.702, "r_x1": 161.228, "r_y1": 235.702, "r_x2": 161.228, "r_y2": 226.48900000000003, "r_x3": 136.8, "r_y3": 226.48900000000003, "coord_origin": "TOPLEFT"}, "text": "used:", "orig": "used:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-5 shows the difference in the special register values when an adopted authority is used:"}, {"label": "list_item", "id": 8, "page_no": 10, "cluster": {"id": 8, "label": "list_item", "bbox": {"l": 136.8, "t": 243.52800000000002, "r": 411.326, "b": 252.74099999999999, "coord_origin": "TOPLEFT"}, "confidence": 0.9504424333572388, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 252.37699999999995, "r_x1": 141.78, "r_y1": 252.37699999999995, "r_x2": 141.78, "r_y2": 243.976, "r_x3": 136.8, "r_y3": 243.976, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 252.74099999999999, "r_x1": 411.326, "r_y1": 252.74099999999999, "r_x2": 411.326, "r_y2": 243.52800000000002, "r_x3": 151.2, "r_y3": 243.52800000000002, "coord_origin": "TOPLEFT"}, "text": "A user connects to the server using the user profile ALICE.", "orig": "A user connects to the server using the user profile ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 A user connects to the server using the user profile ALICE."}, {"label": "list_item", "id": 10, "page_no": 10, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 260.50800000000004, "r": 453.245, "b": 269.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9445974230766296, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 269.356, "r_x1": 141.78, "r_y1": 269.356, "r_x2": 141.78, "r_y2": 260.956, "r_x3": 136.8, "r_y3": 260.956, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 269.721, "r_x1": 453.245, "r_y1": 269.721, "r_x2": 453.245, "r_y2": 260.50800000000004, "r_x3": 151.2, "r_y3": 260.50800000000004, "coord_origin": "TOPLEFT"}, "text": "USER and CURRENT USER initially have the same value of ALICE.", "orig": "USER and CURRENT USER initially have the same value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 USER and CURRENT USER initially have the same value of ALICE."}, {"label": "list_item", "id": 3, "page_no": 10, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 277.48800000000006, "r": 541.459, "b": 298.701, "coord_origin": "TOPLEFT"}, "confidence": 0.9742953777313232, "cells": [{"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 286.336, "r_x1": 141.78, "r_y1": 286.336, "r_x2": 141.78, "r_y2": 277.93600000000004, "r_x3": 136.8, "r_y3": 277.93600000000004, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 286.701, "r_x1": 541.459, "r_y1": 286.701, "r_x2": 541.459, "r_y2": 277.48800000000006, "r_x3": 151.2, "r_y3": 277.48800000000006, "coord_origin": "TOPLEFT"}, "text": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "orig": "ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 298.701, "r_x1": 409.797, "r_y1": 298.701, "r_x2": 409.797, "r_y2": 289.488, "r_x3": 151.2, "r_y3": 289.488, "coord_origin": "TOPLEFT"}, "text": "and was created to adopt JOE's authority when it is called.", "orig": "and was created to adopt JOE's authority when it is called.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called."}, {"label": "list_item", "id": 0, "page_no": 10, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 136.8, "t": 306.527, "r": 547.584, "b": 339.74, "coord_origin": "TOPLEFT"}, "confidence": 0.981132447719574, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 315.376, "r_x1": 141.78, "r_y1": 315.376, "r_x2": 141.78, "r_y2": 306.975, "r_x3": 136.8, "r_y3": 306.975, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 315.74, "r_x1": 547.584, "r_y1": 315.74, "r_x2": 547.584, "r_y2": 306.527, "r_x3": 151.2, "r_y3": 306.527, "coord_origin": "TOPLEFT"}, "text": "While the procedure is running, the special register USER still contains the value of ALICE ", "orig": "While the procedure is running, the special register USER still contains the value of ALICE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 327.74, "r_x1": 514.339, "r_y1": 327.74, "r_x2": 514.339, "r_y2": 318.527, "r_x3": 151.201, "r_y3": 318.527, "coord_origin": "TOPLEFT"}, "text": "because it excludes any adopted authority. The special register CURRENT USER ", "orig": "because it excludes any adopted authority. The special register CURRENT USER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 339.74, "r_x1": 453.294, "r_y1": 339.74, "r_x2": 453.294, "r_y2": 330.527, "r_x3": 151.201, "r_y3": 330.527, "coord_origin": "TOPLEFT"}, "text": "contains the value of JOE because it includes any adopted authority.", "orig": "contains the value of JOE because it includes any adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority."}, {"label": "list_item", "id": 2, "page_no": 10, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.801, "t": 347.507, "r": 547.483, "b": 368.719, "coord_origin": "TOPLEFT"}, "confidence": 0.9748278260231018, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.801, "r_y0": 356.355, "r_x1": 141.781, "r_y1": 356.355, "r_x2": 141.781, "r_y2": 347.955, "r_x3": 136.801, "r_y3": 347.955, "coord_origin": "TOPLEFT"}, "text": "/SM590000", "orig": "/SM590000", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 356.72, "r_x1": 547.483, "r_y1": 356.72, "r_x2": 547.483, "r_y2": 347.507, "r_x3": 151.201, "r_y3": 347.507, "coord_origin": "TOPLEFT"}, "text": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "orig": "When proc1 ends, the session reverts to its original state with both USER and CURRENT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.201, "r_y0": 368.719, "r_x1": 299.563, "r_y1": 368.719, "r_x2": 299.563, "r_y2": 359.506, "r_x3": 151.201, "r_y3": 359.506, "coord_origin": "TOPLEFT"}, "text": "USER having the value of ALICE.", "orig": "USER having the value of ALICE.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/SM590000 When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE."}, {"label": "caption", "id": 11, "page_no": 10, "cluster": {"id": 11, "label": "caption", "bbox": {"l": 136.8, "t": 596.718, "r": 341.274, "b": 605.043, "coord_origin": "TOPLEFT"}, "confidence": 0.9274530410766602, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 605.043, "r_x1": 341.274, "r_y1": 605.043, "r_x2": 341.274, "r_y2": 596.718, "r_x3": 136.8, "r_y3": 596.718, "coord_origin": "TOPLEFT"}, "text": "Figure 3-5 Special registers and adopted authority", "orig": "Figure 3-5 Special registers and adopted authority", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-5 Special registers and adopted authority"}, {"label": "section_header", "id": 6, "page_no": 10, "cluster": {"id": 6, "label": "section_header", "bbox": {"l": 64.8, "t": 625.5550000000001, "r": 247.016, "b": 637.543, "coord_origin": "TOPLEFT"}, "confidence": 0.9659212827682495, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 637.543, "r_x1": 247.016, "r_y1": 637.543, "r_x2": 247.016, "r_y2": 625.5550000000001, "r_x3": 64.8, "r_y3": 625.5550000000001, "coord_origin": "TOPLEFT"}, "text": "3.2.2 Built-in global variables", "orig": "3.2.2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.2.2 Built-in global variables"}, {"label": "text", "id": 5, "page_no": 10, "cluster": {"id": 5, "label": "text", "bbox": {"l": 136.8, "t": 651.7090000000001, "r": 518.01, "b": 672.922, "coord_origin": "TOPLEFT"}, "confidence": 0.9696778059005737, "cells": [{"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.922, "r_x1": 518.01, "r_y1": 660.922, "r_x2": 518.01, "r_y2": 651.7090000000001, "r_x3": 136.8, "r_y3": 651.7090000000001, "coord_origin": "TOPLEFT"}, "text": "Built-in global variables are provided with the database manager and are used in SQL ", "orig": "Built-in global variables are provided with the database manager and are used in SQL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.922, "r_x1": 462.83, "r_y1": 672.922, "r_x2": 462.83, "r_y2": 663.7090000000001, "r_x3": 136.8, "r_y3": 663.7090000000001, "coord_origin": "TOPLEFT"}, "text": "statements to retrieve scalar values that are associated with the variables.", "orig": "statements to retrieve scalar values that are associated with the variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables."}, {"label": "text", "id": 1, "page_no": 10, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 685.728, "r": 532.347, "b": 718.941, "coord_origin": "TOPLEFT"}, "confidence": 0.978398323059082, "cells": [{"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 694.941, "r_x1": 504.455, "r_y1": 694.941, "r_x2": 504.455, "r_y2": 685.728, "r_x3": 136.8, "r_y3": 685.728, "coord_origin": "TOPLEFT"}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "orig": "IBM DB2 for i supports nine different built-in global variables that are read only and ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 706.941, "r_x1": 532.347, "r_y1": 706.941, "r_x2": 532.347, "r_y2": 697.728, "r_x3": 136.8, "r_y3": 697.728, "coord_origin": "TOPLEFT"}, "text": "maintained by the system. These global variables can be used to identify attributes of the ", "orig": "maintained by the system. These global variables can be used to identify attributes of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 718.941, "r_x1": 391.345, "r_y1": 718.941, "r_x2": 391.345, "r_y2": 709.728, "r_x3": 136.8, "r_y3": 709.728, "coord_origin": "TOPLEFT"}, "text": "database connection and used as part of the RCAC logic.", "orig": "database connection and used as part of the RCAC logic.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic."}, {"label": "table", "id": 4, "page_no": 10, "cluster": {"id": 4, "label": "table", "bbox": {"l": 134.5462646484375, "t": 103.41889190673828, "r": 542.0460815429688, "b": 204.2716064453125, "coord_origin": "TOPLEFT"}, "confidence": 0.9731299877166748, "cells": [{"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 118.86300000000006, "r_x1": 209.69, "r_y1": 118.86300000000006, "r_x2": 209.69, "r_y2": 110.53800000000001, "r_x3": 142.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Special register", "orig": "Special register", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.189, "r_y0": 118.86300000000006, "r_x1": 319.933, "r_y1": 118.86300000000006, "r_x2": 319.933, "r_y2": 110.53800000000001, "r_x3": 230.189, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Corresponding value", "orig": "Corresponding value", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 137.82299999999998, "r_x1": 178.274, "r_y1": 137.82299999999998, "r_x2": 178.274, "r_y2": 129.49800000000005, "r_x3": 142.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "USER or", "orig": "USER or", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 148.86400000000003, "r_x1": 212.711, "r_y1": 148.86400000000003, "r_x2": 212.711, "r_y2": 140.539, "r_x3": 142.8, "r_y3": 140.539, "coord_origin": "TOPLEFT"}, "text": "SESSION_USER", "orig": "SESSION_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.22, "r_y0": 137.82299999999998, "r_x1": 468.01, "r_y1": 137.82299999999998, "r_x2": 468.01, "r_y2": 129.49800000000005, "r_x3": 230.22, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread excluding adopted authority.", "orig": "The effective user of the thread excluding adopted authority.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.8, "r_y0": 167.88300000000004, "r_x1": 216.656, "r_y1": 167.88300000000004, "r_x2": 216.656, "r_y2": 159.558, "r_x3": 142.8, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER", "orig": "CURRENT_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.198, "r_y0": 167.88300000000004, "r_x1": 536.173, "r_y1": 167.88300000000004, "r_x2": 536.173, "r_y2": 159.558, "r_x3": 230.198, "r_y3": 159.558, "coord_origin": "TOPLEFT"}, "text": "The effective user of the thread including adopted authority. When no adopted ", "orig": "The effective user of the thread including adopted authority. When no adopted ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 230.221, "r_y0": 178.86300000000006, "r_x1": 447.381, "r_y1": 178.86300000000006, "r_x2": 447.381, "r_y2": 170.538, "r_x3": 230.221, "r_y3": 170.538, "coord_origin": "TOPLEFT"}, "text": "authority is present, this has the same value as USER.", "orig": "authority is present, this has the same value as USER.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 142.801, "r_y0": 197.88300000000004, "r_x1": 209.743, "r_y1": 197.88300000000004, "r_x2": 209.743, "r_y2": 189.558, "r_x3": 142.801, "r_y3": 189.558, "coord_origin": "TOPLEFT"}, "text": "SYSTEM_USER", "orig": "SYSTEM_USER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 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157.522, "r_y0": 493.921, "r_x1": 209.1, "r_y1": 493.921, "r_x2": 209.1, "r_y2": 485.988, "r_x3": 157.522, "r_y3": 485.988, "coord_origin": "TOPLEFT"}, "text": "Owner = JOE", "orig": "Owner = JOE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 50, "label": "text", "bbox": {"l": 157.522, "t": 497.933, "r": 281.693, "b": 505.866, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 157.522, "r_y0": 505.866, "r_x1": 281.693, "r_y1": 505.866, "r_x2": 281.693, "r_y2": 497.933, "r_x3": 157.522, "r_y3": 497.933, "coord_origin": "TOPLEFT"}, "text": "SET OPTION USRPRF=*OWNER", "orig": "SET OPTION USRPRF=*OWNER", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 22, "label": "text", "bbox": {"l": 148.43, "t": 521.823, "r": 201.665, "b": 529.756, "coord_origin": "TOPLEFT"}, "confidence": 0.5350866913795471, "cells": [{"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 529.756, "r_x1": 201.665, "r_y1": 529.756, "r_x2": 201.665, "r_y2": 521.823, "r_x3": 148.43, "r_y3": 521.823, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 25, "label": "text", "bbox": {"l": 148.43, "t": 533.768, "r": 234.579, "b": 541.701, "coord_origin": "TOPLEFT"}, "confidence": 0.5008324384689331, "cells": [{"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 148.43, "r_y0": 541.701, "r_x1": 234.579, "r_y1": 541.701, "r_x2": 234.579, "r_y2": 533.768, "r_x3": 148.43, "r_y3": 533.768, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = JOE", "orig": "CURRENT USER = JOE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 24, "label": "text", "bbox": {"l": 138.476, "t": 566.616, "r": 232.566, "b": 586.495, "coord_origin": "TOPLEFT"}, "confidence": 0.5024971961975098, "cells": [{"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 574.55, "r_x1": 191.711, "r_y1": 574.55, "r_x2": 191.711, "r_y2": 566.616, "r_x3": 138.476, "r_y3": 566.616, "coord_origin": "TOPLEFT"}, "text": "USER = ALICE", "orig": "USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 138.476, "r_y0": 586.495, "r_x1": 232.566, "r_y1": 586.495, "r_x2": 232.566, "r_y2": 578.561, "r_x3": 138.476, "r_y3": 578.561, "coord_origin": "TOPLEFT"}, "text": "CURRENT USER = ALICE", "orig": "CURRENT USER = ALICE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 7, "page_no": 10, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9557498693466187, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 13, "page_no": 10, "cluster": {"id": 13, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9130511283874512, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "19", "orig": "19", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "19"}]}}, {"page_no": 11, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 118.86300000000006, "r_x1": 134.993, "r_y1": 118.86300000000006, "r_x2": 134.993, "r_y2": 110.53800000000001, "r_x3": 70.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Global variable", "orig": "Global variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, 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754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9187920093536377, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "20 ", "orig": "20 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9505079388618469, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 342.519, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8370978832244873, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "caption", "bbox": {"l": 64.8, "t": 93.49800000000005, "r": 201.196, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 322.201, "r": 384.345, "b": 336.964, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.529, "r": 547.543, "b": 435.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 547.386, "b": 481.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, "r": 458.427, "b": 503.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 511.546, "r": 406.048, "b": 520.759, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 396.957, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 536.577, "b": 566.719, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "code", "bbox": {"l": 151.2, "t": 574.9929999999999, "r": 451.016, "b": 641.3530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 118.86300000000006, "r_x1": 134.993, "r_y1": 118.86300000000006, "r_x2": 134.993, "r_y2": 110.53800000000001, "r_x3": 70.8, "r_y3": 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"TOPLEFT"}, "text": "Host name of the current client as returned by the system", "orig": "Host name of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 156.84299999999996, "r_x1": 140.671, "r_y1": 156.84299999999996, "r_x2": 140.671, "r_y2": 148.51800000000003, "r_x3": 70.8, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "CLIENT_IPADDR", "orig": "CLIENT_IPADDR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.872, "r_y0": 156.84299999999996, "r_x1": 267.083, "r_y1": 156.84299999999996, "r_x2": 267.083, "r_y2": 148.51800000000003, "r_x3": 202.872, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(128)", "orig": "VARCHAR(128)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.845, "r_y0": 156.84299999999996, "r_x1": 509.611, "r_y1": 156.84299999999996, "r_x2": 509.611, "r_y2": 148.51800000000003, "r_x3": 281.845, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "IP address of the current client as returned by the system", "orig": "IP address of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 175.86300000000006, "r_x1": 134.965, "r_y1": 175.86300000000006, "r_x2": 134.965, "r_y2": 167.538, "r_x3": 70.8, "r_y3": 167.538, "coord_origin": "TOPLEFT"}, "text": "CLIENT_PORT ", "orig": "CLIENT_PORT ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.903, "r_y0": 175.86300000000006, "r_x1": 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"coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 547.386, "b": 481.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, "r": 458.427, "b": 503.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 511.546, "r": 406.048, "b": 520.759, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 396.957, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 536.577, "b": 566.719, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.2, "t": 574.9929999999999, "r": 451.016, "b": 641.3530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 63.55636978149414, "t": 104.23387145996094, "r": 548.5687255859375, "b": 296.22467041015625, "coord_origin": "TOPLEFT"}, "confidence": 0.9868634939193726, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 118.86300000000006, "r_x1": 134.993, "r_y1": 118.86300000000006, "r_x2": 134.993, "r_y2": 110.53800000000001, "r_x3": 70.8, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Global variable", "orig": "Global variable", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.889, "r_y0": 118.86300000000006, "r_x1": 223.349, "r_y1": 118.86300000000006, "r_x2": 223.349, "r_y2": 110.53800000000001, "r_x3": 202.889, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Type", "orig": "Type", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.825, "r_y0": 118.86300000000006, "r_x1": 331.345, "r_y1": 118.86300000000006, "r_x2": 331.345, "r_y2": 110.53800000000001, "r_x3": 281.825, "r_y3": 110.53800000000001, "coord_origin": "TOPLEFT"}, "text": "Description", "orig": "Description", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 137.82299999999998, "r_x1": 132.729, "r_y1": 137.82299999999998, "r_x2": 132.729, "r_y2": 129.49800000000005, "r_x3": 70.8, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "CLIENT_HOST", "orig": "CLIENT_HOST", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.89, "r_y0": 137.82299999999998, "r_x1": 267.085, "r_y1": 137.82299999999998, "r_x2": 267.085, "r_y2": 129.49800000000005, "r_x3": 202.89, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(255)", "orig": "VARCHAR(255)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.847, "r_y0": 137.82299999999998, "r_x1": 510.176, "r_y1": 137.82299999999998, "r_x2": 510.176, "r_y2": 129.49800000000005, "r_x3": 281.847, "r_y3": 129.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Host name of the current client as returned by the system", "orig": "Host name of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 156.84299999999996, "r_x1": 140.671, "r_y1": 156.84299999999996, "r_x2": 140.671, "r_y2": 148.51800000000003, "r_x3": 70.8, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "CLIENT_IPADDR", "orig": "CLIENT_IPADDR", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 202.872, "r_y0": 156.84299999999996, "r_x1": 267.083, "r_y1": 156.84299999999996, "r_x2": 267.083, "r_y2": 148.51800000000003, "r_x3": 202.872, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "VARCHAR(128)", "orig": "VARCHAR(128)", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 281.845, "r_y0": 156.84299999999996, "r_x1": 509.611, "r_y1": 156.84299999999996, "r_x2": 509.611, "r_y2": 148.51800000000003, "r_x3": 281.845, "r_y3": 148.51800000000003, "coord_origin": "TOPLEFT"}, "text": "IP address of the current client as returned by the system", "orig": "IP address of the current client as returned by the system", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 70.8, "r_y0": 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10, "start_col_offset_idx": 2, "end_col_offset_idx": 3, "text": "Type of the currently running routine", "column_header": false, "row_header": false, "row_section": false}]}], "body": [{"label": "text", "id": 11, "page_no": 11, "cluster": {"id": 11, "label": "text", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 342.519, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8370978832244873, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 342.519, "r_y1": 80.72199999999998, "r_x2": 342.519, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 lists the nine built-in global variables.", "orig": "Table 3-2 lists the nine built-in global variables.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 lists the nine built-in global variables."}, {"label": "caption", "id": 10, "page_no": 11, "cluster": {"id": 10, "label": "caption", "bbox": {"l": 64.8, "t": 93.49800000000005, "r": 201.196, "b": 101.82299999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.913235604763031, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.82299999999998, "r_x1": 201.196, "r_y1": 101.82299999999998, "r_x2": 201.196, "r_y2": 93.49800000000005, "r_x3": 64.8, "r_y3": 93.49800000000005, "coord_origin": "TOPLEFT"}, "text": "Table 3-2 Built-in global variables", "orig": "Table 3-2 Built-in global variables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Table 3-2 Built-in global variables"}, {"label": "section_header", "id": 3, "page_no": 11, "cluster": {"id": 3, "label": "section_header", "bbox": {"l": 64.8, "t": 322.201, "r": 384.345, "b": 336.964, "coord_origin": "TOPLEFT"}, "confidence": 0.9645338654518127, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 336.964, "r_x1": 384.345, "r_y1": 336.964, "r_x2": 384.345, "r_y2": 322.201, "r_x3": 64.8, "r_y3": 322.201, "coord_origin": "TOPLEFT"}, "text": "3.3 VERIFY_GROUP_FOR_USER function", "orig": "3.3 VERIFY_GROUP_FOR_USER function", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.3 VERIFY_GROUP_FOR_USER function"}, {"label": "text", "id": 1, "page_no": 11, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 354.529, "r": 547.543, "b": 435.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9864333868026733, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 363.742, "r_x1": 542.844, "r_y1": 363.742, "r_x2": 542.844, "r_y2": 354.529, "r_x3": 136.8, "r_y3": 354.529, "coord_origin": "TOPLEFT"}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "orig": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 375.742, "r_x1": 547.338, "r_y1": 375.742, "r_x2": 547.338, "r_y2": 366.529, "r_x3": 136.8, "r_y3": 366.529, "coord_origin": "TOPLEFT"}, "text": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "orig": "intended for use with RCAC permissions and masks, it can be used in other SQL statements. ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 387.741, "r_x1": 547.46, "r_y1": 387.741, "r_x2": 547.46, "r_y2": 378.528, "r_x3": 136.8, "r_y3": 378.528, "coord_origin": "TOPLEFT"}, "text": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "orig": "The first parameter must be one of these three special registers: SESSION_USER, USER, or ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 399.741, "r_x1": 520.579, "r_y1": 399.741, "r_x2": 520.579, "r_y2": 390.528, "r_x3": 136.8, "r_y3": 390.528, "coord_origin": "TOPLEFT"}, "text": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "orig": "CURRENT_USER. The second and subsequent parameters are a list of user or group ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 411.741, "r_x1": 524.897, "r_y1": 411.741, "r_x2": 524.897, "r_y2": 402.528, "r_x3": 136.8, "r_y3": 402.528, "coord_origin": "TOPLEFT"}, "text": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "orig": "profiles. Each of these values must be 1 - 10 characters in length. These values are not ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 423.741, "r_x1": 547.543, "r_y1": 423.741, "r_x2": 547.543, "r_y2": 414.528, "r_x3": 136.8, "r_y3": 414.528, "coord_origin": "TOPLEFT"}, "text": "validated for their existence, which means that you can specify the names of user profiles that ", "orig": "validated for their existence, which means that you can specify the names of user profiles that ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 435.741, "r_x1": 342.016, "r_y1": 435.741, "r_x2": 342.016, "r_y2": 426.528, "r_x3": 136.8, "r_y3": 426.528, "coord_origin": "TOPLEFT"}, "text": "do not exist without receiving any kind of error.", "orig": "do not exist without receiving any kind of error.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error."}, {"label": "text", "id": 2, "page_no": 11, "cluster": {"id": 2, "label": "text", "bbox": {"l": 136.8, "t": 448.487, "r": 547.386, "b": 481.7, "coord_origin": "TOPLEFT"}, "confidence": 0.9807308912277222, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 457.7, "r_x1": 525.156, "r_y1": 457.7, "r_x2": 525.156, "r_y2": 448.487, "r_x3": 136.8, "r_y3": 448.487, "coord_origin": "TOPLEFT"}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile ", "orig": "If a special register value is in the list of user profiles or it is a member of a group profile ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 469.7, "r_x1": 547.386, "r_y1": 469.7, "r_x2": 547.386, "r_y2": 460.487, "r_x3": 136.8, "r_y3": 460.487, "coord_origin": "TOPLEFT"}, "text": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "orig": "included in the list, the function returns a long integer value of 1. Otherwise, it returns a value ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 481.7, "r_x1": 289.85, "r_y1": 481.7, "r_x2": 289.85, "r_y2": 472.487, "r_x3": 136.8, "r_y3": 472.487, "coord_origin": "TOPLEFT"}, "text": "of 0. It never returns the null value.", "orig": "of 0. It never returns the null value.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value."}, {"label": "text", "id": 8, "page_no": 11, "cluster": {"id": 8, "label": "text", "bbox": {"l": 136.8, "t": 494.507, "r": 458.427, "b": 503.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9237529635429382, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 503.72, "r_x1": 458.427, "r_y1": 503.72, "r_x2": 458.427, "r_y2": 494.507, "r_x3": 136.8, "r_y3": 494.507, "coord_origin": "TOPLEFT"}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "orig": "Here is an example of using the VERIFY_GROUP_FOR_USER function:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Here is an example of using the VERIFY_GROUP_FOR_USER function:"}, {"label": "list_item", "id": 7, "page_no": 11, "cluster": {"id": 7, "label": "list_item", "bbox": {"l": 136.8, "t": 511.546, "r": 406.048, "b": 520.759, "coord_origin": "TOPLEFT"}, "confidence": 0.9338629841804504, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 520.759, "r_x1": 145.089, "r_y1": 520.759, "r_x2": 145.089, "r_y2": 511.546, "r_x3": 136.8, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 520.759, "r_x1": 406.048, "r_y1": 520.759, "r_x2": 406.048, "r_y2": 511.546, "r_x3": 151.2, "r_y3": 511.546, "coord_origin": "TOPLEFT"}, "text": "There are user profiles for MGR, JANE, JUDY, and TONY.", "orig": "There are user profiles for MGR, JANE, JUDY, and TONY.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. There are user profiles for MGR, JANE, JUDY, and TONY."}, {"label": "list_item", "id": 4, "page_no": 11, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 136.8, "t": 528.5260000000001, "r": 396.957, "b": 537.739, "coord_origin": "TOPLEFT"}, "confidence": 0.9514460563659668, "cells": [{"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 537.739, "r_x1": 145.089, "r_y1": 537.739, "r_x2": 145.089, "r_y2": 528.5260000000001, "r_x3": 136.8, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 537.739, "r_x1": 396.957, "r_y1": 537.739, "r_x2": 396.957, "r_y2": 528.5260000000001, "r_x3": 151.2, "r_y3": 528.5260000000001, "coord_origin": "TOPLEFT"}, "text": "The user profile JANE specifies a group profile of MGR.", "orig": "The user profile JANE specifies a group profile of MGR.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. The user profile JANE specifies a group profile of MGR."}, {"label": "list_item", "id": 5, "page_no": 11, "cluster": {"id": 5, "label": "list_item", "bbox": {"l": 136.8, "t": 545.506, "r": 536.577, "b": 566.719, "coord_origin": "TOPLEFT"}, "confidence": 0.951230525970459, "cells": [{"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 554.719, "r_x1": 145.099, "r_y1": 554.719, "r_x2": 145.099, "r_y2": 545.506, "r_x3": 136.8, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 554.719, "r_x1": 536.577, "r_y1": 554.719, "r_x2": 536.577, "r_y2": 545.506, "r_x3": 151.21, "r_y3": 545.506, "coord_origin": "TOPLEFT"}, "text": "If a user is connected to the server using user profile JANE, all of the following function ", "orig": "If a user is connected to the server using user profile JANE, all of the following function ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 566.719, "r_x1": 286.852, "r_y1": 566.719, "r_x2": 286.852, "r_y2": 557.506, "r_x3": 151.2, "r_y3": 557.506, "coord_origin": "TOPLEFT"}, "text": "invocations return a value of 1:", "orig": "invocations return a value of 1:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1:"}, {"label": "code", "id": 12, "page_no": 11, "cluster": {"id": 12, "label": "code", "bbox": {"l": 151.2, "t": 574.9929999999999, "r": 451.016, "b": 641.3530000000001, "coord_origin": "TOPLEFT"}, "confidence": 0.7066455483436584, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 583.394, "r_x1": 366.057, "r_y1": 583.394, "r_x2": 366.057, "r_y2": 574.9929999999999, "r_x3": 151.2, "r_y3": 574.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 595.393, "r_x1": 406.017, "r_y1": 595.393, "r_x2": 406.017, "r_y2": 586.9929999999999, "r_x3": 151.2, "r_y3": 586.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 607.393, "r_x1": 451.016, "r_y1": 607.393, "r_x2": 451.016, "r_y2": 598.9929999999999, "r_x3": 151.2, "r_y3": 598.9929999999999, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 624.738, "r_x1": 385.844, "r_y1": 624.738, "r_x2": 385.844, "r_y2": 615.525, "r_x3": 151.2, "r_y3": 615.525, "coord_origin": "TOPLEFT"}, "text": "The following function invocation returns a value of 0:", "orig": "The following function invocation returns a value of 0:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 641.3530000000001, "r_x1": 411.057, "r_y1": 641.3530000000001, "r_x2": 411.057, "r_y2": 632.953, "r_x3": 151.2, "r_y3": 632.953, "coord_origin": "TOPLEFT"}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "orig": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY')"}, {"label": "table", "id": 0, "page_no": 11, "cluster": {"id": 0, "label": "table", "bbox": {"l": 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Row and Column Access Control ", "orig": "Chapter 3. 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DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . 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DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 564.375, "r_x1": 141.78, "r_y1": 564.375, "r_x2": 141.78, "r_y2": 555.975, "r_x3": 136.8, "r_y3": 555.975, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 521.574, "b": 260.355, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.35699999999997, "r_x1": 311.697, "r_y1": 116.35699999999997, "r_x2": 311.697, "r_y2": 107.957, "r_x3": 136.8, "r_y3": 107.957, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 164.356, "r_x1": 311.697, "r_y1": 164.356, "r_x2": 311.697, "r_y2": 155.95600000000002, "r_x3": 136.8, "r_y3": 155.95600000000002, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 547.281, "b": 296.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "list_item", "bbox": {"l": 152.039, "t": 304.485, "r": 469.118, "b": 313.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 152.039, "t": 321.525, "r": 403.932, "b": 330.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 545.118, "b": 359.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 529.442, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 526.555, "b": 684.373, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "27"}, {"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 521.574, "b": 260.355, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.35699999999997, "r_x1": 311.697, "r_y1": 116.35699999999997, "r_x2": 311.697, "r_y2": 107.957, "r_x3": 136.8, "r_y3": 107.957, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 164.356, "r_x1": 311.697, "r_y1": 164.356, "r_x2": 311.697, "r_y2": 155.95600000000002, "r_x3": 136.8, "r_y3": 155.95600000000002, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 547.281, "b": 296.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.039, "t": 304.485, "r": 469.118, "b": 313.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.039, "t": 321.525, "r": 403.932, "b": 330.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 545.118, "b": 359.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 529.442, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 526.555, "b": 684.373, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "body": [{"label": "text", "id": 13, "page_no": 12, "cluster": {"id": 13, "label": "text", "bbox": {"l": 136.8, "t": 71.957, "r": 166.739, "b": 80.35799999999995, "coord_origin": "TOPLEFT"}, "confidence": 0.5606335997581482, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.35799999999995, "r_x1": 166.739, "r_y1": 80.35799999999995, "r_x2": 166.739, "r_y2": 71.957, "r_x3": 136.8, "r_y3": 71.957, "coord_origin": "TOPLEFT"}, "text": "RETURN", "orig": "RETURN", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "RETURN"}, {"label": "text", "id": 12, "page_no": 12, "cluster": {"id": 12, "label": "text", "bbox": {"l": 136.8, "t": 83.957, "r": 156.779, "b": 92.35699999999997, "coord_origin": "TOPLEFT"}, "confidence": 0.589726984500885, "cells": [{"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 92.35699999999997, "r_x1": 156.779, "r_y1": 92.35699999999997, "r_x2": 156.779, "r_y2": 83.957, "r_x3": 136.8, "r_y3": 83.957, "coord_origin": "TOPLEFT"}, "text": "CASE", "orig": "CASE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CASE"}, {"label": "code", "id": 9, "page_no": 12, "cluster": {"id": 9, "label": "code", "bbox": {"l": 136.8, "t": 95.957, "r": 521.574, "b": 260.355, "coord_origin": "TOPLEFT"}, "confidence": 0.778521716594696, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 104.35699999999997, "r_x1": 466.615, "r_y1": 104.35699999999997, "r_x2": 466.615, "r_y2": 95.957, "r_x3": 136.8, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 116.35699999999997, "r_x1": 311.697, "r_y1": 116.35699999999997, "r_x2": 311.697, "r_y2": 107.957, "r_x3": 136.8, "r_y3": 107.957, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 140.35699999999997, "r_x1": 436.616, "r_y1": 140.35699999999997, "r_x2": 436.616, "r_y2": 131.95600000000002, "r_x3": 136.8, "r_y3": 131.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 152.356, "r_x1": 351.657, "r_y1": 152.356, "r_x2": 351.657, "r_y2": 143.95600000000002, "r_x3": 136.8, "r_y3": 143.95600000000002, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 164.356, "r_x1": 311.697, "r_y1": 164.356, "r_x2": 311.697, "r_y2": 155.95600000000002, "r_x3": 136.8, "r_y3": 155.95600000000002, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . DATE_OF_BIRTH", "orig": " THEN EMPLOYEES . DATE_OF_BIRTH", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 188.356, "r_x1": 436.616, "r_y1": 188.356, "r_x2": 436.616, "r_y2": 179.95600000000002, "r_x3": 136.8, "r_y3": 179.95600000000002, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 200.356, "r_x1": 356.637, "r_y1": 200.356, "r_x2": 356.637, "r_y2": 191.95500000000004, "r_x3": 136.8, "r_y3": 191.95500000000004, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 212.35500000000002, "r_x1": 521.574, "r_y1": 212.35500000000002, "r_x2": 521.574, "r_y2": 203.95500000000004, "r_x3": 136.8, "r_y3": 203.95500000000004, "coord_origin": "TOPLEFT"}, "text": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "orig": " THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' ||", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 224.35500000000002, "r_x1": 346.677, "r_y1": 224.35500000000002, "r_x2": 346.677, "r_y2": 215.95500000000004, "r_x3": 136.8, "r_y3": 215.95500000000004, "coord_origin": "TOPLEFT"}, "text": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "orig": " DAY (EMPLOYEES.DATE_OF_BIRTH ))", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 236.35500000000002, "r_x1": 206.759, "r_y1": 236.35500000000002, "r_x2": 206.759, "r_y2": 227.95500000000004, "r_x3": 136.8, "r_y3": 227.95500000000004, "coord_origin": "TOPLEFT"}, "text": " ELSE NULL", "orig": " ELSE NULL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 248.35500000000002, "r_x1": 156.779, "r_y1": 248.35500000000002, "r_x2": 156.779, "r_y2": 239.95500000000004, "r_x3": 136.8, "r_y3": 239.95500000000004, "coord_origin": "TOPLEFT"}, "text": " END", "orig": " END", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 260.355, "r_x1": 186.719, "r_y1": 260.355, "r_x2": 186.719, "r_y2": 251.95399999999995, "r_x3": 136.8, "r_y3": 251.95399999999995, "coord_origin": "TOPLEFT"}, "text": " ENABLE ;", "orig": " ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ;"}, {"label": "list_item", "id": 6, "page_no": 12, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 275.506, "r": 547.281, "b": 296.719, "coord_origin": "TOPLEFT"}, "confidence": 0.8678942918777466, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 284.719, "r_x1": 145.098, "r_y1": 284.719, "r_x2": 145.098, "r_y2": 275.506, "r_x3": 136.8, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.21, "r_y0": 284.719, "r_x1": 547.281, "r_y1": 284.719, "r_x2": 547.281, "r_y2": 275.506, "r_x3": 151.21, "r_y3": 275.506, "coord_origin": "TOPLEFT"}, "text": " The other column to mask in this example is the TAX_ID information. In this example, the ", "orig": " The other column to mask in this example is the TAX_ID information. In this example, the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 296.719, "r_x1": 339.373, "r_y1": 296.719, "r_x2": 339.373, "r_y2": 287.506, "r_x3": 151.2, "r_y3": 287.506, "coord_origin": "TOPLEFT"}, "text": "rules to enforce include the following ones:", "orig": "rules to enforce include the following ones:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones:"}, {"label": "list_item", "id": 4, "page_no": 12, "cluster": {"id": 4, "label": "list_item", "bbox": {"l": 152.039, "t": 304.485, "r": 469.118, "b": 313.698, "coord_origin": "TOPLEFT"}, "confidence": 0.9461130499839783, "cells": [{"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 313.698, "r_x1": 157.577, "r_y1": 313.698, "r_x2": 157.577, "r_y2": 304.485, "r_x3": 152.039, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 313.698, "r_x1": 469.118, "r_y1": 313.698, "r_x2": 469.118, "r_y2": 304.485, "r_x3": 165.599, "r_y3": 304.485, "coord_origin": "TOPLEFT"}, "text": "Human Resources can see the unmasked TAX_ID of the employees.", "orig": "Human Resources can see the unmasked TAX_ID of the employees.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Human Resources can see the unmasked TAX_ID of the employees."}, {"label": "list_item", "id": 3, "page_no": 12, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 152.039, "t": 321.525, "r": 403.932, "b": 330.738, "coord_origin": "TOPLEFT"}, "confidence": 0.9467733502388, "cells": [{"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 330.738, "r_x1": 157.577, "r_y1": 330.738, "r_x2": 157.577, "r_y2": 321.525, "r_x3": 152.039, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 330.738, "r_x1": 403.932, "r_y1": 330.738, "r_x2": 403.932, "r_y2": 321.525, "r_x3": 165.599, "r_y3": 321.525, "coord_origin": "TOPLEFT"}, "text": "Employees can see only their own unmasked TAX_ID.", "orig": "Employees can see only their own unmasked TAX_ID.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Employees can see only their own unmasked TAX_ID."}, {"label": "list_item", "id": 0, "page_no": 12, "cluster": {"id": 0, "label": "list_item", "bbox": {"l": 152.039, "t": 338.505, "r": 545.118, "b": 359.718, "coord_origin": "TOPLEFT"}, "confidence": 0.9705166220664978, "cells": [{"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 347.718, "r_x1": 157.577, "r_y1": 347.718, "r_x2": 157.577, "r_y2": 338.505, "r_x3": 152.039, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 347.718, "r_x1": 545.118, "r_y1": 347.718, "r_x2": 545.118, "r_y2": 338.505, "r_x3": 165.599, "r_y3": 338.505, "coord_origin": "TOPLEFT"}, "text": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "orig": "Managers see a masked version of TAX_ID with the first five characters replaced with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.599, "r_y0": 359.718, "r_x1": 364.664, "r_y1": 359.718, "r_x2": 364.664, "r_y2": 350.505, "r_x3": 165.599, "r_y3": 350.505, "coord_origin": "TOPLEFT"}, "text": "the X character (for example, XXX-XX-1234).", "orig": "the X character (for example, XXX-XX-1234).", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234)."}, {"label": "list_item", "id": 2, "page_no": 12, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 152.039, "t": 367.484, "r": 529.442, "b": 376.697, "coord_origin": "TOPLEFT"}, "confidence": 0.9469642639160156, "cells": [{"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.039, "r_y0": 376.697, "r_x1": 157.577, "r_y1": 376.697, "r_x2": 157.577, "r_y2": 367.484, "r_x3": 152.039, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "-", "orig": "-", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 165.6, "r_y0": 376.697, "r_x1": 529.442, "r_y1": 376.697, "r_x2": 529.442, "r_y2": 367.484, "r_x3": 165.6, "r_y3": 367.484, "coord_origin": "TOPLEFT"}, "text": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "orig": "Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "-Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX."}, {"label": "list_item", "id": 10, "page_no": 12, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 151.2, "t": 384.524, "r": 530.024, "b": 393.737, "coord_origin": "TOPLEFT"}, "confidence": 0.7003502249717712, "cells": [{"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 393.737, "r_x1": 530.024, "r_y1": 393.737, "r_x2": 530.024, "r_y2": 384.524, "r_x3": 151.2, "r_y3": 384.524, "coord_origin": "TOPLEFT"}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "orig": "To implement this column mask, run the SQL statement that is shown in Example 3-9.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "To implement this column mask, run the SQL statement that is shown in Example 3-9."}, {"label": "caption", "id": 7, "page_no": 12, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 136.8, "t": 406.518, "r": 351.999, "b": 414.843, "coord_origin": "TOPLEFT"}, "confidence": 0.8488203287124634, "cells": [{"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 414.843, "r_x1": 351.999, "r_y1": 414.843, "r_x2": 351.999, "r_y2": 406.518, "r_x3": 136.8, "r_y3": 406.518, "coord_origin": "TOPLEFT"}, "text": "Example 3-9 Creating a mask on the TAX_ID column", "orig": "Example 3-9 Creating a mask on the TAX_ID column", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-9 Creating a mask on the TAX_ID column"}, {"label": "code", "id": 8, "page_no": 12, "cluster": {"id": 8, "label": "code", "bbox": {"l": 136.8, "t": 423.977, "r": 526.555, "b": 684.373, "coord_origin": "TOPLEFT"}, "confidence": 0.8411229252815247, "cells": [{"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 432.377, "r_x1": 381.657, "r_y1": 432.377, "r_x2": 381.657, "r_y2": 423.977, "r_x3": 136.8, "r_y3": 423.977, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "orig": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 444.377, "r_x1": 371.637, "r_y1": 444.377, "r_x2": 371.637, "r_y2": 435.977, "r_x3": 136.8, "r_y3": 435.977, "coord_origin": "TOPLEFT"}, "text": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "orig": "ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 456.377, "r_x1": 261.718, "r_y1": 456.377, "r_x2": 261.718, "r_y2": 447.977, "r_x3": 136.8, "r_y3": 447.977, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN TAX_ID ", "orig": "FOR COLUMN TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 468.377, "r_x1": 176.76, "r_y1": 468.377, "r_x2": 176.76, "r_y2": 459.976, "r_x3": 136.8, "r_y3": 459.976, "coord_origin": "TOPLEFT"}, "text": "RETURN ", "orig": "RETURN ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 480.376, "r_x1": 161.76, "r_y1": 480.376, "r_x2": 161.76, "r_y2": 471.976, "r_x3": 136.8, "r_y3": 471.976, "coord_origin": "TOPLEFT"}, "text": "CASE ", "orig": "CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 492.376, "r_x1": 441.596, "r_y1": 492.376, "r_x2": 441.596, "r_y2": 483.976, "r_x3": 136.8, "r_y3": 483.976, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 504.376, "r_x1": 291.718, "r_y1": 504.376, "r_x2": 291.718, "r_y2": 495.976, "r_x3": 136.8, "r_y3": 495.976, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 528.376, "r_x1": 451.616, "r_y1": 528.376, "r_x2": 451.616, "r_y2": 519.975, "r_x3": 136.8, "r_y3": 519.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 540.375, "r_x1": 366.657, "r_y1": 540.375, "r_x2": 366.657, "r_y2": 531.975, "r_x3": 136.8, "r_y3": 531.975, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER = EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER = EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 552.375, "r_x1": 286.678, "r_y1": 552.375, "r_x2": 286.678, "r_y2": 543.975, "r_x3": 136.8, "r_y3": 543.975, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID", "orig": " THEN EMPLOYEES . TAX_ID", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 576.375, "r_x1": 446.636, "r_y1": 576.375, "r_x2": 446.636, "r_y2": 567.975, "r_x3": 136.8, "r_y3": 567.975, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 588.375, "r_x1": 371.637, "r_y1": 588.375, "r_x2": 371.637, "r_y2": 579.9739999999999, "r_x3": 136.8, "r_y3": 579.9739999999999, "coord_origin": "TOPLEFT"}, "text": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "orig": " AND SESSION_USER <> EMPLOYEES . USER_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 600.375, "r_x1": 526.555, "r_y1": 600.375, "r_x2": 526.555, "r_y2": 591.9739999999999, "r_x3": 136.8, "r_y3": 591.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "orig": " THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) )", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 624.374, "r_x1": 451.616, "r_y1": 624.374, "r_x2": 451.616, "r_y2": 615.9739999999999, "r_x3": 136.8, "r_y3": 615.9739999999999, "coord_origin": "TOPLEFT"}, "text": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "orig": " WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 636.374, "r_x1": 291.718, "r_y1": 636.374, "r_x2": 291.718, "r_y2": 627.9739999999999, "r_x3": 136.8, "r_y3": 627.9739999999999, "coord_origin": "TOPLEFT"}, "text": " THEN EMPLOYEES . TAX_ID ", "orig": " THEN EMPLOYEES . TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 660.374, "r_x1": 266.698, "r_y1": 660.374, "r_x2": 266.698, "r_y2": 651.973, "r_x3": 136.8, "r_y3": 651.973, "coord_origin": "TOPLEFT"}, "text": " ELSE 'XXX-XX-XXXX' ", "orig": " ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 672.373, "r_x1": 171.78, "r_y1": 672.373, "r_x2": 171.78, "r_y2": 663.973, "r_x3": 136.8, "r_y3": 663.973, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 684.373, "r_x1": 181.74, "r_y1": 684.373, "r_x2": 181.74, "r_y2": 675.973, "r_x3": 136.8, "r_y3": 675.973, "coord_origin": "TOPLEFT"}, "text": "ENABLE ;", "orig": "ENABLE ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ;"}], "headers": [{"label": "page_footer", "id": 1, "page_no": 12, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 344.94, "t": 755.538, "r": 523.584, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9556925892829895, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 344.94, "r_y0": 763.863, "r_x1": 523.584, "r_y1": 763.863, "r_x2": 523.584, "r_y2": 755.538, "r_x3": 344.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 3. Row and Column Access Control ", "orig": "Chapter 3. Row and Column Access Control ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 3. Row and Column Access Control"}, {"label": "page_footer", "id": 5, "page_no": 12, "cluster": {"id": 5, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9105783104896545, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "27", "orig": "27", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "27"}]}}, {"page_no": 13, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 443.699, "r_y0": 401.72, "r_x1": 446.468, "r_y1": 401.72, "r_x2": 446.468, "r_y2": 392.507, "r_x3": 443.699, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 522.418, "r_y0": 401.72, "r_x1": 525.187, "r_y1": 401.72, "r_x2": 525.187, "r_y2": 392.507, "r_x3": 522.418, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": " ", "orig": " ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 4, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 449.917, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "caption", "bbox": {"l": 64.8, "t": 173.538, "r": 293.146, "b": 181.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "section_header", "bbox": {"l": 64.8, "t": 202.375, "r": 203.983, "b": 214.36300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.529, "r": 547.209, "b": 261.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 409.443, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.957, "r": 376.677, "b": 329.357, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 31, "label": "text", "bbox": {"l": 136.8, "t": 332.957, "r": 291.718, "b": 341.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 32, "label": "text", "bbox": {"l": 136.8, "t": 344.956, "r": 271.678, "b": 353.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.956, "r": 291.718, "b": 365.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.508, "r": 540.81, "b": 413.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 64.8, "t": 649.038, "r": 347.441, "b": 657.363, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 449.917, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.8, "t": 173.538, "r": 293.146, "b": 181.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.8, "t": 202.375, "r": 203.983, "b": 214.36300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.529, "r": 547.209, "b": 261.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 409.443, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.957, "r": 376.677, "b": 329.357, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Column Access Control (masks) */"}, {"label": "text", "id": 31, "page_no": 13, "cluster": {"id": 31, "label": "text", "bbox": {"l": 136.8, "t": 332.957, "r": 291.718, "b": 341.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 32, "page_no": 13, "cluster": {"id": 32, "label": "text", "bbox": {"l": 136.8, "t": 344.956, "r": 271.678, "b": 353.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.956, "r": 291.718, "b": 365.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.508, "r": 540.81, "b": 413.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.8, "t": 649.038, "r": 347.441, "b": 657.363, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "list_item", "id": 10, "page_no": 13, "cluster": {"id": 10, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 449.917, "b": 80.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.8751048445701599, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.089, "r_y1": 80.72199999999998, "r_x2": 145.089, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 449.917, "r_y1": 80.72199999999998, "r_x2": 449.917, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "orig": "Figure 3-10 shows the masks that are created in the HR_SCHEMA.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Figure 3-10 shows the masks that are created in the HR_SCHEMA."}, {"label": "caption", "id": 7, "page_no": 13, "cluster": {"id": 7, "label": "caption", "bbox": {"l": 64.8, "t": 173.538, "r": 293.146, "b": 181.86300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.944426953792572, "cells": [{"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 181.86300000000006, "r_x1": 293.146, "r_y1": 181.86300000000006, "r_x2": 293.146, "r_y2": 173.538, "r_x3": 64.8, "r_y3": 173.538, "coord_origin": "TOPLEFT"}, "text": "Figure 3-10 Column masks shown in System i Navigator", "orig": "Figure 3-10 Column masks shown in System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-10 Column masks shown in System i Navigator"}, {"label": "section_header", "id": 2, "page_no": 13, "cluster": {"id": 2, "label": "section_header", "bbox": {"l": 64.8, "t": 202.375, "r": 203.983, "b": 214.36300000000006, "coord_origin": "TOPLEFT"}, "confidence": 0.9645015001296997, "cells": [{"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 214.36300000000006, "r_x1": 203.983, "r_y1": 214.36300000000006, "r_x2": 203.983, "r_y2": 202.375, "r_x3": 64.8, "r_y3": 202.375, "coord_origin": "TOPLEFT"}, "text": "3.6.6 Activating RCAC", "orig": "3.6.6 Activating RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3.6.6 Activating RCAC"}, {"label": "text", "id": 1, "page_no": 13, "cluster": {"id": 1, "label": "text", "bbox": {"l": 136.8, "t": 228.529, "r": 547.209, "b": 261.741, "coord_origin": "TOPLEFT"}, "confidence": 0.9768574237823486, "cells": [{"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 237.74199999999996, "r_x1": 537.1, "r_y1": 237.74199999999996, "r_x2": 537.1, "r_y2": 228.529, "r_x3": 136.8, "r_y3": 228.529, "coord_origin": "TOPLEFT"}, "text": "Now that you have created the row permission and the two column masks, RCAC must be ", "orig": "Now that you have created the row permission and the two column masks, RCAC must be ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 249.74199999999996, "r_x1": 529.214, "r_y1": 249.74199999999996, "r_x2": 529.214, "r_y2": 240.529, "r_x3": 136.8, "r_y3": 240.529, "coord_origin": "TOPLEFT"}, "text": "activated. The row permission and the two column masks are enabled (last clause in the ", "orig": "activated. The row permission and the two column masks are enabled (last clause in the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 261.741, "r_x1": 547.209, "r_y1": 261.741, "r_x2": 547.209, "r_y2": 252.52800000000002, "r_x3": 136.8, "r_y3": 252.52800000000002, "coord_origin": "TOPLEFT"}, "text": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "orig": "scripts), but now you must activate RCAC on the table. To do so, complete the following steps:", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps:"}, {"label": "list_item", "id": 9, "page_no": 13, "cluster": {"id": 9, "label": "list_item", "bbox": {"l": 136.8, "t": 269.50800000000004, "r": 409.443, "b": 278.721, "coord_origin": "TOPLEFT"}, "confidence": 0.9059441685676575, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 278.721, "r_x1": 145.089, "r_y1": 278.721, "r_x2": 145.089, "r_y2": 269.50800000000004, "r_x3": 136.8, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": "1.", "orig": "1.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 278.721, "r_x1": 409.443, "r_y1": 278.721, "r_x2": 409.443, "r_y2": 269.50800000000004, "r_x3": 151.2, "r_y3": 269.50800000000004, "coord_origin": "TOPLEFT"}, "text": " Run the SQL statements that are shown in Example 3-10.", "orig": " Run the SQL statements that are shown in Example 3-10.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "1. Run the SQL statements that are shown in Example 3-10."}, {"label": "section_header", "id": 11, "page_no": 13, "cluster": {"id": 11, "label": "section_header", "bbox": {"l": 136.8, "t": 291.558, "r": 375.273, "b": 299.883, "coord_origin": "TOPLEFT"}, "confidence": 0.657009482383728, "cells": [{"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 299.883, "r_x1": 375.273, "r_y1": 299.883, "r_x2": 375.273, "r_y2": 291.558, "r_x3": 136.8, "r_y3": 291.558, "coord_origin": "TOPLEFT"}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "orig": "Example 3-10 Activating RCAC on the EMPLOYEES table ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Example 3-10 Activating RCAC on the EMPLOYEES table"}, {"label": "list_item", "id": 13, "page_no": 13, "cluster": {"id": 13, "label": "list_item", "bbox": {"l": 136.8, "t": 308.957, "r": 376.677, "b": 317.357, "coord_origin": "TOPLEFT"}, "confidence": 0.6002400517463684, "cells": [{"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 317.357, "r_x1": 376.677, "r_y1": 317.357, "r_x2": 376.677, "r_y2": 308.957, "r_x3": 136.8, "r_y3": 308.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Row Access Control (permissions) */", "orig": "/* Active Row Access Control (permissions) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Row Access Control (permissions) */"}, {"label": "list_item", "id": 14, "page_no": 13, "cluster": {"id": 14, "label": "list_item", "bbox": {"l": 136.8, "t": 320.957, "r": 376.677, "b": 329.357, "coord_origin": "TOPLEFT"}, "confidence": 0.598584771156311, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 329.357, "r_x1": 376.677, "r_y1": 329.357, "r_x2": 376.677, "r_y2": 320.957, "r_x3": 136.8, "r_y3": 320.957, "coord_origin": "TOPLEFT"}, "text": "/* Active Column Access Control (masks) */", "orig": "/* Active Column Access Control (masks) */", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "/* Active Column Access Control (masks) */"}, {"label": "text", "id": 31, "page_no": 13, "cluster": {"id": 31, "label": "text", "bbox": {"l": 136.8, "t": 332.957, "r": 291.718, "b": 341.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 341.357, "r_x1": 291.718, "r_y1": 341.357, "r_x2": 291.718, "r_y2": 332.957, "r_x3": 136.8, "r_y3": 332.957, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "orig": "ALTER TABLE HR_SCHEMA.EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ALTER TABLE HR_SCHEMA.EMPLOYEES"}, {"label": "text", "id": 32, "page_no": 13, "cluster": {"id": 32, "label": "text", "bbox": {"l": 136.8, "t": 344.956, "r": 271.678, "b": 353.357, "coord_origin": "TOPLEFT"}, "confidence": 1.0, "cells": [{"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 353.357, "r_x1": 271.678, "r_y1": 353.357, "r_x2": 271.678, "r_y2": 344.956, "r_x3": 136.8, "r_y3": 344.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL", "orig": "ACTIVATE ROW ACCESS CONTROL", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE ROW ACCESS CONTROL"}, {"label": "text", "id": 15, "page_no": 13, "cluster": {"id": 15, "label": "text", "bbox": {"l": 136.8, "t": 356.956, "r": 291.718, "b": 365.356, "coord_origin": "TOPLEFT"}, "confidence": 0.5431806445121765, "cells": [{"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 365.356, "r_x1": 291.718, "r_y1": 365.356, "r_x2": 291.718, "r_y2": 356.956, "r_x3": 136.8, "r_y3": 356.956, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL;", "orig": "ACTIVATE COLUMN ACCESS CONTROL;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "ACTIVATE COLUMN ACCESS CONTROL;"}, {"label": "list_item", "id": 6, "page_no": 13, "cluster": {"id": 6, "label": "list_item", "bbox": {"l": 136.8, "t": 380.508, "r": 540.81, "b": 413.72, "coord_origin": "TOPLEFT"}, "confidence": 0.9449257850646973, "cells": [{"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 389.721, "r_x1": 145.12, "r_y1": 389.721, "r_x2": 145.12, "r_y2": 380.508, "r_x3": 136.8, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.231, "r_y0": 389.721, "r_x1": 540.81, "r_y1": 389.721, "r_x2": 540.81, "r_y2": 380.508, "r_x3": 151.231, "r_y3": 380.508, "coord_origin": "TOPLEFT"}, "text": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "orig": "Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 401.72, "r_x1": 387.371, "r_y1": 401.72, "r_x2": 387.371, "r_y2": 392.507, "r_x3": 151.2, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "the main navigation pane of System i Navigator, click ", "orig": "the main navigation pane of System i Navigator, click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 387.3, "r_y0": 401.72, "r_x1": 431.056, "r_y1": 401.72, "r_x2": 431.056, "r_y2": 392.507, "r_x3": 387.3, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "Schemas", "orig": "Schemas", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 433.86, "r_y0": 401.84, "r_x1": 443.691, "r_y1": 401.84, "r_x2": 443.691, "r_y2": 389.649, "r_x3": 433.86, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 446.519, "r_y0": 401.72, "r_x1": 509.728, "r_y1": 401.72, "r_x2": 509.728, "r_y2": 392.507, "r_x3": 446.519, "r_y3": 392.507, "coord_origin": "TOPLEFT"}, "text": "HR_SCHEMA", "orig": "HR_SCHEMA", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 512.579, "r_y0": 401.84, "r_x1": 522.409, "r_y1": 401.84, "r_x2": 522.409, "r_y2": 389.649, "r_x3": 512.579, "r_y3": 389.649, "coord_origin": "TOPLEFT"}, "text": "\uf0ae", "orig": "\uf0ae", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.198, "r_y0": 413.72, "r_x1": 181.935, "r_y1": 413.72, "r_x2": 181.935, "r_y2": 404.507, "r_x3": 151.198, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Tables", "orig": "Tables", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 181.798, "r_y0": 413.72, "r_x1": 248.946, "r_y1": 413.72, "r_x2": 248.946, "r_y2": 404.507, "r_x3": 181.798, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ", right-click the ", "orig": ", right-click the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 248.939, "r_y0": 413.72, "r_x1": 310.421, "r_y1": 413.72, "r_x2": 310.421, "r_y2": 404.507, "r_x3": 248.939, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "EMPLOYEES", "orig": "EMPLOYEES", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 310.498, "r_y0": 413.72, "r_x1": 381.542, "r_y1": 413.72, "r_x2": 381.542, "r_y2": 404.507, "r_x3": 310.498, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": " table, and click ", "orig": " table, and click ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 381.599, "r_y0": 413.72, "r_x1": 427.659, "r_y1": 413.72, "r_x2": 427.659, "r_y2": 404.507, "r_x3": 381.599, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": "Definition", "orig": "Definition", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 427.679, "r_y0": 413.72, "r_x1": 430.448, "r_y1": 413.72, "r_x2": 430.448, "r_y2": 404.507, "r_x3": 427.679, "r_y3": 404.507, "coord_origin": "TOPLEFT"}, "text": ".", "orig": ".", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas \uf0ae HR_SCHEMA \uf0ae Tables , right-click the EMPLOYEES table, and click Definition ."}, {"label": "caption", "id": 5, "page_no": 13, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 64.8, "t": 649.038, "r": 347.441, "b": 657.363, "coord_origin": "TOPLEFT"}, "confidence": 0.9499983787536621, "cells": [{"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 657.363, "r_x1": 347.441, "r_y1": 657.363, "r_x2": 347.441, "r_y2": 649.038, "r_x3": 64.8, "r_y3": 649.038, "coord_origin": "TOPLEFT"}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "orig": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 3-11 Selecting the EMPLOYEES table from System i Navigator"}, {"label": "picture", "id": 3, "page_no": 13, "cluster": {"id": 3, "label": "picture", "bbox": {"l": 63.801902770996094, "t": 95.38241577148438, "r": 547.11474609375, "b": 170.0321502685547, "coord_origin": "TOPLEFT"}, "confidence": 0.9635388851165771, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 0, "page_no": 13, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 63.985130310058594, "t": 427.90496826171875, "r": 530.0478515625, "b": 646.1396484375, "coord_origin": "TOPLEFT"}, "confidence": 0.9801459908485413, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 8, "page_no": 13, "cluster": {"id": 8, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 78.402, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9161998629570007, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 78.402, "r_y1": 763.863, "r_x2": 78.402, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "28 ", "orig": "28 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "28"}, {"label": "page_footer", "id": 4, "page_no": 13, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 93.42, "t": 755.538, "r": 334.258, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.9540064334869385, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 93.42, "r_y0": 763.863, "r_x1": 334.258, "r_y1": 763.863, "r_x2": 334.258, "r_y2": 755.538, "r_x3": 93.42, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}]}}, {"page_no": 14, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. The index being advised is for the ORDER BY clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 675.843, "r_x1": 227.119, "r_y1": 675.843, "r_x2": 227.119, "r_y2": 667.518, "r_x3": 64.8, "r_y3": 667.518, "coord_origin": "TOPLEFT"}, "text": "Figure 4-69 Index advice with no RCAC", "orig": "Figure 4-69 Index advice with no RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538, "r": 523.576, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.95576012134552, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 7, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 514.058, "b": 104.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.558, "r": 327.111, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.569, "r": 547.308, "b": 539.781, "coord_origin": "TOPLEFT"}, "confidence": 0.9766736626625061, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. The index being advised is for the ORDER BY clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 6, "label": "caption", "bbox": {"l": 64.8, "t": 667.518, "r": 227.119, "b": 675.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9514285922050476, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 675.843, "r_x1": 227.119, "r_y1": 675.843, "r_x2": 227.119, "r_y2": 667.518, "r_x3": 64.8, "r_y3": 667.518, "coord_origin": "TOPLEFT"}, "text": "Figure 4-69 Index advice with no RCAC", "orig": "Figure 4-69 Index advice with no RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.2491226196289, "r": 545.4508666992188, "b": 477.5412292480469, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797719120979309, "cells": [], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 4, "page_no": 14, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538, "r": 523.576, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.95576012134552, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "77"}, {"label": "list_item", "id": 3, "page_no": 14, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 514.058, "b": 104.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause."}, {"label": "caption", "id": 5, "page_no": 14, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.558, "r": 327.111, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"label": "list_item", "id": 2, "page_no": 14, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.569, "r": 547.308, "b": 539.781, "coord_origin": "TOPLEFT"}, "confidence": 0.9766736626625061, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. The index being advised is for the ORDER BY clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause."}, {"label": "caption", "id": 6, "page_no": 14, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 64.8, "t": 667.518, "r": 227.119, "b": 675.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9514285922050476, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 675.843, "r_x1": 227.119, "r_y1": 675.843, "r_x2": 227.119, "r_y2": 667.518, "r_x3": 64.8, "r_y3": 667.518, "coord_origin": "TOPLEFT"}, "text": "Figure 4-69 Index advice with no RCAC", "orig": "Figure 4-69 Index advice with no RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4-69 Index advice with no RCAC"}, {"label": "picture", "id": 0, "page_no": 14, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.2491226196289, "r": 545.4508666992188, "b": 477.5412292480469, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 14, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797719120979309, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "body": [{"label": "list_item", "id": 3, "page_no": 14, "cluster": {"id": 3, "label": "list_item", "bbox": {"l": 136.8, "t": 71.50900000000001, "r": 514.058, "b": 104.72199999999998, "coord_origin": "TOPLEFT"}, "confidence": 0.9761855006217957, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 80.72199999999998, "r_x1": 145.088, "r_y1": 80.72199999999998, "r_x2": 145.088, "r_y2": 71.50900000000001, "r_x3": 136.8, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "2.", "orig": "2.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 80.72199999999998, "r_x1": 513.368, "r_y1": 80.72199999999998, "r_x2": 513.368, "r_y2": 71.50900000000001, "r_x3": 151.2, "r_y3": 71.50900000000001, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "orig": "Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 92.72199999999998, "r_x1": 514.058, "r_y1": 92.72199999999998, "r_x2": 514.058, "r_y2": 83.50900000000001, "r_x3": 151.2, "r_y3": 83.50900000000001, "coord_origin": "TOPLEFT"}, "text": "enabled. It is clear that the implementation of the SQL statement is more complex ", "orig": "enabled. It is clear that the implementation of the SQL statement is more complex ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 104.72199999999998, "r_x1": 389.657, "r_y1": 104.72199999999998, "r_x2": 389.657, "r_y2": 95.50900000000001, "r_x3": 151.2, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": "because the row permission rule becomes part of the ", "orig": "because the row permission rule becomes part of the ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 389.579, "r_y0": 104.40499999999997, "r_x1": 414.539, "r_y1": 104.40499999999997, "r_x2": 414.539, "r_y2": 95.957, "r_x3": 389.579, "r_y3": 95.957, "coord_origin": "TOPLEFT"}, "text": "WHERE", "orig": "WHERE", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 414.6, "r_y0": 104.72199999999998, "r_x1": 448.87, "r_y1": 104.72199999999998, "r_x2": 448.87, "r_y2": 95.50900000000001, "r_x3": 414.6, "r_y3": 95.50900000000001, "coord_origin": "TOPLEFT"}, "text": " clause.", "orig": " clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause."}, {"label": "caption", "id": 5, "page_no": 14, "cluster": {"id": 5, "label": "caption", "bbox": {"l": 136.8, "t": 480.558, "r": 327.111, "b": 488.883, "coord_origin": "TOPLEFT"}, "confidence": 0.9529654383659363, "cells": [{"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 488.883, "r_x1": 327.111, "r_y1": 488.883, "r_x2": 327.111, "r_y2": 480.558, "r_x3": 136.8, "r_y3": 480.558, "coord_origin": "TOPLEFT"}, "text": "Figure 4-68 Visual Explain with RCAC enabled", "orig": "Figure 4-68 Visual Explain with RCAC enabled", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4-68 Visual Explain with RCAC enabled"}, {"label": "list_item", "id": 2, "page_no": 14, "cluster": {"id": 2, "label": "list_item", "bbox": {"l": 136.8, "t": 506.569, "r": 547.308, "b": 539.781, "coord_origin": "TOPLEFT"}, "confidence": 0.9766736626625061, "cells": [{"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 136.8, "r_y0": 515.7819999999999, "r_x1": 145.089, "r_y1": 515.7819999999999, "r_x2": 145.089, "r_y2": 506.569, "r_x3": 136.8, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "3.", "orig": "3.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 515.7819999999999, "r_x1": 543.646, "r_y1": 515.7819999999999, "r_x2": 543.646, "r_y2": 506.569, "r_x3": 151.2, "r_y3": 506.569, "coord_origin": "TOPLEFT"}, "text": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "orig": "Compare the advised indexes that are provided by the Optimizer without RCAC and with ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 527.781, "r_x1": 547.308, "r_y1": 527.781, "r_x2": 547.308, "r_y2": 518.568, "r_x3": 151.2, "r_y3": 518.568, "coord_origin": "TOPLEFT"}, "text": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "orig": "RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 151.2, "r_y0": 539.781, "r_x1": 430.257, "r_y1": 539.781, "r_x2": 430.257, "r_y2": 530.568, "r_x3": 151.2, "r_y3": 530.568, "coord_origin": "TOPLEFT"}, "text": "enabled. The index being advised is for the ORDER BY clause.", "orig": "enabled. The index being advised is for the ORDER BY clause.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause."}, {"label": "caption", "id": 6, "page_no": 14, "cluster": {"id": 6, "label": "caption", "bbox": {"l": 64.8, "t": 667.518, "r": 227.119, "b": 675.843, "coord_origin": "TOPLEFT"}, "confidence": 0.9514285922050476, "cells": [{"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 675.843, "r_x1": 227.119, "r_y1": 675.843, "r_x2": 227.119, "r_y2": 667.518, "r_x3": 64.8, "r_y3": 667.518, "coord_origin": "TOPLEFT"}, "text": "Figure 4-69 Index advice with no RCAC", "orig": "Figure 4-69 Index advice with no RCAC", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Figure 4-69 Index advice with no RCAC"}, {"label": "picture", "id": 0, "page_no": 14, "cluster": {"id": 0, "label": "picture", "bbox": {"l": 136.5016632080078, "t": 119.2491226196289, "r": 545.4508666992188, "b": 477.5412292480469, "coord_origin": "TOPLEFT"}, "confidence": 0.9864527583122253, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}, {"label": "picture", "id": 1, "page_no": 14, "cluster": {"id": 1, "label": "picture", "bbox": {"l": 64.27847290039062, "t": 553.5814819335938, "r": 506.39263916015625, "b": 664.0870971679688, "coord_origin": "TOPLEFT"}, "confidence": 0.9797719120979309, "cells": [], "children": []}, "text": "", "annotations": [], "provenance": null, "predicted_class": null, "confidence": null}], "headers": [{"label": "page_footer", "id": 4, "page_no": 14, "cluster": {"id": 4, "label": "page_footer", "bbox": {"l": 214.8, "t": 755.538, "r": 523.576, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.95576012134552, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 214.8, "r_y0": 763.863, "r_x1": 523.576, "r_y1": 763.863, "r_x2": 523.576, "r_y2": 755.538, "r_x3": 214.8, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "orig": "Chapter 4. Implementing Row and Column Access Control: Banking example ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Chapter 4. Implementing Row and Column Access Control: Banking example"}, {"label": "page_footer", "id": 7, "page_no": 14, "cluster": {"id": 7, "label": "page_footer", "bbox": {"l": 536.1, "t": 754.849, "r": 547.217, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.9056528806686401, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 536.1, "r_y0": 764.062, "r_x1": 547.217, "r_y1": 764.062, "r_x2": 547.217, "r_y2": 754.849, "r_x3": 536.1, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "77", "orig": "77", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "77"}]}}, {"page_no": 15, "size": {"width": 612.0, "height": 792.0}, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 83.982, "r_y1": 763.863, "r_x2": 83.982, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "124 ", "orig": "124 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.94, "r_y0": 763.863, "r_x1": 339.654, "r_y1": 763.863, "r_x2": 339.654, "r_y2": 755.538, "r_x3": 98.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 519.518, "r_x1": 123.24, "r_y1": 519.518, "r_x2": 123.24, "r_y2": 511.927, "r_x3": 64.8, "r_y3": 511.927, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 530.558, "r_x1": 96.24, "r_y1": 530.558, "r_x2": 96.24, "r_y2": 522.967, "r_x3": 64.8, "r_y3": 522.967, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 541.538, "r_x1": 124.14, "r_y1": 541.538, "r_x2": 124.14, "r_y2": 533.947, "r_x3": 79.2, "r_y3": 533.947, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 563.558, "r_x1": 500.697, "r_y1": 563.558, "r_x2": 500.697, "r_y2": 555.968, "r_x3": 64.8, "r_y3": 555.968, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 574.538, "r_x1": 281.399, "r_y1": 574.538, "r_x2": 281.399, "r_y2": 566.948, "r_x3": 79.2, "r_y3": 566.948, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 585.518, "r_x1": 137.64, "r_y1": 585.518, "r_x2": 137.64, "r_y2": 577.928, "r_x3": 79.2, "r_y3": 577.928, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 596.558, "r_x1": 361.378, "r_y1": 596.558, "r_x2": 361.378, "r_y2": 588.968, "r_x3": 64.8, "r_y3": 588.968, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 629.559, "r_x1": 258.059, "r_y1": 629.559, "r_x2": 258.059, "r_y2": 621.968, "r_x3": 64.8, "r_y3": 621.968, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "parsed_page": null, "predictions": {"layout": {"clusters": [{"id": 1, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 83.982, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8994001150131226, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 83.982, "r_y1": 763.863, "r_x2": 83.982, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "124 ", "orig": "124 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 0, "label": "page_footer", "bbox": {"l": 98.94, "t": 755.538, "r": 339.654, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.94, "r_y0": 763.863, "r_x1": 339.654, "r_y1": 763.863, "r_x2": 339.654, "r_y2": 755.538, "r_x3": 98.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, {"id": 2, "label": "code", "bbox": {"l": 64.8, "t": 71.94299999999998, "r": 500.697, "b": 706.539, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 519.518, "r_x1": 123.24, "r_y1": 519.518, "r_x2": 123.24, "r_y2": 511.927, "r_x3": 64.8, "r_y3": 511.927, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 530.558, "r_x1": 96.24, "r_y1": 530.558, "r_x2": 96.24, "r_y2": 522.967, "r_x3": 64.8, "r_y3": 522.967, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 541.538, "r_x1": 124.14, "r_y1": 541.538, "r_x2": 124.14, "r_y2": 533.947, "r_x3": 79.2, "r_y3": 533.947, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 563.558, "r_x1": 500.697, "r_y1": 563.558, "r_x2": 500.697, "r_y2": 555.968, "r_x3": 64.8, "r_y3": 555.968, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 574.538, "r_x1": 281.399, "r_y1": 574.538, "r_x2": 281.399, "r_y2": 566.948, "r_x3": 79.2, "r_y3": 566.948, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 585.518, "r_x1": 137.64, "r_y1": 585.518, "r_x2": 137.64, "r_y2": 577.928, "r_x3": 79.2, "r_y3": 577.928, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 596.558, "r_x1": 361.378, "r_y1": 596.558, "r_x2": 361.378, "r_y2": 588.968, "r_x3": 64.8, "r_y3": 588.968, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 629.559, "r_x1": 258.059, "r_y1": 629.559, "r_x2": 258.059, "r_y2": 621.968, "r_x3": 64.8, "r_y3": 621.968, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_footer", "id": 1, "page_no": 15, "cluster": {"id": 1, "label": "page_footer", "bbox": {"l": 64.8, "t": 754.849, "r": 83.982, "b": 764.062, "coord_origin": "TOPLEFT"}, "confidence": 0.8994001150131226, "cells": [{"index": 0, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 764.062, "r_x1": 83.982, "r_y1": 763.863, "r_x2": 83.982, "r_y2": 755.538, "r_x3": 64.8, "r_y3": 754.849, "coord_origin": "TOPLEFT"}, "text": "124 ", "orig": "124 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "124"}, {"label": "page_footer", "id": 0, "page_no": 15, "cluster": {"id": 0, "label": "page_footer", "bbox": {"l": 98.94, "t": 755.538, "r": 339.654, "b": 763.863, "coord_origin": "TOPLEFT"}, "confidence": 0.943077027797699, "cells": [{"index": 1, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 98.94, "r_y0": 763.863, "r_x1": 339.654, "r_y1": 763.863, "r_x2": 339.654, "r_y2": 755.538, "r_x3": 98.94, "r_y3": 755.538, "coord_origin": "TOPLEFT"}, "text": "Row and Column Access Control Support in IBM DB2 for i", "orig": "Row and Column Access Control Support in IBM DB2 for i", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "Row and Column Access Control Support in IBM DB2 for i"}, {"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.8, "t": 71.94299999999998, "r": 500.697, "b": 706.539, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 519.518, "r_x1": 123.24, "r_y1": 519.518, "r_x2": 123.24, "r_y2": 511.927, "r_x3": 64.8, "r_y3": 511.927, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 530.558, "r_x1": 96.24, "r_y1": 530.558, "r_x2": 96.24, "r_y2": 522.967, "r_x3": 64.8, "r_y3": 522.967, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 541.538, "r_x1": 124.14, "r_y1": 541.538, "r_x2": 124.14, "r_y2": 533.947, "r_x3": 79.2, "r_y3": 533.947, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 563.558, "r_x1": 500.697, "r_y1": 563.558, "r_x2": 500.697, "r_y2": 555.968, "r_x3": 64.8, "r_y3": 555.968, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 574.538, "r_x1": 281.399, "r_y1": 574.538, "r_x2": 281.399, "r_y2": 566.948, "r_x3": 79.2, "r_y3": 566.948, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 585.518, "r_x1": 137.64, "r_y1": 585.518, "r_x2": 137.64, "r_y2": 577.928, "r_x3": 79.2, "r_y3": 577.928, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 596.558, "r_x1": 361.378, "r_y1": 596.558, "r_x2": 361.378, "r_y2": 588.968, "r_x3": 64.8, "r_y3": 588.968, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 629.559, "r_x1": 258.059, "r_y1": 629.559, "r_x2": 258.059, "r_y2": 621.968, "r_x3": 64.8, "r_y3": 621.968, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ;"}], "body": [{"label": "code", "id": 2, "page_no": 15, "cluster": {"id": 2, "label": "code", "bbox": {"l": 64.8, "t": 71.94299999999998, "r": 500.697, "b": 706.539, "coord_origin": "TOPLEFT"}, "confidence": 0.7632161378860474, "cells": [{"index": 2, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 79.53399999999999, "r_x1": 177.119, "r_y1": 79.53399999999999, "r_x2": 177.119, "r_y2": 71.94299999999998, "r_x3": 64.8, "r_y3": 71.94299999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 3, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 90.51400000000001, "r_x1": 365.878, "r_y1": 90.51400000000001, "r_x2": 365.878, "r_y2": 82.923, "r_x3": 64.8, "r_y3": 82.923, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 4, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 101.49400000000003, "r_x1": 392.818, "r_y1": 101.49400000000003, "r_x2": 392.818, "r_y2": 93.90300000000002, "r_x3": 64.8, "r_y3": 93.90300000000002, "coord_origin": "TOPLEFT"}, "text": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "orig": "THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 5, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 112.53399999999999, "r_x1": 374.878, "r_y1": 112.53399999999999, "r_x2": 374.878, "r_y2": 104.94299999999998, "r_x3": 64.8, "r_y3": 104.94299999999998, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 6, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 123.51400000000001, "r_x1": 177.119, "r_y1": 123.51400000000001, "r_x2": 177.119, "r_y2": 115.923, "r_x3": 64.8, "r_y3": 115.923, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_TAX_ID ", "orig": "THEN C . CUSTOMER_TAX_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 7, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 134.49400000000003, "r_x1": 150.18, "r_y1": 134.49400000000003, "r_x2": 150.18, "r_y2": 126.90300000000002, "r_x3": 64.8, "r_y3": 126.90300000000002, "coord_origin": "TOPLEFT"}, "text": "ELSE 'XXX-XX-XXXX' ", "orig": "ELSE 'XXX-XX-XXXX' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 8, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 145.534, "r_x1": 96.24, "r_y1": 145.534, "r_x2": 96.24, "r_y2": 137.94399999999996, "r_x3": 64.8, "r_y3": 137.94399999999996, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 9, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 156.514, "r_x1": 124.14, "r_y1": 156.514, "r_x2": 124.14, "r_y2": 148.92399999999998, "r_x3": 79.2, "r_y3": 148.92399999999998, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 10, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 178.53499999999997, "r_x1": 460.258, "r_y1": 178.53499999999997, "r_x2": 460.258, "r_y2": 170.94399999999996, "r_x3": 64.8, "r_y3": 170.94399999999996, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 11, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 189.515, "r_x1": 272.459, "r_y1": 189.515, "r_x2": 272.459, "r_y2": 181.92399999999998, "r_x3": 79.2, "r_y3": 181.92399999999998, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 12, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 200.55499999999995, "r_x1": 137.64, "r_y1": 200.55499999999995, "r_x2": 137.64, "r_y2": 192.96400000000006, "r_x3": 79.2, "r_y3": 192.96400000000006, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 13, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 211.53499999999997, "r_x1": 361.378, "r_y1": 211.53499999999997, "r_x2": 361.378, "r_y2": 203.94399999999996, "r_x3": 64.8, "r_y3": 203.94399999999996, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 14, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 222.515, "r_x1": 249.059, "r_y1": 222.515, "r_x2": 249.059, "r_y2": 214.92399999999998, "r_x3": 64.8, "r_y3": 214.92399999999998, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 15, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 233.55499999999995, "r_x1": 365.878, "r_y1": 233.55499999999995, "r_x2": 365.878, "r_y2": 225.96500000000003, "r_x3": 64.8, "r_y3": 225.96500000000003, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 16, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 244.53499999999997, "r_x1": 249.059, "r_y1": 244.53499999999997, "r_x2": 249.059, "r_y2": 236.94500000000005, "r_x3": 64.8, "r_y3": 236.94500000000005, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 17, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 255.515, "r_x1": 374.878, "r_y1": 255.515, "r_x2": 374.878, "r_y2": 247.92499999999995, "r_x3": 64.8, "r_y3": 247.92499999999995, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 18, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 266.55499999999995, "r_x1": 249.059, "r_y1": 266.55499999999995, "r_x2": 249.059, "r_y2": 258.96500000000003, "r_x3": 64.8, "r_y3": 258.96500000000003, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "orig": "THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 19, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 277.53499999999997, "r_x1": 159.18, "r_y1": 277.53499999999997, "r_x2": 159.18, "r_y2": 269.94500000000005, "r_x3": 64.8, "r_y3": 269.94500000000005, "coord_origin": "TOPLEFT"}, "text": "ELSE '*************' ", "orig": "ELSE '*************' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 20, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 288.515, "r_x1": 96.24, "r_y1": 288.515, "r_x2": 96.24, "r_y2": 280.925, "r_x3": 64.8, "r_y3": 280.925, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 21, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 299.556, "r_x1": 124.14, "r_y1": 299.556, "r_x2": 124.14, "r_y2": 291.965, "r_x3": 79.2, "r_y3": 291.965, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 22, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 321.516, "r_x1": 428.818, "r_y1": 321.516, "r_x2": 428.818, "r_y2": 313.925, "r_x3": 64.8, "r_y3": 313.925, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 23, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 332.556, "r_x1": 209.519, "r_y1": 332.556, "r_x2": 209.519, "r_y2": 324.965, "r_x3": 79.2, "r_y3": 324.965, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_LOGIN_ID ", "orig": "FOR COLUMN CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 24, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 343.536, "r_x1": 137.64, "r_y1": 343.536, "r_x2": 137.64, "r_y2": 335.945, "r_x3": 79.2, "r_y3": 335.945, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 25, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 354.516, "r_x1": 361.378, "r_y1": 354.516, "r_x2": 361.378, "r_y2": 346.925, "r_x3": 64.8, "r_y3": 346.925, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 26, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 365.556, "r_x1": 186.119, "r_y1": 365.556, "r_x2": 186.119, "r_y2": 357.966, "r_x3": 64.8, "r_y3": 357.966, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 27, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 376.536, "r_x1": 374.878, "r_y1": 376.536, "r_x2": 374.878, "r_y2": 368.946, "r_x3": 64.8, "r_y3": 368.946, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 28, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 387.516, "r_x1": 186.119, "r_y1": 387.516, "r_x2": 186.119, "r_y2": 379.926, "r_x3": 64.8, "r_y3": 379.926, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_LOGIN_ID ", "orig": "THEN C . CUSTOMER_LOGIN_ID ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 29, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 398.557, "r_x1": 123.24, "r_y1": 398.557, "r_x2": 123.24, "r_y2": 390.966, "r_x3": 64.8, "r_y3": 390.966, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 30, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 409.537, "r_x1": 96.24, "r_y1": 409.537, "r_x2": 96.24, "r_y2": 401.946, "r_x3": 64.8, "r_y3": 401.946, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 31, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 420.517, "r_x1": 124.14, "r_y1": 420.517, "r_x2": 124.14, "r_y2": 412.926, "r_x3": 79.2, "r_y3": 412.926, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 32, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 442.537, "r_x1": 469.258, "r_y1": 442.537, "r_x2": 469.258, "r_y2": 434.946, "r_x3": 64.8, "r_y3": 434.946, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 33, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 453.517, "r_x1": 249.959, "r_y1": 453.517, "r_x2": 249.959, "r_y2": 445.926, "r_x3": 79.2, "r_y3": 445.926, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 34, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 464.557, "r_x1": 137.64, "r_y1": 464.557, "r_x2": 137.64, "r_y2": 456.967, "r_x3": 79.2, "r_y3": 456.967, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 35, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 475.537, "r_x1": 361.378, "r_y1": 475.537, "r_x2": 361.378, "r_y2": 467.947, "r_x3": 64.8, "r_y3": 467.947, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 36, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 486.517, "r_x1": 226.559, "r_y1": 486.517, "r_x2": 226.559, "r_y2": 478.927, "r_x3": 64.8, "r_y3": 478.927, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 37, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 497.558, "r_x1": 374.878, "r_y1": 497.558, "r_x2": 374.878, "r_y2": 489.967, "r_x3": 64.8, "r_y3": 489.967, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 38, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 508.538, "r_x1": 226.559, "r_y1": 508.538, "r_x2": 226.559, "r_y2": 500.947, "r_x3": 64.8, "r_y3": 500.947, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 39, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 519.518, "r_x1": 123.24, "r_y1": 519.518, "r_x2": 123.24, "r_y2": 511.927, "r_x3": 64.8, "r_y3": 511.927, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 40, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 530.558, "r_x1": 96.24, "r_y1": 530.558, "r_x2": 96.24, "r_y2": 522.967, "r_x3": 64.8, "r_y3": 522.967, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 541.538, "r_x1": 124.14, "r_y1": 541.538, "r_x2": 124.14, "r_y2": 533.947, "r_x3": 79.2, "r_y3": 533.947, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 563.558, "r_x1": 500.697, "r_y1": 563.558, "r_x2": 500.697, "r_y2": 555.968, "r_x3": 64.8, "r_y3": 555.968, "coord_origin": "TOPLEFT"}, "text": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "orig": "CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 574.538, "r_x1": 281.399, "r_y1": 574.538, "r_x2": 281.399, "r_y2": 566.948, "r_x3": 79.2, "r_y3": 566.948, "coord_origin": "TOPLEFT"}, "text": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 585.518, "r_x1": 137.64, "r_y1": 585.518, "r_x2": 137.64, "r_y2": 577.928, "r_x3": 79.2, "r_y3": 577.928, "coord_origin": "TOPLEFT"}, "text": "RETURN CASE ", "orig": "RETURN CASE ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 596.558, "r_x1": 361.378, "r_y1": 596.558, "r_x2": 361.378, "r_y2": 588.968, "r_x3": 64.8, "r_y3": 588.968, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 607.538, "r_x1": 258.059, "r_y1": 607.538, "r_x2": 258.059, "r_y2": 599.948, "r_x3": 64.8, "r_y3": 599.948, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 618.518, "r_x1": 374.878, "r_y1": 618.518, "r_x2": 374.878, "r_y2": 610.928, "r_x3": 64.8, "r_y3": 610.928, "coord_origin": "TOPLEFT"}, "text": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "orig": "WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 629.559, "r_x1": 258.059, "r_y1": 629.559, "r_x2": 258.059, "r_y2": 621.968, "r_x3": 64.8, "r_y3": 621.968, "coord_origin": "TOPLEFT"}, "text": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "orig": "THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 49, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 640.539, "r_x1": 123.24, "r_y1": 640.539, "r_x2": 123.24, "r_y2": 632.948, "r_x3": 64.8, "r_y3": 632.948, "coord_origin": "TOPLEFT"}, "text": "ELSE '*****' ", "orig": "ELSE '*****' ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 50, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 651.579, "r_x1": 96.24, "r_y1": 651.579, "r_x2": 96.24, "r_y2": 643.988, "r_x3": 64.8, "r_y3": 643.988, "coord_origin": "TOPLEFT"}, "text": "END ", "orig": "END ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 51, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 662.559, "r_x1": 124.14, "r_y1": 662.559, "r_x2": 124.14, "r_y2": 654.968, "r_x3": 79.2, "r_y3": 654.968, "coord_origin": "TOPLEFT"}, "text": "ENABLE ; ", "orig": "ENABLE ; ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 52, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 64.8, "r_y0": 684.579, "r_x1": 226.559, "r_y1": 684.579, "r_x2": 226.559, "r_y2": 676.989, "r_x3": 64.8, "r_y3": 676.989, "coord_origin": "TOPLEFT"}, "text": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "orig": "ALTER TABLE BANK_SCHEMA.CUSTOMERS ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 53, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 695.559, "r_x1": 214.019, "r_y1": 695.559, "r_x2": 214.019, "r_y2": 687.969, "r_x3": 79.2, "r_y3": 687.969, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE ROW ACCESS CONTROL ", "orig": "ACTIVATE ROW ACCESS CONTROL ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 54, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 79.2, "r_y0": 706.539, "r_x1": 223.019, "r_y1": 706.539, "r_x2": 223.019, "r_y2": 698.949, "r_x3": 79.2, "r_y3": 698.949, "coord_origin": "TOPLEFT"}, "text": "ACTIVATE COLUMN ACCESS CONTROL ;", "orig": "ACTIVATE COLUMN ACCESS CONTROL ;", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . 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This ", "orig": "access to data in a comprehensive and transparent way. This ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 41, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 299.592, "r_x1": 414.421, "r_y1": 299.592, "r_x2": 414.421, "r_y2": 290.728, "r_x3": 152.94, "r_y3": 290.728, "coord_origin": "TOPLEFT"}, "text": "publication helps you understand the capabilities of RCAC and provides ", "orig": "publication helps you understand the capabilities of RCAC and provides ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 42, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 311.592, "r_x1": 414.225, "r_y1": 311.592, "r_x2": 414.225, "r_y2": 302.728, "r_x3": 152.94, "r_y3": 302.728, "coord_origin": "TOPLEFT"}, "text": "examples of defining, creating, and implementing the row permissions ", "orig": "examples of defining, creating, and implementing the row permissions ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 43, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 323.592, "r_x1": 362.316, "r_y1": 323.592, "r_x2": 362.316, "r_y2": 314.727, "r_x3": 152.94, "r_y3": 314.727, "coord_origin": "TOPLEFT"}, "text": "and column masks in a relational database environment.", "orig": "and column masks in a relational database environment.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 340.572, "r_x1": 414.464, "r_y1": 340.572, "r_x2": 414.464, "r_y2": 331.707, "r_x3": 152.94, "r_y3": 331.707, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application ", "orig": "This paper is intended for database engineers, data-centric application ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 352.571, "r_x1": 407.6, "r_y1": 352.571, "r_x2": 407.6, "r_y2": 343.707, "r_x3": 152.94, "r_y3": 343.707, "coord_origin": "TOPLEFT"}, "text": "developers, and security officers who want to design and implement ", "orig": "developers, and security officers who want to design and implement ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 364.571, "r_x1": 399.286, "r_y1": 364.571, "r_x2": 399.286, "r_y2": 355.707, "r_x3": 152.94, "r_y3": 355.707, "coord_origin": "TOPLEFT"}, "text": "RCAC as a part of their data control and governance policy. A solid ", "orig": "RCAC as a part of their data control and governance policy. 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This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment."}, {"label": "text", "id": 0, "page_no": 17, "cluster": {"id": 0, "label": "text", "bbox": {"l": 152.94, "t": 331.707, "r": 414.464, "b": 388.571, "coord_origin": "TOPLEFT"}, "confidence": 0.9799742698669434, "cells": [{"index": 44, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 340.572, "r_x1": 414.464, "r_y1": 340.572, "r_x2": 414.464, "r_y2": 331.707, "r_x3": 152.94, "r_y3": 331.707, "coord_origin": "TOPLEFT"}, "text": "This paper is intended for database engineers, data-centric application ", "orig": "This paper is intended for database engineers, data-centric application ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 45, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 352.571, "r_x1": 407.6, "r_y1": 352.571, "r_x2": 407.6, "r_y2": 343.707, "r_x3": 152.94, "r_y3": 343.707, "coord_origin": "TOPLEFT"}, "text": "developers, and security officers who want to design and implement ", "orig": "developers, and security officers who want to design and implement ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 46, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 364.571, "r_x1": 399.286, "r_y1": 364.571, "r_x2": 399.286, "r_y2": 355.707, "r_x3": 152.94, "r_y3": 355.707, "coord_origin": "TOPLEFT"}, "text": "RCAC as a part of their data control and governance policy. A solid ", "orig": "RCAC as a part of their data control and governance policy. A solid ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 47, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 376.571, "r_x1": 414.05, "r_y1": 376.571, "r_x2": 414.05, "r_y2": 367.707, "r_x3": 152.94, "r_y3": 367.707, "coord_origin": "TOPLEFT"}, "text": "background in IBM i object level security, DB2 for i relational database ", "orig": "background in IBM i object level security, DB2 for i relational database ", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}, {"index": 48, "rgba": {"r": 0, "g": 0, "b": 0, "a": 255}, "rect": {"r_x0": 152.94, "r_y0": 388.571, "r_x1": 268.856, "r_y1": 388.571, "r_x2": 268.856, "r_y2": 379.706, "r_x3": 152.94, "r_y3": 379.706, "coord_origin": "TOPLEFT"}, "text": "concepts, and SQL is assumed.", "orig": "concepts, and SQL is assumed.", "text_direction": "left_to_right", "confidence": 1.0, "from_ocr": false}], "children": []}, "text": "This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. 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أن تؤدي البرمجة بلغة +تحسين الإنتاجية وحل المشكلات من خلال البرمجة بلغة R و Python +تعتبر البرمجة بلغة R و Python من الأدوات القوية التي يمكن أن تعزز الإنتاجية وتساعد في إيجاد حلول فعالة للمشكلات. يمتلك كل من R و Python ميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل على المحللين والعلماء إجراء تحليلات معقدة بطريقة سريعة وفعالة. إذا كان لديك عقلية تحليلية، فإن استخدام هذه اللغات يمكن أن يسهم بشكل كبير في تحسين نتائج العمل . +عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج الأنماط والتوجهات منها. يمكن للمبرمجين استخدام R و Python لتنفيذ عمليات تحليلية متقدمة، مثل النمذجة الإحصائية وتحليل البيانات الكبيرة. هذا ليس فقط يوفر الوقت، بل يمكن أن يؤدي أيضًا إلى اتخاذ قرارات أكثر دقة بناء ً على استنتاجات قائمة على البيانات . +علاوة على ذلك، توفر كل من R و Python مكتبات وأدوات غنية تدعم مجموعة واسعة م ن التطبيقات، من التحليل البياني إلى التعلم الآلي. يمكن للمستخدمين الاستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكلات المختلفة. على سبيل 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b/tests/data/groundtruth/docling_v2/right_to_left_01.md index f3717bd4..36b32048 100644 --- a/tests/data/groundtruth/docling_v2/right_to_left_01.md +++ b/tests/data/groundtruth/docling_v2/right_to_left_01.md @@ -1,9 +1,9 @@ -## تحسين الإنتاجية وحل المشكلات من خلال البرمجة بلغة +## تحسين الإنتاجية وحل المشكلات من خلال البرمجة بلغة R و Python -تعتبر البرمجة بلغة +تعتبر البرمجة بلغة R و Python من الأدوات القوية التي يمكن أن تعزز الإنتاجية وتساعد في إيجاد حلول فعالة للمشكلات. يمتلك كل من R و Python ميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل على المحللين والعلماء إجراء تحليلات معقدة بطريقة سريعة وفعالة. إذا كان لديك عقلية تحليلية، فإن استخدام هذه اللغات يمكن أن يسهم بشكل كبير في تحسين نتائج العمل . -عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج +عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج الأنماط والتوجهات منها. يمكن للمبرمجين استخدام R و Python لتنفيذ عمليات تحليلية متقدمة، مثل النمذجة الإحصائية وتحليل البيانات الكبيرة. هذا ليس فقط يوفر الوقت، بل يمكن أن يؤدي أيضًا إلى اتخاذ قرارات أكثر دقة بناء ً على استنتاجات قائمة على البيانات . -علاوة على ذلك، توفر كل من +علاوة على ذلك، توفر كل من R و Python مكتبات وأدوات غنية تدعم مجموعة واسعة م ن التطبيقات، من التحليل البياني إلى التعلم الآلي. يمكن للمستخدمين الاستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكلات المختلفة. على سبيل المثال، يمكن استخدام مكتبة pandas في Python لإدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرسم البياني والتحليل الإ حصائي، مما يجعلها مثالية للباحثين والمحللين . -في النهاية، يمكن أن تؤدي البرمجة بلغة \ No newline at end of file +في النهاية، يمكن أن تؤدي البرمجة بلغة R و Python مع عقلية تحليلية إلى تحسين الإنتاجية وتوفير حلول مبتكرة للمشكلات المعقدة. إن القدرة على تحليل البيانات بشكل فعال وتطبيق الأساليب البرمجية المناسبة يمكن أن تكون له ا تأثيرات إيجابية بعيدة المدى على الأداء الشخصي والمهني . \ No newline at end of file diff --git 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b/tests/data/groundtruth/docling_v2/right_to_left_02.doctags.txt index 873cb801..0c286071 100644 --- a/tests/data/groundtruth/docling_v2/right_to_left_02.doctags.txt +++ b/tests/data/groundtruth/docling_v2/right_to_left_02.doctags.txt @@ -1,7 +1,7 @@ -وعليه، -ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور -مايــــــــة اممــــــــن -المصــــــــــــــــــــر -تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ +11 +وعليه، فإن الحكومة المصرية تضع صوو عييهاوخ لوال المر اوة الم باوة تكايف السيد رئيس الجماورية لاخ بخلعمل ياى تح يق يدد من الأ هودا ياى رعساخ : وضع ماف بهخء الإنسخن المصري ياى رعس قخئموة الأولويو خت، لخصووة فووا مجووخحت الصووحة واللعاوويل، العموول ياووى تح يووق معوودحت نمووو قويوووة ومسووولدامة و وووخماة فوووا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددات الأمون ال ووما المصوري فوا ضووء اللحوديخت الإقايميوة والدوليو ة، ومواصوواة واووود تلوووير الماووخر ة السيخسووية، واسوولمرار ملخبعووة ما و و خت الأمووون واحسووول رار ومكخفحوووة الإرهوووخ ، تلووووير ما وووخت ال خفوووة والوووويا الوووو،ها، والبلوووخ الوووديها المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل الموا،هة والسام المجلمعا . +ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور ( 2024 -2026 ) تح يق عربعة عهدا اسلراتيجية رئيسة، وها ياى الهحو الآتا : +مايــــــــة اممــــــــن القومي المصـر بنــــاء ا نســــا المصــــــــــــــــــــر بنـــــاء ا تصـــــاع تنابســــــــــــــــــــــي تحقيق اظستق رار السياســــــــــــــــــــــــي +تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ د باوكل رئووويس ياوووى مسووولادفخت ر يوووة مصووور 2023 ، وتوصووويخت واسوووخت الحووووار الوو،ها، ومسولادفخت الوو ارات، والبرنوخما الوو،ها ليصوا خت الايك ايوة، ومبلاف احسلراتيجيخت الو،هية . \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/right_to_left_02.json b/tests/data/groundtruth/docling_v2/right_to_left_02.json index ed5970d0..a2d84c8c 100644 --- a/tests/data/groundtruth/docling_v2/right_to_left_02.json +++ 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\ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/right_to_left_02.md b/tests/data/groundtruth/docling_v2/right_to_left_02.md index 805d7f10..0a03abe3 100644 --- a/tests/data/groundtruth/docling_v2/right_to_left_02.md +++ b/tests/data/groundtruth/docling_v2/right_to_left_02.md @@ -1,11 +1,11 @@ -وعليه، +11 -ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور +وعليه، فإن الحكومة المصرية تضع صوو عييهاوخ لوال المر اوة الم باوة تكايف السيد رئيس الجماورية لاخ بخلعمل ياى تح يق يدد من الأ هودا ياى رعساخ : وضع ماف بهخء الإنسخن المصري ياى رعس قخئموة الأولويو خت، لخصووة فووا مجووخحت الصووحة واللعاوويل، العموول ياووى تح يووق معوودحت نمووو قويوووة ومسووولدامة و وووخماة فوووا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددات الأمون ال ووما المصوري فوا ضووء اللحوديخت الإقايميوة والدوليو ة، ومواصوواة واووود تلوووير الماووخر ة السيخسووية، واسوولمرار ملخبعووة ما و و خت الأمووون واحسووول رار ومكخفحوووة الإرهوووخ ، تلووووير ما وووخت ال خفوووة والوووويا الوووو،ها، والبلوووخ الوووديها المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل الموا،هة والسام المجلمعا . -مايــــــــة اممــــــــن +ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور ( 2024 -2026 ) تح يق عربعة عهدا اسلراتيجية رئيسة، وها ياى الهحو الآتا : -المصــــــــــــــــــــر +مايــــــــة اممــــــــن القومي المصـر بنــــاء ا نســــا المصــــــــــــــــــــر بنـــــاء ا تصـــــاع تنابســــــــــــــــــــــي تحقيق اظستق رار السياســــــــــــــــــــــــي -تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ +تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ د باوكل رئووويس ياوووى مسووولادفخت ر يوووة مصووور 2023 ، وتوصووويخت واسوووخت الحووووار الوو،ها، ومسولادفخت الوو ارات، والبرنوخما الوو،ها ليصوا خت الايك ايوة، ومبلاف احسلراتيجيخت الو،هية . \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/right_to_left_02.pages.json b/tests/data/groundtruth/docling_v2/right_to_left_02.pages.json index 93b7e9c7..d64dd1c9 100644 --- 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a/tests/data/groundtruth/docling_v2/right_to_left_03.md +++ b/tests/data/groundtruth/docling_v2/right_to_left_03.md @@ -1,61 +1,61 @@ -## اميد +## اميد نامه پذيرش در بازار اصلی -کالا ی داخلی -## 2-5 - -شمشه و شمشال توليد شده به روش ريخته گری +## 2-5 -استاندارد ک الا نام استاندارد -20300 +شمشه و شمشال توليد شده به روش ريخته گری پيوسته مورد مصرف در فولادهای سازه ای - مطابق آناليز پيوست شماره استاندارد ملی -خير - -بلی +20300 استاندارد اجباری است؟ -سازمان ملی استاندارد ايران +بلی + +خير مرجع صادرکننده استاندارد +سازمان ملی استاندارد ايران + +آيا توليدکننده محصول، استاندارد مذکور را اخذ نموده است؟ + بلی خير -آيا توليدکننده محصول، استاندارد مذکور را اخذ - -## 3 - -19 +## 3 -پذيرش در بورس تاريخ ارائه مدارک -04 +19 / 09 / 1403 تاريخ پذيرش +04 / 10 / 1403 + +شماره جلسه کميته عرضه + 436 -شماره جلسه - -05 - تاريخ درج اميدنامه -کارگزاری +05 / 10 / 1403 مشاور پذيرش -بر +کارگزاری آ رمون بورس -نحوة تعيين قيمت پايه پس از پذيرش کالا در +نحوة تعيين قيمت پايه پس از پذيرش کالا در بورس -حداقل +بر اساس قيمت 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"origin": { "mimetype": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", diff --git a/tests/data/groundtruth/docling_v2/test_emf_docx.docx.json b/tests/data/groundtruth/docling_v2/test_emf_docx.docx.json index 6ac98517..bb8807f6 100644 --- a/tests/data/groundtruth/docling_v2/test_emf_docx.docx.json +++ b/tests/data/groundtruth/docling_v2/test_emf_docx.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "test_emf_docx", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", diff --git a/tests/data/groundtruth/docling_v2/unit_test_01.html.json b/tests/data/groundtruth/docling_v2/unit_test_01.html.json index 38267cf9..180188a7 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_01.html.json +++ b/tests/data/groundtruth/docling_v2/unit_test_01.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "unit_test_01", "origin": { "mimetype": "text/html", diff --git a/tests/data/groundtruth/docling_v2/unit_test_01.html.md b/tests/data/groundtruth/docling_v2/unit_test_01.html.md index 38cbbbcf..63f0c203 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_01.html.md +++ b/tests/data/groundtruth/docling_v2/unit_test_01.html.md @@ -1,13 +1,13 @@ # Title -## section-1 +### section-1 -### section-1.1 +#### section-1.1 -## section-2 +### section-2 -#### section-2.0.1 +##### section-2.0.1 -### section-2.2 +#### section-2.2 -### section-2.3 \ No newline at end of file +#### section-2.3 \ No newline at end of file diff --git a/tests/data/groundtruth/docling_v2/unit_test_headers.docx.json b/tests/data/groundtruth/docling_v2/unit_test_headers.docx.json index 98e3c3e1..91033ea0 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_headers.docx.json +++ b/tests/data/groundtruth/docling_v2/unit_test_headers.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "unit_test_headers", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", diff --git a/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json index bae9059f..24df7f24 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json +++ b/tests/data/groundtruth/docling_v2/unit_test_headers_numbered.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "unit_test_headers_numbered", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", diff --git a/tests/data/groundtruth/docling_v2/unit_test_lists.docx.json b/tests/data/groundtruth/docling_v2/unit_test_lists.docx.json index be6b8a57..bc730663 100644 --- a/tests/data/groundtruth/docling_v2/unit_test_lists.docx.json +++ b/tests/data/groundtruth/docling_v2/unit_test_lists.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "unit_test_lists", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", diff --git a/tests/data/groundtruth/docling_v2/wiki_duck.html.json b/tests/data/groundtruth/docling_v2/wiki_duck.html.json index 8e4e868d..5a4928be 100644 --- a/tests/data/groundtruth/docling_v2/wiki_duck.html.json +++ b/tests/data/groundtruth/docling_v2/wiki_duck.html.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "wiki_duck", "origin": { "mimetype": "text/html", diff --git a/tests/data/groundtruth/docling_v2/wiki_duck.html.md b/tests/data/groundtruth/docling_v2/wiki_duck.html.md index d43d7775..85a0908a 100644 --- a/tests/data/groundtruth/docling_v2/wiki_duck.html.md +++ b/tests/data/groundtruth/docling_v2/wiki_duck.html.md @@ -206,7 +206,7 @@ Duck is the common name for numerous species of waterfowl in the family Anatidae Ducks are sometimes confused with several types of unrelated water birds with similar forms, such as loons or divers, grebes, gallinules and coots. -## Etymology +### Etymology The word duck comes from Old English dūce 'diver', a derivative of the verb *dūcan 'to duck, bend down low as if to get under something, or dive', because of the way many species in the dabbling duck group feed by upending; compare with Dutch duiken and German tauchen 'to dive'. @@ -228,7 +228,7 @@ Wood ducks. -## Taxonomy +### Taxonomy All ducks belong to the biological order Anseriformes, a group that contains the ducks, geese and swans, as well as the screamers, and the magpie goose.[5] All except the screamers belong to the biological family Anatidae.[5] Within the family, ducks are split into a variety of subfamilies and 'tribes'. The number and composition of these subfamilies and tribes is the cause of considerable disagreement among taxonomists.[5] Some base their decisions on morphological characteristics, others on shared behaviours or genetic studies.[6][7] The number of suggested subfamilies containing ducks ranges from two to five.[8][9] The significant level of hybridisation that occurs among wild ducks complicates efforts to tease apart the relationships between various species.[9] @@ -240,7 +240,7 @@ In most modern classifications, the so-called 'true ducks' belong to the subfami A number of other species called ducks are not considered to be 'true ducks', and are typically placed in other subfamilies or tribes. The whistling ducks are assigned either to a tribe (Dendrocygnini) in the subfamily Anatinae or the subfamily Anserinae,[15] or to their own subfamily (Dendrocygninae) or family (Dendrocyganidae).[9][16] The freckled duck of Australia is either the sole member of the tribe Stictonettini in the subfamily Anserinae,[15] or in its own family, the Stictonettinae.[9] The shelducks make up the tribe Tadornini in the family Anserinae in some classifications,[15] and their own subfamily, Tadorninae, in others,[17] while the steamer ducks are either placed in the family Anserinae in the tribe Tachyerini[15] or lumped with the shelducks in the tribe Tadorini.[9] The perching ducks make up in the tribe Cairinini in the subfamily Anserinae in some classifications, while that tribe is eliminated in other classifications and its members assigned to the tribe Anatini.[9] The torrent duck is generally included in the subfamily Anserinae in the monotypic tribe Merganettini,[15] but is sometimes included in the tribe Tadornini.[18] The pink-eared duck is sometimes included as a true duck either in the tribe Anatini[15] or the tribe Malacorhynchini,[19] and other times is included with the shelducks in the tribe Tadornini.[15] -## Morphology +### Morphology Male Mandarin duck @@ -250,7 +250,7 @@ The overall body plan of ducks is elongated and broad, and they are also relativ The drakes of northern species often have extravagant plumage, but that is moulted in summer to give a more female-like appearance, the "eclipse" plumage. Southern resident species typically show less sexual dimorphism, although there are exceptions such as the paradise shelduck of New Zealand, which is both strikingly sexually dimorphic and in which the female's plumage is brighter than that of the male. The plumage of juvenile birds generally resembles that of the female. Female ducks have evolved to have a corkscrew shaped vagina to prevent rape. -## Distribution and habitat +### Distribution and habitat Flying steamer ducks in Ushuaia, Argentina @@ -264,9 +264,9 @@ Female mallard in Cornwall, England Some duck species, mainly those breeding in the temperate and Arctic Northern Hemisphere, are migratory; those in the tropics are generally not. Some ducks, particularly in Australia where rainfall is erratic, are nomadic, seeking out the temporary lakes and pools that form after localised heavy rain.[23] -## Behaviour +### Behaviour -### Feeding +#### Feeding Pecten along the bill @@ -288,7 +288,7 @@ The others have the characteristic wide flat bill adapted to dredging-type jobs The Guardian published an article advising that ducks should not be fed with bread because it damages the health of the ducks and pollutes waterways.[25] -### Breeding +#### Breeding A Muscovy duckling @@ -296,13 +296,13 @@ A Muscovy duckling Ducks generally only have one partner at a time, although the partnership usually only lasts one year.[26] Larger species and the more sedentary species (like fast-river specialists) tend to have pair-bonds that last numerous years.[27] Most duck species breed once a year, choosing to do so in favourable conditions (spring/summer or wet seasons). Ducks also tend to make a nest before breeding, and, after hatching, lead their ducklings to water. Mother ducks are very caring and protective of their young, but may abandon some of their ducklings if they are physically stuck in an area they cannot get out of (such as nesting in an enclosed courtyard) or are not prospering due to genetic defects or sickness brought about by hypothermia, starvation, or disease. Ducklings can also be orphaned by inconsistent late hatching where a few eggs hatch after the mother has abandoned the nest and led her ducklings to water.[28] -### Communication +#### Communication Female mallard ducks (as well as several other species in the genus Anas, such as the American and Pacific black ducks, spot-billed duck, northern pintail and common teal) make the classic "quack" sound while males make a similar but raspier sound that is sometimes written as "breeeeze",[29][self-published source?] but, despite widespread misconceptions, most species of duck do not "quack".[30] In general, ducks make a range of calls, including whistles, cooing, yodels and grunts. For example, the scaup – which are diving ducks – make a noise like "scaup" (hence their name). Calls may be loud displaying calls or quieter contact calls. A common urban legend claims that duck quacks do not echo; however, this has been proven to be false. This myth was first debunked by the Acoustics Research Centre at the University of Salford in 2003 as part of the British Association's Festival of Science.[31] It was also debunked in one of the earlier episodes of the popular Discovery Channel television show MythBusters.[32] -### Predators +#### Predators Ringed teal @@ -312,15 +312,15 @@ Ducks have many predators. Ducklings are particularly vulnerable, since their in Adult ducks are fast fliers, but may be caught on the water by large aquatic predators including big fish such as the North American muskie and the European pike. In flight, ducks are safe from all but a few predators such as humans and the peregrine falcon, which uses its speed and strength to catch ducks. -## Relationship with humans +### Relationship with humans -### Hunting +#### Hunting Humans have hunted ducks since prehistoric times. Excavations of middens in California dating to 7800 – 6400 BP have turned up bones of ducks, including at least one now-extinct flightless species.[33] Ducks were captured in "significant numbers" by Holocene inhabitants of the lower Ohio River valley, suggesting they took advantage of the seasonal bounty provided by migrating waterfowl.[34] Neolithic hunters in locations as far apart as the Caribbean,[35] Scandinavia,[36] Egypt,[37] Switzerland,[38] and China relied on ducks as a source of protein for some or all of the year.[39] Archeological evidence shows that Māori people in New Zealand hunted the flightless Finsch's duck, possibly to extinction, though rat predation may also have contributed to its fate.[40] A similar end awaited the Chatham duck, a species with reduced flying capabilities which went extinct shortly after its island was colonised by Polynesian settlers.[41] It is probable that duck eggs were gathered by Neolithic hunter-gathers as well, though hard evidence of this is uncommon.[35][42] In many areas, wild ducks (including ducks farmed and released into the wild) are hunted for food or sport,[43] by shooting, or by being trapped using duck decoys. Because an idle floating duck or a duck squatting on land cannot react to fly or move quickly, "a sitting duck" has come to mean "an easy target". These ducks may be contaminated by pollutants such as PCBs.[44] -### Domestication +#### Domestication Indian Runner ducks, a common breed of domestic ducks @@ -328,7 +328,7 @@ Indian Runner ducks, a common breed of domestic ducks Ducks have many economic uses, being farmed for their meat, eggs, and feathers (particularly their down). Approximately 3 billion ducks are slaughtered each year for meat worldwide.[45] They are also kept and bred by aviculturists and often displayed in zoos. Almost all the varieties of domestic ducks are descended from the mallard (Anas platyrhynchos), apart from the Muscovy duck (Cairina moschata).[46][47] The Call duck is another example of a domestic duck breed. Its name comes from its original use established by hunters, as a decoy to attract wild mallards from the sky, into traps set for them on the ground. The call duck is the world's smallest domestic duck breed, as it weighs less than 1 kg (2.2 lb).[48] -### Heraldry +#### Heraldry Three black-colored ducks in the coat of arms of Maaninka[49] @@ -336,13 +336,13 @@ Three black-colored ducks in the coat of arms of Maaninka[49] Ducks appear on several coats of arms, including the coat of arms of Lubāna (Latvia)[50] and the coat of arms of Föglö (Åland).[51] -### Cultural references +#### Cultural references In 2002, psychologist Richard Wiseman and colleagues at the University of Hertfordshire, UK, finished a year-long LaughLab experiment, concluding that of all animals, ducks attract the most humor and silliness; he said, "If you're going to tell a joke involving an animal, make it a duck."[52] The word "duck" may have become an inherently funny word in many languages, possibly because ducks are seen as silly in their looks or behavior. Of the many ducks in fiction, many are cartoon characters, such as Walt Disney's Donald Duck, and Warner Bros.' Daffy Duck. Howard the Duck started as a comic book character in 1973[53][54] and was made into a movie in 1986. The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck as the mascot for the fictional youth hockey team who are protagonists of the movie, based on the duck being described as a fierce fighter. This led to the duck becoming the nickname and mascot for the eventual National Hockey League professional team of the Anaheim Ducks, who were founded with the name the Mighty Ducks of Anaheim.[citation needed] The duck is also the nickname of the University of Oregon sports teams as well as the Long Island Ducks minor league baseball team.[55] -## See also +### See also - Birds portal @@ -353,9 +353,9 @@ The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck a - Fictional ducks - Rubber duck -## Notes +### Notes -### Citations +#### Citations 1. ^ "Duckling". The American Heritage Dictionary of the English Language, Fourth Edition. Houghton Mifflin Company. 2006. Retrieved 2015-05-22. 2. ^ "Duckling". Kernerman English Multilingual Dictionary (Beta Version). K. Dictionaries Ltd. 2000–2006. Retrieved 2015-05-22. @@ -413,7 +413,7 @@ The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck a 54. ^ Sanderson, Peter; Gilbert, Laura (2008). "1970s". Marvel Chronicle A Year by Year History. London, United Kingdom: Dorling Kindersley. p. 161. ISBN 978-0756641238. December saw the debut of the cigar-smoking Howard the Duck. In this story by writer Steve Gerber and artist Val Mayerik, various beings from different realities had begun turning up in the Man-Thing's Florida swamp, including this bad-tempered talking duck. 55. ^ "The Duck". University of Oregon Athletics. Retrieved 2022-01-20. -### Sources +#### Sources - American Ornithologists' Union (1998). Checklist of North American Birds (PDF). Washington, DC: American Ornithologists' Union. ISBN 978-1-891276-00-2. Archived (PDF) from the original on 2022-10-09. - Carboneras, Carlos (1992). del Hoyo, Josep; Elliott, Andrew; Sargatal, Jordi (eds.). Handbook of the Birds of the World. Vol. 1: Ostrich to Ducks. Barcelona: Lynx Edicions. ISBN 978-84-87334-10-8. @@ -436,7 +436,7 @@ The 1992 Disney film The Mighty Ducks, starring Emilio Estevez, chose the duck a - Sued-Badillo, Jalil (2003). Autochthonous Societies. General History of the Caribbean. Paris: UNESCO. ISBN 978-92-3-103832-7. - Thorpe, I. J. (1996). The Origins of Agriculture in Europe. New York: Routledge. ISBN 978-0-415-08009-5. -## External links +### External links - Definitions from Wiktionary - Media from Commons diff --git a/tests/data/groundtruth/docling_v2/word_sample.docx.json b/tests/data/groundtruth/docling_v2/word_sample.docx.json index e507823e..2f5dfa32 100644 --- a/tests/data/groundtruth/docling_v2/word_sample.docx.json +++ b/tests/data/groundtruth/docling_v2/word_sample.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "word_sample", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", diff --git a/tests/data/groundtruth/docling_v2/word_tables.docx.json b/tests/data/groundtruth/docling_v2/word_tables.docx.json index 6b30c8cc..5a2642be 100644 --- a/tests/data/groundtruth/docling_v2/word_tables.docx.json +++ b/tests/data/groundtruth/docling_v2/word_tables.docx.json @@ -1,6 +1,6 @@ { "schema_name": "DoclingDocument", - "version": "1.2.0", + "version": "1.3.0", "name": "word_tables", "origin": { "mimetype": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", diff --git a/tests/data_scanned/groundtruth/docling_v2/ocr_test.json b/tests/data_scanned/groundtruth/docling_v2/ocr_test.json index 1be6459c..ac79ac25 100644 --- a/tests/data_scanned/groundtruth/docling_v2/ocr_test.json +++ b/tests/data_scanned/groundtruth/docling_v2/ocr_test.json @@ -1 +1 @@ -{"schema_name": "DoclingDocument", "version": "1.2.0", "name": "ocr_test", "origin": {"mimetype": "application/pdf", "binary_hash": 14853448746796404529, "filename": "ocr_test.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 69.0, "t": 767.2550173333334, "r": 506.6666666666667, "b": 688.5883506666667, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package", "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package"}], "pictures": [], "tables": [], "key_value_items": [], "form_items": [], "pages": {"1": {"size": {"width": 595.20119, "height": 841.921684}, "image": null, "page_no": 1}}} \ No newline at end of file +{"schema_name": "DoclingDocument", "version": "1.3.0", "name": "ocr_test", "origin": {"mimetype": "application/pdf", "binary_hash": 14853448746796404529, "filename": "ocr_test.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 69.0, "t": 767.2550173333334, "r": 506.6666666666667, "b": 688.5883506666667, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 94]}], "orig": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package", "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [], "key_value_items": [], "form_items": [], "pages": {"1": {"size": {"width": 595.20119, "height": 841.921684}, "image": null, "page_no": 1}}} \ No newline at end of file diff --git a/tests/test_backend_html.py b/tests/test_backend_html.py index caf5355e..b21486bf 100644 --- a/tests/test_backend_html.py +++ b/tests/test_backend_html.py @@ -138,7 +138,9 @@ def test_e2e_html_conversions(): doc: DoclingDocument = conv_result.document pred_md: str = doc.export_to_markdown() - assert verify_export(pred_md, str(gt_path) + ".md"), "export to md" + assert verify_export( + pred_md, str(gt_path) + ".md", generate=GENERATE + ), "export to md" pred_itxt: str = doc._export_to_indented_text( max_text_len=70, explicit_tables=False diff --git a/tests/test_backend_patent_uspto.py b/tests/test_backend_patent_uspto.py index 002aa712..aebc01d9 100644 --- a/tests/test_backend_patent_uspto.py +++ b/tests/test_backend_patent_uspto.py @@ -13,9 +13,10 @@ from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend, XmlTab from docling.datamodel.base_models import InputFormat from docling.datamodel.document import InputDocument +from .test_data_gen_flag import GEN_TEST_DATA from .verify_utils import verify_document -GENERATE: bool = False +GENERATE: bool = GEN_TEST_DATA DATA_PATH: Path = Path("./tests/data/uspto/") GT_PATH: Path = Path("./tests/data/groundtruth/docling_v2/")